# Welcome to Coresignal
Overview of available datasets and APIs. Select the right data solution for your use case and access fresh company, employee, and job posting data.
Coresignal has been a leading public web data vendor since 2016. Today, we offer 4.5B+ regularly updated company, employee, and jobs data records accessible via datasets or APIs.
Records include timestamps tied to the latest collection cycle, so you can be confident the data is suited for real-time enrichment pipelines, live AI agents, and production-grade applications that require low-latency, current data.
## Data and dashboard tools
Whether you are exploring flat-file datasets, integrating via an API, or using no-code dashboard tools, all three access methods draw from the same regularly refreshed database. Pick the path that fits your workflow, or combine them.
Learn how to bring fresh public web data into your systems using APIs – power market research, lead qualification, talent sourcing, and business development
### Integrations and updates
Keep your stack connected and your implementation up to date. The resources below include ready-made integrations and a changelog for tracking dataset updates that may affect your pipelines.
Stay up to date with dataset changes that may affect your implementation
***
## About Coresignal
Coresignal is a founding member of the [Ethical Web Data Collection Initiative](/introduction/data-and-compliance) and supports academic institutions and media organizations with data for research. In 2025, Datarade named Coresignal the Top Data Provider.
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# Data and Compliance
Learn how Coresignal collects fresh public web data ethically and in compliance with global privacy regulations.
Coresignal seeks transparency about our data privacy practices and helps our partners make informed decisions. We are committed to the ethical collection of public web data and prioritizing personal data protection and security. Data records we serve include an updated timestamp – because transparency extends to data currency, not just data privacy.
{% hint style="info" %}
#### Do you have any questions about data privacy and compliance?
Reach out to our team at ****
{% endhint %}
### What data does Coresignal collect?
{% tabs %}
{% tab title="Data we collect" %}
Coresignal **exclusively collects publicly available data from online sources and media**.\
The collected data generally includes business-related information, such as company details (firmographics, funding details, job postings, and product reviews), and limited business-related data about individual professionals who disclose their data to the general public. Collected data is refreshed on a regular cycle to ensure records reflect the latest publicly available information.\
In its data collection practices, Coresignal strictly refrains from any data collection that involves any kind of data aggregation within secured login areas.
{% endtab %}
{% tab title="Data we don't collect" %}
* Private data about individuals: any information that is not publicly available and remains private upon the decision of the individual;
* Sensitive data about individuals, even if it is publicly available. This includes information like social security numbers, home addresses, telephone numbers, geolocation data, biometric data, or photos of individuals.
* Material non-public information (MNPI) that is not publicly accessible or is located within secured login areas.
{% endtab %}
{% endtabs %}
### Certified by Ethical Web Data Collection Initiative
Coresignal is a founding member of the [Ethical Web Data Collection Initiative](https://ethicalwebdata.com/) (EWDCI), an organization that advocates for responsible web data collection and protection of personal data.
Coresignal has undergone an accreditation process to confirm that we follow the four core principles of ethical web data collection: legality, ethics, ecosystem engagement, and social responsibility.
These principles guide Coresignal through every step of data collection – from the data we gather to the partnerships we form with our clients.
{% columns %}
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1. **Legality.** We only collect publicly available data that companies and individuals disclose to the general public to facilitate their business and profession-related interests. We strive to ensure our employees are well-informed about personal data protection and the latest developments in privacy regulations. We are supported by legal counsel, who provide expert advice on a range of complex legal matters, including privacy.
2. **Ethics.** We follow a strict ethical framework, including ethical principles related to websites, customers, proxies, and data.
3. **Ecosystem.** We are in a symbiotic relationship with the free and open Internet ecosystem and strive to engage it collaboratively, openly and communicatively.
4. **Responsibility.** We pledge to support and collaborate with civil society and governmental organizations for the benefit of society.
{% endcolumn %}
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***
### Following the industry standards
We abide by the highest standards in the industry and follow the latest developments in case law regarding data collection, including privacy-related matters. The field of personal data protection is subject to potential new developments, rapid changes, and clarifications with case law, as well as decisions submitted by various supervisory authorities worldwide.
Data privacy regulations vary by jurisdiction, and it is crucial for us to be aware of the relevant laws and regulations that impact our business, as well as to put our best efforts into complying with them and following current best practices.
We have made significant efforts to establish, implement, and maintain a privacy strategy that aligns with the major current privacy regulations, including those in the US and EU.\
If you would like to find out more, please visit our [Data Privacy and Transparency](https://coresignal.com/data-transparency) page.
# Delivery Formats
Access Coresignal's fresh B2B data via flat files (JSONL, Parquet, CSV) or APIs. Supports S3, Azure, GCS delivery.
We seek to provide multiple ways to access our data, ensuring that your team can get information in the right format and at the right time.
## Delivery options
We generally offer two options: **flat file datasets** and access data via **API**. Depending on the project scope and size, you can choose the option that best suits your needs.
| Delivery option | Sources | Description |
| --------------------------------------------------------------------------------------- | ---------------------------- | ------------------------------------------------------------------------------- |
| Flat files: download the dataset using a web link | All sources | We provide you with the link and login credentials for you to retrieve the data |
| Flat files: uploaded data file to your **cloud server** (S3, Azure, Google Cloud, etc.) | All sources | Provide your storage credentials, and we will send the data to you |
| APIs: get data using available APIs | Company, Employee, Jobs data | Access data by sending API requests |
## Get a flat-file dataset
We offer **nine** different flat-file datasets for businesses. Datasets are available in **JSONL, Parquet or CSV formats:**
| Dataset | Delivery format |
| --------------------- | ------------------- |
| Base Company | JSONL; Parquet |
| Company Posts | JSONL; Parquet |
| Base Employee | JSONL; Parquet; CSV |
| Employee Posts | JSONL; Parquet |
| Base Jobs | JSONL; Parquet; CSV |
| Clean Company | JSONL; Parquet; CSV |
| Clean Employee | JSONL; Parquet; CSV |
| Multi-source Company | JSONL; Parquet |
| Multi-source Employee | JSONL; Parquet |
| Multi-source Jobs | JSONL; Parquet |
{% hint style="info" %}
We are constantly improving our delivery capabilities. If you do not find a preferred method or format, contact us.
{% endhint %}
## Access the data via API
Data access via our API provides a freshly collected dataset in JSON format that can be analyzed using Python, Ruby, PHP, or any other preferred scripting language.
## Recommended tools
{% hint style="info" %}
We can only offer general solutions since it depends on the tech stack you use or what you prefer using.
{% endhint %}
Ingesting large datasets can be efficiently managed using a combination of tools and technologies tailored to handle big data workloads.
| Tool category | Tool example |
| ----------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Database systems |
|
# Product Comparison
Not sure which product to choose? Compare the differences:
1. [Record count](#id-1.-record-count). We offer millions of records going back to 2016.
2. [File formats and size](#id-2.-file-formats-and-size). We offer data in JSONL, Parquet, and CSV formats.
3. [Processing levels](#id-3.-processing-levels). We offer Multi-source, Clean, and Base data.
4. [Delivery formats](#id-4.-delivery-formats). We provide data as datasets (flat files) or data API.
### 1. Record count
Coresignal's products have various processing levels.
| Data source | Record count | Scraping since |
| ------------- | ------------ | -------------- |
| Company Data | 70M+ | July 2016 |
| Employee Data | 895M+ | July 2016 |
| Jobs Data | 468M+ | August 2020 |
### 2. File formats and size
Coresignal provides data in **JSONL, Parquet, and CSV formats**. Each format will result in a differently sized file.
{% hint style="info" %}
**JSONL/Parquet or CSV?**
* **Data in JSONL/Parquet format is sorted by country**. In the JSONL/Parquet format delivery, data is organized by country, with each country having its own folder. Inside each folder, you'll find multiple `.json.gz` or `.gz.parquet` files. A single JSONL file can contain up to 100,000 records.
* **Data in CSV format delivery is sorted by entity type**. In the CSV format delivery, data is organized by entity type, following a schema-based structure. Each folder represents a different entity, and related information is stored separately, requiring joins to establish connections between datasets. A single CSV file can contain up to 10 million records.
{% endhint %}
***
### 3. Processing levels
Coresignal provides three types of processing levels: **Multi-source, Clean, and Base data**.
{% hint style="info" %}
**Multi-source, Clean, or Base?**
* **Multi-source datasets** contain cleaned and enriched data combining information from multiple sources.
* **Clean datasets** are derived from our Base data and cleaned to ensure the best quality.
* **Base datasets** freshly scraped and structured/updated for easier use.
{% endhint %}
***
### 4. Delivery formats
Coresignal delivers data as datasets (flat files) or data APIs. Each method has its benefits – please check the information below.
{% hint style="info" %}
**Dataset or data API?**
* **Datasets**. Good choice if you need a large dataset for a large-scale project or seek to use years of historical data. You can choose to get regular daily, weekly or monthly updates or one-time delivery.
* **Data APIs**. You can set up data API and access fresh records on demand. Good choice if you want to integrate data into your product, easily enrich data, and need more flexibility accessing records.
{% endhint %}
**Incremental or full delivery?**
Incremental delivery includes records that were updated that particular month, while full delivery will include all available records.
# FAQ
Answers common questions about Coresignal data, pricing, compliance, API access, credits, and subscriptions.
Whether you are evaluating Coresignal for the first time or looking to get more from your current plan, this page addresses the questions we hear most often, from how our data is collected and kept fresh to pricing, compliance, and technical integration.
## Data
What kind of data does Coresignal provide?
Coresignal provides three B2B data categories:
* **Company data** – comprehensive global company profiles including firmographics, workforce trends, funding rounds, financials, web traffic, and technographics.
* **Employee data** – rich professional profiles covering work experience, education, skills, locations, and salary projections.
* **Jobs data** – fresh and historical job postings with details such as job title, location, job description, required skills, salary, and hiring trends.
Where does Coresignal get its data?
Coresignal collects data from publicly available sources across the web, including professional networking platforms, company websites, job boards, and other publicly accessible business information sources. The collection focuses exclusively on business-related data: professional profiles, company details, and job postings. Coresignal does not collect private or sensitive personal data and does not access areas that require a login.
Is Coresignal GDPR compliant?
Coresignal collects only publicly available, business-related data from publicly accessible sources and does not process private or sensitive personal information. Coresignal is certified by the Ethical Web Data Collection Initiative and has built its data collection practices around these principles since 2016.
More information can be found in [privacy rights](https://coresignal.com/privacy-rights/) and [data transparency](https://coresignal.com/data-transparency/) pages.
What is the difference between datasets and APIs?
Both provide access to the same underlying data, but they serve different workflows.
**Datasets** are bulk file deliveries, typically in JSONL or Parquet, covering large volumes of records. They're suited for training machine learning models, running batch enrichment, populating a data warehouse, or any use case that requires a large static snapshot for offline work.
**APIs** provide on-demand access to individual records or result sets. They're suited for real-time enrichment, live AI agent queries, and production applications where your system needs to retrieve specific data at the moment it's needed. Coresignal's API returns results in an average of 176 ms.
How frequently are Coresignal datasets and APIs updated?
The update frequency of datasets varies by dataset and subscription terms, and is daily, weekly, or monthly. The database connected to our APIs is refreshed in real time, and API responses reflect data available at the time of the request.
How fresh is Coresignal data?
Coresignal continuously collects data from publicly available sources, and freshness varies by dataset and access method.
Through the Real-time Employee API, employee profile data is retrieved at the time of the request, providing the most current available snapshot. For dataset deliveries, Coresignal operates on regular update cycles so that records reflect recent changes rather than stale snapshots. Clients using webhooks receive updates triggered by actual profile changes, so the data they receive is fresh.
How much does Coresignal cost?
Coresignal offers multiple data solutions and flexible pricing tailored to different data needs, suitable for small businesses as well as large enterprises. For datasets, pricing is determined by contract length, dataset locations, and source tier. For APIs, a free trial is available. You can then choose a monthly plan starting at $49/month or an annual plan. More details are available on the [pricing page](https://coresignal.com/pricing/).
## APIs
Can I use natural language instead of Elasticsearch DSL?
Yes, Coresignal's [AI Data Search](/self-service/features-and-tools/ai-data-search) tool lets you query data in plain English rather than writing Elasticsearch DSL manually. You describe what you're looking for, and the tool automatically converts natural-language prompts into Elasticsearch DSL queries.
What is Agentic Search API?
[Agentic Search API](https://docs.coresignal.com/agentic-search-api/) is a natural-language search API for professional data. You describe what you want in plain English, and it translates it into a query for you.
It’s built for LLM pipelines, AI agents, and other agentic workflows. You can use it in two ways:
* Query mode – returns a generated Elasticsearch DSL query, generated for multi-source entities.
* Data mode – returns up to 100 matching records directly.
There are two endpoints:
* `/v2/agentic_search/fast` – optimized for speed and cost.
* `/v2/agentic_search/reasoning` – optimized for complex, multi-criteria searches.
Does Coresignal provide real-time data?
Yes, Coresignal provides real-time data through its Real-time Employee API. Unlike batch data pipelines, the real-time API supports dynamic platforms where timing matters, enabling live data enrichment directly within your application interface.
By sending a profile URL to the API, you receive normalized employee data reflecting the most recent job title, company affiliation, and experience, making it well-suited for use cases such as AI agents, live CRM enrichment, and real-time profile verification.
Do credits expire?
Credits expire based on your plan type.
* On monthly plans, credits are valid for one billing cycle and reset each month – unused credits do not carry over. Premium, Scale, and Elite monthly plans include a 3-month rollover, meaning unused credits remain valid for up to 3 months before expiring.
* On annual plans, you receive all credits upfront, and they're valid for 12 months from the purchase date.
* Free trial credits are valid for 7 days from the registration date.
How do yearly plans work?
Choosing an annual plan gives you a 10% discount compared with monthly billing. You pay for the full year upfront and receive all credits immediately. Your billing cycle is set to one year.
This means there is no monthly renewal to manage, and your full credit allocation is available from day one. Annual plans are available from Starter plan and are well suited for teams with predictable data needs that want to reduce per-credit costs and simplify billing.
## Self-service
How do I manage a subscription if I'm not the team owner?
A subscription can be shared within a team. One team owner manages the plan, and multiple users with email addresses from the same company domain can access shared credits. Team members can use the shared subscription but cannot manage it, add and remove other members from a closed team – those actions are reserved for the team owner.
Ownership cannot be transferred directly through the interface. If a team owner needs to be changed, you will need to create a new team or contact Coresignal directly.
What happens if I run out of credits?
If you use all your credits before the end of the billing cycle, you can go to the **Plans & credits** dashboard section to renew your current plan to keep the same terms or upgrade to a higher-tier plan for more credits. Any remaining credits from your previous plan remain valid until the end of your original billing cycle. Repurchasing keeps your workflows running without interruption and without waiting for your next billing cycle to reset.
Can I cancel my plan at any time?
Yes, you can cancel your plan anytime from the dashboard in the **Settings > Billing** section by clicking "Cancel subscription." Your plan and credits remain active until the end of the current billing cycle. If you cancel before the next billing date, you won't be charged for the following month or year.
There are no cancellation fees. Canceling simply stops the next renewal, and your access continues through the period you have already paid for.
# Data for AI
This page explains why Coresignal data is a good fit for AI pipelines and how to integrate it.
## What makes Coresignal’s data AI-ready?
AI models are only as good as the data they’re trained on. That’s why the quality, structure, and accessibility of used data matter. At Coresignal, we design our datasets with AI applications in mind, enabling efficient model training, improved performance, and reliable outcomes.
Our data is delivered in widely supported formats, pre-processed to reduce noise and inconsistency, and enriched with metadata for better traceability and control. Below is a breakdown of the key factors that make Coresignal’s datasets suitable for AI and ML pipelines.
### **Fresh and continuous updates**
AI systems learn from patterns in real-world activity. That’s why data of certain entities is continuously refreshed. With the option to integrate employee webhooks or incremental updates, your models stay aligned with the most current market movements and behavioral signals.
### **Format and structure designed for scale**
Coresignal data is available in standard formats: CSV, JSON, JSONL, and Parquet, ensuring compatibility with common data processing and AI tools. These are optimized for large-scale processing and native support across modern ML frameworks. Whether you're fine-tuning models or building AI-powered platforms, our data is structured to scale with your architecture.
Find more about [datasets delivery formats](/introduction/delivery-formats).
### **Clean and ready-to-use data**
Our main data is offered in multiple processing levels: Base, Clean, and Multi-source, to eliminate noise, clean records, normalize formats and combine several sources. This significantly reduces the risk of bias, improves training efficiency, and accelerates time-to-insight for AI teams. Additionally, you can [specify fields](/api-introduction/requests/collect) in requests’ responses to prevent your data from being cluttered with unnecessary information.
{% hint style="info" %}
**Multi-source, Clean, or Base?**
* **Multi-source datasets** contain cleaned and enriched data combining information from multiple sources.
* **Clean datasets** are derived from our Base data and cleaned to ensure the best quality.
* **Base datasets** freshly scraped and structured/updated for easier use.
{% endhint %}
### **Metadata and record-level traceability**
Our datasets include durable record identifiers and system-generated timestamps (e.g., `created_at`, `updated_at`) to support internal data management functions such as version control, deduplication, and consistency validation. These metadata fields enable:
* Filtering or segmenting records by recency.
* Observing changes to record attributes over time.
* Merging datasets without reliance on non-unique fields.
Where applicable, records also include domain-level context, like profile URLs, allowing traceability for training audits or data validation processes.
### **Deduplication and bias mitigation**
We continually refine our deduplication logic to ensure records are unique across all datasets. For machine learning workflows, this reduces redundancy, improves training efficiency, and helps mitigate overfitting. Cleaner data leads to more balanced datasets and better generalization in predictive models.
### **Ethically sourced data**
Coresignal only collects publicly available data, ensuring ethicality and adherence to the best web data collection industry practices, and audit readiness for AI models. With over 4.5 billion records across company, employee, job posting, and other datasets, we enable use cases ranging from predictive analytics to generative intelligence, backed by transparent sourcing and scalable infrastructure.
Read more about [ethical public web data collection](/introduction/data-and-compliance).
# Data Overview
Browse Coresignal datasets. Compare Multi-source, Clean, and Base tiers built on fresh, regularly updated public web data.
All datasets listed below are collected from public web sources and regularly updated, ensuring you always have access to current records when building enrichment pipelines, scoring leads, or tracking market signals.
## Company data
Regularly refreshed employee records covering career milestones, skills, and more – with timestamps on each record so you can validate data currency in your pipeline
{% hint style="info" %}
**Multi-source, Clean, or Base?**
* **Multi-source datasets** contain cleaned and enriched data combining information from multiple sources.
* **Clean datasets** are derived from our Base data and cleaned to ensure the best quality.
* **Base datasets** freshly scraped and structured/updated for easier use.
{% endhint %}
{% hint style="success" %}
#### Start a free trial
All new users can test data quality with free **API credits**. After the trial, select an API subscription plan based on your business needs
Start now
{% endhint %}
## Discover next
{% hint style="warning" %}
**Work in progress**
This is a preview version of our complete documentation. The full version is available to our clients and includes comprehensive data statistics, onboarding information, release notes, FAQs, and more.
{% endhint %}
***
## Data categories and other sources
Right now, you can also access information about 7 data categories:
* Employee data
* Firmographic data
* Job posting data
* Funding data
* Employee review data
* Product review data
* Community and repository data
| Source | Data dictionary and data sample |
| ------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Docker Hub |
|
{% hint style="success" %}
#### **Start a free trial**
Explore our data for free with 2,000 free trial credits.
Start now
{% endhint %}
# APIs Overview
Overview of all Coresignal APIs for fresh company, employee, and jobs data – including a Real-time Employee API for real-time data access.
## Start with APIs
{% hint style="info" %}
**Multi-source, Clean, or Base?**
* **Multi-source datasets** contain cleaned and enriched data combining information from multiple sources.
* **Clean datasets** are derived from our Base data and cleaned to ensure the best quality.
* **Base datasets** freshly scraped and structured/updated for easier use.
{% endhint %}
## Company APIs
### Tools used in examples
We use Postman (with cURL request examples) in the documentation to demonstrate how to work with our APIs. The tool is provided for illustrative purposes only, feel free to use any HTTP client or API-compatible tool of your choice.
# Authorization
## Authorization key
To start using our API, you need an API Key. You can get the API Key from Coresignal's [self-service platform](https://dashboard.coresignal.com/sign-in). Moreover, your account manager or sales representative can generate the key for you upon your request for the API plan.
***
## Authorization header
All requests must contain an `API Key` header. Its value is your unique API Key.
{% code title="Authorization header" %}
```json
-H “apikey: "API_Key"”
```
{% endcode %}
## cURL authorization
Several examples of authorization in cURL are given according to the required request. Here, requests are made using Base Company API endpoints.
{% code title="Elasticsearch DSL" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/company_base/search/es_dsl' \
-H 'accept: application/json' \
-H 'apikey: "API_Key"' \
-H 'Content-Type: application/json' \
-d '{
//insert your query
}'
```
{% endcode %}
## General authorization templates
Templates for authentication in various programming languages and programming environment.
{% tabs %}
{% tab title="Python" %}
```python
import requests
api_endpoint = "API_ENDPOINT"
api_key = "API_Key"
headers = {
"apikey": api_key
}
response = requests.get(api_endpoint, headers=headers)
# Print the response content or handle it as needed
print(response.text)
```
{% endtab %}
{% tab title="Ruby" %}
```ruby
require 'net/http'
require 'uri'
uri = URI.parse("API_ENDPOINT")
http = Net::HTTP.new(uri.host, uri.port)
request = Net::HTTP::Get.new(uri.request_uri)
api_key = "API_Key"
request['apikey'] = api_key
response = http.request(request)
puts response.body
```
{% endtab %}
{% tab title="Node.js" %}
```nodejs
const https = require('https');
const apiEndpoint = 'API_ENDPOINT';
const apiKey = "API_Key";
const options = {
headers: {
'apikey': apiKey
}
};
https.get(apiEndpoint, options, (response) => {
let data = '';
response.on('data', (chunk) => {
data += chunk;
});
response.on('end', () => {
console.log(data);
});
});
```
{% endtab %}
{% tab title="PHP" %}
```php
[
'header' => "apikey: $apiKey"
]
];
$context = stream_context_create($options);
$response = file_get_contents($apiEndpoint, false, $context);
?>
```
{% endtab %}
{% endtabs %}
Use any API-compatible tool to authorize and start querying the API.
## FAQ
Where can I find my API Key?
API Key is stored in the [self-service platform](https://dashboard.coresignal.com/sign-in) home page's `API Keys` section.
How do I use API Key?
1. Change your authorization request header's to API Key: `-H 'apikey: "API_Key"'`\
Here, insert your API Key instead of `"API_Key"`
2. Make sure that you are using the correct endpoints. For example: `/v2/company_base/search/es_dsl`
# Credits
Understand how Coresignal's credit system works. Credits are deducted per successful request when accessing fresh data via Search and Collect APIs.
## Overview
Your unique API Key has a set number of credits. Credits are deducted for each successful (200) request when using the collect or enrich endpoints. Each successful request deducts a set number of credits, which you can find on the Pricing page under the [Credit usage per endpoint](/pricing#credit-usage-per-endpoint) topic.
[Search Preview costs](/pricing#search-preview) apply per query returning up to 20 results and depend on the processing level used to retrieve the data.
## Credits in Bulk Collect requests
For Bulk Collect requests, the number of credits deducted from your account depends on the data records count and the used endpoint. For instance, collecting 100 job postings will consume 100 credits.
Read more about Bulk Collect requests in [Bulk Collect requests](/api-introduction/requests/bulk-collect) topic.
Discuss your credit needs with your account manager or explore available plans in the [self-service platform](https://dashboard.coresignal.com/sign-in).
## FAQ
Where can I see my credit balance?
Responses contain an `x-credits-remaining` header that shows the number of credits left in your account.
**Example of remaining credits header:**
```json
x-credits-remaining: 9973
```
What happens if I don't have enough credits?
If you don't have enough credits, the request will return error `402` and the following response:
{% code title="Insufficient credits" %}
```json
{
"detail": "Insufficient credits"
}
```
{% endcode %}
You can repurchase more credits to continue.
What happens if I submit duplicate requests?
We validate your search and Elasticsearch POST requests in Bulk Collect to prevent duplicate submissions. If you submit duplicate POST requests using search or Elasticsearch filters, you will see the following error message:
{% code title="409" %}
```json
{
"detail": "Identical data request is already in progress."
}
```
{% endcode %}
# Rate Limits
API rate limit reference for all Coresignal endpoints. Optimize fresh data streaming without hitting Search or Collect thresholds.
API rate limits specify how many requests a user or application can make to an API within a specific time period. These limits help ensure fair usage and maintain performance across the platform. Rate limits can vary depending on the plan or product used. Check the rate limit details at the product level to optimize your usage and avoid interruptions.
## Data APIs
The following rate limits apply to search, collect, bulk collect, search preview, webhook requests, and Agentic Search API `/v2/agentic_search/fast` endpoint, and depend on the plan you use. Check all plan features on the [Pricing](/pricing) page.
| Rate limits \ Plans | Free trial | Mini | Starter | Pro | Growth | Premium | Scale | Elite |
| ---------------------- | ---------- | ------- | ------- | -------- | -------- | -------- | --------- | ---------- |
| Rate limits (Data API) | 5 req/s | 5 req/s | 5 req/s | 10 req/s | 20 req/s | 50 req/s | 100 req/s | 100+ req/s |
## Agentic Search API
[Agentic Search API](/agentic-search-api) rate limits depend on the endpoint used.
| Endpoint | Requests per time period |
| ------------------------------ | ------------------------ |
| `/v2/agentic_search/reasoning` | 10 requests per hour |
# Response Codes
Complete reference for all Coresignal API response codes – covering Search, Collect, Bulk Collect, Webhooks, and the Real-time Employee API.
## Overview
Explore the various possible response codes and their meanings.
These error codes can be encountered in all:
Codes received with Real-time Employee API endpoint
## Search and collect response codes
| Response code | Description |
| ------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `200` | A successful request. Credits are deducted. |
| `400` | Request was unacceptable, often due to a missing required parameter. |
| `401` | No valid API Key was provided. Check if your key is valid and try again. |
| `402` | Insufficient credits. Contact your account manager to get more credits. |
| `404` |
Several cases can cause the error:
Nonexistent API URL
Nonexistent ID: Using a profile ID that does not exist in our database
|
| `422` | Incorrect data structure or data types are provided in the request. |
| `429` |
Endpoint rate limit has been exceeded. All API endpoints restrict the number of requests allowed per second for each client's API Key.
|
| `500` | Code `500` denotes server issues. One possible example is that the wrong request type was selected (POST/GET). |
| `502` | Bad gateway. |
| `503` |
503 can be returned in the following cases:
Elasticsearch is overloaded and temporarily unable to accept additional requests
The request is too complex to complete within a specified time
Other temporary disruptions
|
## Bulk Collect response codes
| Response codes | Description |
| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `201` | A successful request. |
| `202` | The process is in progress. |
| `400` | Indicates incorrectly formatted or oversized files or invalid data. |
| `402` | Insufficient credits. Reduce your request size or contact your account manager to get more credits. |
| `404` | Request did not find any matching IDs. |
| `409` | Duplicate POST requests were submitted. |
| `422` |
Request exceeded the limit of 10k IDs
The provided URLs cannot be parsed into shorthand names
Input does not meet the shorthand\_names requirements
Request input includes duplicates
|
| `503` |
Collect allows you to download the prepared dataset as often as you like within 30 days of submitting the query. After 30 days of query submission, the GET query will return a 404 response code.
Error codes indicate failures due to incorrectly formatted or oversized files or invalid data.
The subscription has already expired.
Renewing a subscription would push the expiry date over 1 year away.
|
| `401` | No valid API Key was provided. Check if your key is valid and try again. |
| `404` | Subscription doesn’t exist or doesn’t belong to the user |
| `409` | Subscription was just created and hasn’t finished initialising |
| `422` | The webhook URL you provided is invalid. |
| `5xx` | Unable to create subscription due to a server error. |
### Search Filter and Elasticsearch DSL endpoint response codes
| Response code | Description |
| ------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `200` | A successful request. Credits are deducted. |
| `400` | The request was unacceptable, often due to a missing required parameter. |
| `401` | No valid API Key was provided. Check if your key is valid and try again. |
| `422` |
There are two possible reasons for the 422 error code:
The webhook URL you provided is invalid
You have used incompatible filters (such as any date filter from search filters or Elasticsearch DSL schema).
|
## Real-time Employee API response codes
| Response code | Description |
| ------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `200` | A successful request. Credits are deducted. |
| `400` | The request was unacceptable, often due to a missing required parameter. |
| `402` | Insufficient credits. |
| `408` | Response timeout. Credits are not deducted for this request. You can try sending the request again. |
| `422` |
Incorrect data structure or data types were provided in the request. Unprocessable content:
Invalid URL
Invalid cache age
|
| `454` | Profile was missing, removed, or made private during the most recent scrape. Credits are not deducted. |
| `503` | Something went wrong on the API side. You will get a Bad gateway error message if you have selected the wrong request type. |
# Requests
Use the requests below to query and retrieve data through the API. Each request type is documented with parameters, usage details, and examples.
# Elasticsearch DSL
Write powerful queries against Coresignal's fresh data using Elasticsearch DSL. Includes schema links, input values, sorting options, and query tips.
## Introduction to Elasticsearch DSL
We provide ready-to-use Elasticsearch DSL (Domain Specific Language) schemas that simplify how clients interact with the provided data. Instead of building complex queries from scratch, you can leverage our curated DSL structures to search, filter, and analyze your data with speed and precision.
JSON-based schemas are optimized for common use cases and data models, enabling you to implement powerful full-text search, filtering, and aggregations without requiring in-depth knowledge of Elasticsearch internals. Nevertheless, Elasticsearch schema allows you to use more sophisticated queries like [fuzzy ](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-fuzzy-query.html)and [wildcard](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-wildcard-query.html) queries.
For all specific Elasticsearch DSL information, please refer to their official [documentation](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl.html).
When sending Elasticsearch DSL search requests, the result set includes only record IDs. The endpoint does not return complete record data, regardless of the query's complexity. To obtain full record details, a [collect](/api-introduction/requests/collect) request must be sent.
{% hint style="success" %}
#### Having trouble writing Elasticsearch queries on your own?
Explore **AI query builder** feature available in [Self-service](https://dashboard.coresignal.com/home) playground. Write a prompt, and AI assistant will automatically convert it into a query.
{% endhint %}
### Discover products
Find all APIs Elasticsearch DSL schemas, request samples and additional information.
Appliances, Electrical, and Electronics Manufacturing
Architectural and Structural Metal Manufacturing
Architecture and Planning
Armed Forces
Artificial Rubber and Synthetic Fiber Manufacturing
Artists and Writers
Arts & Crafts
Audio and Video Equipment Manufacturing
Automation Machinery Manufacturing
Automotive
Aviation & Aerospace
Aviation and Aerospace Component Manufacturing
Baked Goods Manufacturing
Banking
Bars, Taverns, and Nightclubs
Bed-and-Breakfasts, Hostels, Homestays
Beverage Manufacturing
Biomass Electric Power Generation
Biotechnology
Biotechnology Research
Blockchain Services
Blogs
Boilers, Tanks, and Shipping Container Manufacturing
Book and Periodical Publishing
Book Publishing
Breweries
Broadcast Media Production and Distribution
Building Construction
Building Equipment Contractors
Building Finishing Contractors
Building Materials
Building Structure and Exterior Contractors
Business Consulting and Services
Business Content
Business Intelligence Platforms
Business Supplies & Equipment
Cable and Satellite Programming
Capital Markets
Caterers
Chemical Manufacturing
Chemical Raw Materials Manufacturing
Child Day Care Services
Chiropractors
Circuses and Magic Shows
Civic and Social Organizations
Civil Engineering
Claims Adjusting, Actuarial Services
Clay and Refractory Products Manufacturing
Climate Data and Analytics
Climate Technology Product Manufacturing
Coal Mining
Collection Agencies
Commercial and Industrial Equipment Rental
Commercial and Industrial Machinery Maintenance
Commercial and Service Industry Machinery Manufacturing
Commercial Real Estate
Communications Equipment Manufacturing
Community Development and Urban Planning
Community Services
Computer and Network Security
Computer Games
Computer Hardware
Computer Hardware Manufacturing
Computer Networking
Computer Networking Products
Computers and Electronics Manufacturing
Conservation Programs
Construction
Construction Hardware Manufacturing
Consumer Electronics
Consumer Goods
Consumer Goods Rental
Consumer Services
Correctional Institutions
Cosmetics
Cosmetology and Barber Schools
Courts of Law
Credit Intermediation
Cutlery and Handtool Manufacturing
Dairy
Dairy Product Manufacturing
Dance Companies
Data Infrastructure and Analytics
Data Security Software Products
Death Care Services
Defense & Space
Defense and Space Manufacturing
Dentists
Design
Design Services
Desktop Computing Software Products
Digital Accessibility Services
Distilleries
E-learning
E-Learning Providers
Economic Programs
Education
Education Administration Programs
Education Management
Electric Lighting Equipment Manufacturing
Electric Power Generation
Electric Power Transmission, Control, and Distribution
Electrical Equipment Manufacturing
Electronic and Precision Equipment Maintenance
Embedded Software Products
Emergency and Relief Services
Energy Technology
Engineering Services
Engines and Power Transmission Equipment Manufacturing
Entertainment
Entertainment Providers
Environmental Quality Programs
Environmental Services
Equipment Rental Services
Events Services
Executive Offices
Executive Search Services
Fabricated Metal Products
Facilities Services
Family Planning Centers
Farming
Farming, Ranching, Forestry
Fashion Accessories Manufacturing
Financial Services
Fine Art
Fine Arts Schools
Fire Protection
Fisheries
Flight Training
Food & Beverages
Food and Beverage Manufacturing
Food and Beverage Retail
Food and Beverage Services
Food Production
Footwear and Leather Goods Repair
Footwear Manufacturing
Forestry and Logging
Fossil Fuel Electric Power Generation
Freight and Package Transportation
Fruit and Vegetable Preserves Manufacturing
Fuel Cell Manufacturing
Fundraising
Funds and Trusts
Funeral Services
Furniture
Furniture and Home Furnishings Manufacturing
Gambling Facilities and Casinos
Geothermal Electric Power Generation
Glass Product Manufacturing
Glass, Ceramics and Concrete Manufacturing
Golf Courses and Country Clubs
Government Administration
Government Relations
Government Relations Services
Graphic Design
Ground Passenger Transportation
Health and Human Services
Health, Wellness & Fitness
Higher Education
Highway, Street, and Bridge Construction
Historical Sites
Holding Companies
Home Health Care Services
Horticulture
Hospitality
Hospitals
Hospitals and Health Care
Hotels and Motels
Household and Institutional Furniture Manufacturing
Household Appliance Manufacturing
Household Services
Housing and Community Development
Housing Programs
Human Resources
Human Resources Services
HVAC and Refrigeration Equipment Manufacturing
Hydroelectric Power Generation
Import & Export
Individual and Family Services
Industrial Automation
Industrial Machinery Manufacturing
Industry Associations
Information Services
Information Technology & Services
Insurance
Insurance Agencies and Brokerages
Insurance and Employee Benefit Funds
Insurance Carriers
Interior Design
International Affairs
International Trade and Development
Internet Marketplace Platforms
Internet News
Internet Publishing
Interurban and Rural Bus Services
Investment Advice
Investment Banking
Investment Management
IT Services and IT Consulting
IT System Custom Software Development
IT System Data Services
IT System Design Services
IT System Installation and Disposal
IT System Operations and Maintenance
IT System Testing and Evaluation
IT System Training and Support
Janitorial Services
Landscaping Services
Language Schools
Laundry and Drycleaning Services
Law Enforcement
Law Practice
Leasing Non-residential Real Estate
Leasing Residential Real Estate
Leather Product Manufacturing
Legal Services
Legislative Offices
Leisure, Travel & Tourism
Libraries
Lime and Gypsum Products Manufacturing
Loan Brokers
Luxury Goods & Jewelry
Machinery Manufacturing
Magnetic and Optical Media Manufacturing
Manufacturing
Maritime
Maritime Transportation
Market Research
Marketing Services
Mattress and Blinds Manufacturing
Measuring and Control Instrument Manufacturing
Meat Products Manufacturing
Mechanical Or Industrial Engineering
Media and Telecommunications
Media Production
Medical and Diagnostic Laboratories
Medical Device
Medical Equipment Manufacturing
Medical Practices
Mental Health Care
Metal Ore Mining
Metal Treatments
Metal Valve, Ball, and Roller Manufacturing
Metalworking Machinery Manufacturing
Military and International Affairs
Mining
Mobile Computing Software Products
Mobile Food Services
Mobile Gaming Apps
Motor Vehicle Manufacturing
Motor Vehicle Parts Manufacturing
Movies and Sound Recording
Movies, Videos, and Sound
Museums
Museums, Historical Sites, and Zoos
Music
Musicians
Nanotechnology Research
Natural Gas Distribution
Natural Gas Extraction
Newspaper Publishing
Non-profit Organization Management
Non-profit Organizations
Nonmetallic Mineral Mining
Nonresidential Building Construction
Nuclear Electric Power Generation
Nursing Homes and Residential Care Facilities
Office Administration
Office Furniture and Fixtures Manufacturing
Oil and Coal Product Manufacturing
Oil and Gas
Oil Extraction
Oil, Gas, and Mining
Online and Mail Order Retail
Online Audio and Video Media
Online Media
Operations Consulting
Optometrists
Outpatient Care Centers
Outsourcing and Offshoring Consulting
Outsourcing/Offshoring
Packaging & Containers
Packaging and Containers Manufacturing
Paint, Coating, and Adhesive Manufacturing
Paper & Forest Products
Paper and Forest Product Manufacturing
Parts Distribution
Pension Funds
Performing Arts
Performing Arts and Spectator Sports
Periodical Publishing
Personal and Laundry Services
Personal Care Product Manufacturing
Personal Care Services
Pet Services
Pharmaceutical Manufacturing
Philanthropic Fundraising Services
Philanthropy
Photography
Physical, Occupational and Speech Therapists
Physicians
Pipeline Transportation
Plastics and Rubber Product Manufacturing
Plastics Manufacturing
Political Organizations
Postal Services
Primary and Secondary Education
Primary Metal Manufacturing
Printing Services
Professional Organizations
Professional Services
Professional Training and Coaching
Program Development
Public Assistance Programs
Public Health
Public Policy
Public Policy Offices
Public Relations and Communications Services
Public Safety
Public Works
Racetracks
Radio and Television Broadcasting
Rail Transportation
Railroad Equipment Manufacturing
Ranching
Ranching and Fisheries
Real Estate
Real Estate Agents and Brokers
Real Estate and Equipment Rental Services
Recreational Facilities
Regenerative Design
Religious Institutions
Renewable Energy Equipment Manufacturing
Renewable Energy Power Generation
Renewable Energy Semiconductor Manufacturing
Renewables & Environment
Repair and Maintenance
Research
Research Services
Residential Building Construction
Restaurants
Retail
Retail Apparel and Fashion
Retail Appliances, Electrical, and Electronic Equipment
Retail Art Dealers
Retail Art Supplies
Retail Books and Printed News
Retail Building Materials and Garden Equipment
Retail Florists
Retail Furniture and Home Furnishings
Retail Gasoline
Retail Groceries
Retail Health and Personal Care Products
Retail Luxury Goods and Jewelry
Retail Motor Vehicles
Retail Musical Instruments
Retail Office Equipment
Retail Office Supplies and Gifts
Retail Pharmacies
Retail Recyclable Materials & Used Merchandise
Reupholstery and Furniture Repair
Robot Manufacturing
Robotics Engineering
Rubber Products Manufacturing
Satellite Telecommunications
Savings Institutions
School and Employee Bus Services
Seafood Product Manufacturing
Secretarial Schools
Securities and Commodity Exchanges
Security and Investigations
Security Guards and Patrol Services
Security Systems Services
Semiconductor Manufacturing
Semiconductors
Services for Renewable Energy
Services for the Elderly and Disabled
Sheet Music Publishing
Shipbuilding
Shuttles and Special Needs Transportation Services
Sightseeing Transportation
Skiing Facilities
Smart Meter Manufacturing
Soap and Cleaning Product Manufacturing
Social Networking Platforms
Software Development
Solar Electric Power Generation
Sound Recording
Space Research and Technology
Specialty Trade Contractors
Spectator Sports
Sporting Goods
Sporting Goods Manufacturing
Sports and Recreation Instruction
Sports Teams and Clubs
Spring and Wire Product Manufacturing
Staffing and Recruiting
Steam and Air-Conditioning Supply
Strategic Management Services
Subdivision of Land
Sugar and Confectionery Product Manufacturing
Surveying and Mapping Services
Taxi and Limousine Services
Technical and Vocational Training
Technology, Information and Internet
Technology, Information and Media
Telecommunications
Telecommunications Carriers
Telephone Call Centers
Temporary Help Services
Textile Manufacturing
Theater Companies
Think Tanks
Tobacco
Tobacco Manufacturing
Translation and Localization
Transportation Equipment Manufacturing
Transportation Programs
Transportation, Logistics, Supply Chain and Storage
Transportation/Trucking/Railroad
Travel Arrangements
Truck Transportation
Trusts and Estates
Turned Products and Fastener Manufacturing
Urban Transit Services
Utilities
Utilities Administration
Utility System Construction
Vehicle Repair and Maintenance
Venture Capital and Private Equity Principals
Veterinary
Veterinary Services
Vocational Rehabilitation Services
Warehousing
Warehousing and Storage
Waste Collection
Waste Treatment and Disposal
Water Supply and Irrigation Systems
Water, Waste, Steam, and Air Conditioning Services
Wellness and Fitness Services
Wholesale
Wholesale Alcoholic Beverages
Wholesale Apparel and Sewing Supplies
Wholesale Appliances, Electrical, and Electronics
Wholesale Building Materials
Wholesale Chemical and Allied Products
Wholesale Computer Equipment
Wholesale Drugs and Sundries
Wholesale Food and Beverage
Wholesale Footwear
Wholesale Furniture and Home Furnishings
Wholesale Hardware, Plumbing, Heating Equipment
Wholesale Import and Export
Wholesale Luxury Goods and Jewelry
Wholesale Machinery
Wholesale Metals and Minerals
Wholesale Motor Vehicles and Parts
Wholesale Paper Products
Wholesale Petroleum and Petroleum Products
Wholesale Photography Equipment and Supplies
Wholesale Raw Farm Products
Wholesale Recyclable Materials
Wind Electric Power Generation
Wine & Spirits
Wineries
Wireless Services
Women's Handbag Manufacturing
Wood Product Manufacturing
Writing and Editing
Zoos and Botanical Gardens
hq_location, location_hq_regions and location_regions input values
Americas
AMER
EMEA
Asia
Northern America
APAC
Europe
EU
Latin America and the Caribbean
Southern Asia
South America
Western Europe
Northern Europe
Africa
Southern Europe
South-Eastern Asia
Eastern Asia
Western Asia
Sub-Saharan Africa
Eastern Europe
Central America
Oceania
Australia and New Zealand
Northern Africa
Western Africa
Southern Africa
Eastern Africa
Caribbean
Middle Africa
Central Asia
Melanesia
Polynesia
Micronesia
Channel Islands
last_round_type input values
Angel
Convertible note
Corporate round
Debt financing
Equity crowdfunding
Grant
Initial coin offering
Non equity assistance
Post IPO debt
Post IPO equity
Post IPO secondary
Pre seed
Private equity
Product crowdfunding
Secondary market
Seed
Serie B
Series A
Series B
Series C
Series D
Series E
Series F
Series G
Series H
Series I
Series J
Series unknown
Undisclosed
management_level input values
C-Level
Director
Founder
Head
Intern
Manager
Owner
Partner
President/Vice President
Senior
Specialist
department input values
Administrative
Consulting
Customer Service
Design
Education
Engineering and Technical
Finance & Accounting
General Management
Human Resources
Legal
Marketing
Medical
Operations
Product
Project Management
Real Estate
Research
Sales
Trades
Other
type input values
Self-Employed
Privately Held
Self-Owned
Nonprofit
Public Company
Partnership
Government Agency
Educational
## Sorting options
There are several sorting options to help you read your request results. All queries can be sorted by two options: sort by `id` or `_score`. If you omit the sorting part entirely, results will be listed by `last_updated` in descending order.
**Sorting by** `_score` enables you to view search results in order of relevance, with the most relevant items appearing first.
```json
{
"query": {}, //Insert your query
"sort": [
"_score"
]
}
```
**Sorting by** `ID` arranges the list of IDs in ascending order, starting from the smallest value and progressing to the largest.
```json
{
"query": {}, //Insert your query
"sort": [
"id"
]
}
```
Several entities offer additional sorting options, which you can find in Elasticsearch DSL related topics.
## Limitations
* We do not own the functions and syntax that this endpoint operates in. For all specific Elasticsearch DSL information, please refer to their [official documentation.](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl.html)
* The endpoint accepts the query object of a standard Elasticsearch request.
* No matter the content of the query, the search only returns record IDs (like all other Search endpoints).
* Queries in this endpoint are limited to 15,000 characters.
* The maximum number of clauses a BooleanQuery can contain (number of boolean operators within the query) is 1024.
## More information on Elasticsearch DSL
Do your Elasticsearch DSL endpoints support analytics features?
No, the analytics features are not supported in our Elasticsearch DSL endpoints.
Is it possible to search for a boolean value?
Yes. The elastic search query itself includes a parameter `bool` that indicates to return results if they meet the criteria.
Is there a way to exclude results?
Use an elastic search boolean query with a `must_not` clause.
# Results Pagination
### Why is pagination needed?
Results containing IDs are provided in batches of 1,000 IDs per page.
Sometimes, a request might yield more IDs than can be displayed on a single page. In such situations, you'll need to use pagination to obtain all the requested IDs from the POST request.
### Response header information
Response headers contain information such as **the total result count**, **page number**, and **the last ID** on the page:
* `x-next-page-after` – displays the last ID on the page. This field contains `last_updated` and `ID` information. Here `last_updated` format differs between entities.
* `x-total-pages` – lists the total number of pages with ID results.
* `x-total-results` – shows the total number of IDs returned by your search.
An example of Multi-source Company API response headers:
```json
x-next-page-after: "2025-03-03",3771705
x-total-pages: 57
x-total-results: 56940
```
### Sorting in pagination
Pagination using **ID** sorting works the same way as pagination without sorting.
Pagination using **score** sorting has a different ID format. The format difference is seen by the `x-next-page-after` parameter, where results show: the **score**, the **last updated** date, and the **last ID** on the page.
An example of Multi-source Company API response headers:
{% code title="Sorting by score" %}
```json
x-next-page-after: 26.806067,"2025-02-25",6428995
x-total-pages: 57
x-total-results: 56940
```
{% endcode %}
### Using pagination in cURL requests
Add a parameter `?after={x-next-page-after}` to the POST request to see the next results page. Example of Multi-source Company request:
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/company_multi_source/search/es_dsl?after="2025-03-03",3771705' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"query": {}, //Insert your query
}'
```
### Limiting search results per page
Query parameter `?items_per_page={int}` allows you to specify the number of results retrieved per Search results page. The current limit is 1,000. Thus, this parameter lets you set a smaller limit value for the results page.
# Semantic Search
Use Elasticsearch DSL semantic search endpoints to expand job title queries with synonym matches, improving recall without changing the request format.
Semantic search expands [Elasticsearch DSL](/api-introduction/requests/elasticsearch-dsl) queries to include job titles with equivalent meaning, even when the wording differs. A search for "Software Engineer" can automatically surface results for "Software Developer" or "Senior Software Engineer" without modifying the query format.
It is activated via dedicated `/semantic_search/es_dsl` endpoints – drop-in replacements for the standard `/search/es_dsl` endpoints. The minimum confidence score for synonym inclusion is controlled by the `threshold` parameter.
{% hint style="info" %}
Semantic search endpoints have higher latency than their standard counterparts, as each request triggers an additional model call before query execution.
{% endhint %}
Same request format
No changes to Elasticsearch DSL query structure. Drop-in replacement for existing /search/es_dsl endpoints.
Broader recall
Semantically equivalent titles are matched automatically, reducing gaps caused by wording variations across profiles and postings.
Full transparency
Every injected synonym is listed in the response metadata with its confidence score and boost status.
## Endpoints
Each semantic search endpoint request matches standard multi-source `/search/es_dsl` endpoints, with synonym search performed before executing the query.
| Semantic search endpoint | Regular search endpoint |
| ------------------------------------------------------- | ---------------------------------------------- |
| POST `/v2/company_multi_source/semantic_search/es_dsl` | POST `/v2/company_multi_source/search/es_dsl` |
| POST `/v2/employee_multi_source/semantic_search/es_dsl` | POST `/v2/employee_multi_source/search/es_dsl` |
| POST `/v2/job_multi_source/semantic_search/es_dsl` | POST `/v2/job_multi_source/search/es_dsl` |
## Supported fields for semantic search
Synonym injection is triggered when a `match_phrase` clause targets one of the data fields below. Other fields pass through unchanged.
| Endpoint | Data field |
| ------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------- |
| POST `/v2/company_multi_source/semantic_search/es_dsl` | `active_job_postings[].job_posting_title` |
| POST `/v2/employee_multi_source/semantic_search/es_dsl` |
|
| POST `/v2/job_multi_source/semantic_search/es_dsl` | `title` |
## Request parameters
Control semantic search with additional request parameters to get more relevant results.
| Parameter | Type | Default value | Description |
| ----------- | ----- | ------------- | ----------------------------------------------------------------- |
| `threshold` | Float | `0.9` | Minimum confidence score required for a synonym to be injected |
| `boost` | Float | `0.1` | Controls the weight of seniority signals in the relevance scoring |
## How it works
{% stepper %}
{% step %}
### Send request
Send an Elasticsearch DSL query with semantic search endpoint. Use an identical format to existing `/search/es_dsl` endpoints. No changes to request structure are required.
{% endstep %}
{% step %}
### Scanning data fields
The query is scanned for `match_phrase` clauses targeting supported data fields.
{% endstep %}
{% step %}
### Looking for a match
If a match is found, the semantic search model is called. Synonyms at or above the configured `threshold` are injected as an expanded `should` clause in the query.
{% endstep %}
{% step %}
### Executing the query
The enriched query is forwarded to Elasticsearch DSL and executed.
{% endstep %}
{% step %}
### Getting a response
The response includes a `metadata` block documenting what was injected and standard Elasticsearch DSL results (IDs) in `results` field. If no eligible `match_phrase` is detected, the query is forwarded as-is and `metadata[].synonyms_injected` is returned as an empty array.
{% endstep %}
{% endstepper %}
## Response
Response is similar to existing `/search/es_dsl` endpoints with an additional `metadata` block that includes information about applied semantic search and IDs presented in `results` field.
Field
Description
Data type
metadata
Information about applied semantic search
Array of struct
metadata[].threshold
Value set in the request
Float
metadata[].synonyms_injected
List of added synonyms to the query. If no synonyms were injected – returned as an empty array
Array of struct
metadata[].synonyms_injected[].field
Data field where original and synonym values were applied
String
metadata[].synonyms_injected[].original
Original field value
String
metadata[].synonyms_injected[].synonym
Added synonym value based on the original value and threshold
String
metadata[].synonyms_injected[].score
Confidence score
Float
metadata[].synonyms_injected[].boosted
Identifies whether the boost was applied
Boolean
results
Returned matching IDs
Array of integers
{% code title="Response example" overflow="wrap" %}
```json
{
"metadata": {
"threshold": 0.9,
"synonyms_injected": [
{
"field": "active_experience_title",
"original": "Software Engineer",
"synonym": "Senior Software Engineer",
"score": 0.97,
"boosted": true
},
{
"field": "active_experience_title",
"original": "Software Engineer",
"synonym": "Software Developer",
"score": 0.96,
"boosted": false
}
]
},
"results": [
10000,
10001,
10002
]
}
```
{% endcode %}
# Search Filters
## Introduction to Search Filters
Welcome to **Search Filters** – a streamlined tool designed to help you query datasets effortlessly. If you're looking to retrieve profile IDs, this feature provides an intuitive and efficient alternative to more complex Elasticsearch DSL queries. This endpoint eliminates the learning curve, making data exploration accessible to all users, including those without a deep technical background.
### Discover products
Find APIs with search filters endpoints, filter lists, their values, and samples.
### Why Search Filters?
* **Explore the available filters:** Each entity offers different varieties of filters. Get to know each filter’s function and how it can be applied in diverse contexts.
* **Run simple queries:** Whether you're filtering by department, role, or ID range, the interface keeps things straightforward. No need to learn complex query syntax. Just apply relevant filters and retrieve the data you need.
### Helpful tips
* Combine multiple filters to refine your search results.
* Use partial values to broaden your matches (e.g., search for "Eng\*" to include all Engineering-related entries).
* Save common filter sets for repeated use cases.
## Possible input values
Some search filter fields have a predefined list of possible input values. Find these lists below.
size or experience_company_size input values
"Myself Only"
"1 employee"
"1-10 employees"
"2-10 employees"
"11-50 employees"
"51-200 employees"
"201-500 employees"
"501-1000 employees"
"1001-5000 employees"
"5001-10,000 employees"
"10,001+ employees"
industry or experience_company_industry input values
"Appliances, Electrical, and Electronics Manufacturing"
"Furniture and Home Furnishings Manufacturing"
"Hospitals and Health Care"
"Entertainment Providers"
"Consumer Services"
"Food Production"
"Human Resources Services"
"Staffing and Recruiting"
"Machinery Manufacturing"
"Wellness and Fitness Services"
"Legal Services"
"Civil Engineering"
"Religious Institutions"
"Transportation, Logistics, Supply Chain and Storage"
"Public Relations and Communications Services"
"Sports"
"Events Services"
"Artists and Writers"
"Venture Capital and Private Equity Principals"
"Medical Equipment Manufacturing"
"Automotive"
"Consumer Electronics"
"Architecture and Planning"
"Insurance"
"Health, Wellness & Fitness"
"Market Research"
"Writing and Editing"
"Media Production"
"Musicians"
"Personal Care Product Manufacturing"
"Mental Health Care"
"International Trade & Development"
"Printing Services"
"Biotechnology Research"
"Motor Vehicle Manufacturing"
"Computer Hardware"
"Industrial Machinery Manufacturing"
"Biotechnology"
"Spectator Sports"
"Retail Luxury Goods and Jewelry"
"Movies, Videos, and Sound"
"Wine & Spirits"
"Philanthropic Fundraising Services"
"Medical Device"
"Plastics Manufacturing"
"Entertainment"
"Newspaper Publishing"
"Education"
"Travel Arrangements"
"Law Enforcement"
"Commercial Real Estate"
"Renewable Energy Semiconductor Manufacturing"
"Arts & Crafts"
"International Affairs"
"Banking"
"Industrial Automation"
"Dairy Product Manufacturing"
"Human Resources"
"Retail Office Equipment"
"Truck Transportation"
"Airlines and Aviation"
"Packaging and Containers Manufacturing"
"Defense & Space"
"Beverage Manufacturing"
"Graphic Design"
"Automation Machinery Manufacturing"
"Individual and Family Services"
"Pharmaceutical Manufacturing"
"Research"
"Business Supplies & Equipment"
"Farming"
"Hospital & Health Care"
"Medical Practices"
"Government Administration"
"Wholesale Building Materials"
"Consumer Goods"
"Security and Investigations"
"Furniture"
"Education Management"
"Pharmaceuticals"
"Government Relations Services"
"Oil & Energy"
"Public Safety"
"Fundraising"
"Facilities Services"
"Non-profit Organization Management"
"Venture Capital & Private Equity"
"Computer and Network Security"
"Translation and Localization"
"Primary and Secondary Education"
"Investment Advice"
"Gambling Facilities and Casinos"
"Online Media"
"Civic and Social Organizations"
"Mechanical Or Industrial Engineering"
"Semiconductors"
"Glass, Ceramics and Concrete Manufacturing"
"Glass, Ceramics & Concrete"
"Leisure, Travel & Tourism"
"Strategic Management Services"
"Research Services"
"Other"
"Utilities"
"Think Tanks"
"Writing & Editing"
"Import & Export"
"Gambling & Casinos"
"Veterinary"
"Translation & Localization"
"Defense and Space Manufacturing"
"Textile Manufacturing"
"Sporting Goods"
"Staffing & Recruiting"
"Aviation and Aerospace Component Manufacturing"
"Broadcast Media"
"Veterinary Services"
"E-Learning Providers"
"Sports Teams and Clubs"
"Food and Beverage Manufacturing"
"Recreational Facilities"
"Electrical & Electronic Manufacturing"
"Movies and Sound Recording"
"Judiciary"
"Food and Beverage Retail"
"Outsourcing and Offshoring Consulting"
"Paper and Forest Product Manufacturing"
"Logistics & Supply Chain"
"Investment Banking"
"Political Organizations"
"Computer Software"
"Animation"
"Maritime Transportation"
"Warehousing and Storage"
"Libraries"
"Leasing Non-residential Real Estate"
"Philanthropy"
"Sporting Goods Manufacturing"
"Luxury Goods & Jewelry"
"Maritime"
"Museums, Historical Sites, and Zoos"
"Ranching"
"Cosmetics"
"Computers and Electronics Manufacturing"
"Textiles"
"Building Materials"
"Civic & Social Organization"
"Aviation & Aerospace"
"Architecture & Planning"
"Alternative Dispute Resolution"
"Wireless Services"
"Computer Games"
"Freight and Package Transportation"
"Public Policy Offices"
"Publishing"
"Printing"
"Sports and Recreation Instruction"
"Fisheries"
"Nanotechnology Research"
"Building Construction"
"Public Relations & Communications"
"Program Development"
"Animation and Post-production"
"Armed Forces"
"Computer Hardware Manufacturing"
"Computer Networking"
"Paper & Forest Products"
"Security & Investigations"
"Computer & Network Security"
"Services for Renewable Energy"
"Chemicals"
"IT System Custom Software Development"
"Semiconductor Manufacturing"
"Operations Consulting"
"Professional Services"
"Marketing & Advertising"
"Tobacco Manufacturing"
"Warehousing"
"Executive Office"
"Machinery"
"Internet Marketplace Platforms"
"Outsourcing/Offshoring"
"Insurance Agencies and Brokerages"
"Marketing Services"
"Technology, Information and Media"
"Commercial and Industrial Machinery Maintenance"
"Railroad Equipment Manufacturing"
"Administration of Justice"
"Primary/Secondary Education"
"Security Systems Services"
"Solar Electric Power Generation"
"Internet Publishing"
"Political Organization"
"Legislative Offices"
"Retail Appliances, Electrical, and Electronic Equipment"
"Engineering Services"
"Medical Practice"
"Newspapers"
"Public Policy"
"Renewable Energy Power Generation"
"Motor Vehicle Parts Manufacturing"
"Media and Telecommunications"
"Individual & Family Services"
"Shipbuilding"
"Golf Courses and Country Clubs"
"Rail Transportation"
"Retail Groceries"
"Real Estate Agents and Brokers"
"Wholesale Motor Vehicles and Parts"
"Electric Power Generation"
"Wholesale Furniture and Home Furnishings"
"Vehicle Repair and Maintenance"
"Digital Accessibility Services"
"Recreational Facilities & Services"
"IT System Design Services"
"Retail Furniture and Home Furnishings"
"Health and Human Services"
"Mining & Metals"
"Retail Books and Printed News"
"Fabricated Metal Products"
"Book Publishing"
"Package/Freight Delivery"
"Leather Product Manufacturing"
"Dentists"
"Tobacco"
"Economic Programs"
"Military"
"Ground Passenger Transportation"
"Plastics"
"Dairy"
"Physicians"
"Space Research and Technology"
"Executive Search Services"
"Wholesale Paper Products"
"Repair and Maintenance"
"Personal Care Services"
"Community Services"
"Retail Motor Vehicles"
"Supermarkets"
"Wireless"
"Periodical Publishing"
"Plastics and Rubber Product Manufacturing"
"Retail Health and Personal Care Products"
"Data Infrastructure and Analytics"
"Interior Design"
"Bars, Taverns, and Nightclubs"
"Water, Waste, Steam, and Air Conditioning Services"
"Urban Transit Services"
"Zoos and Botanical Gardens"
"Nanotechnology"
"Airlines/Aviation"
"Railroad Manufacture"
"IT System Operations and Maintenance"
"Leasing Residential Real Estate"
"Fishery"
"Blogs"
"Museums & Institutions"
"Taxi and Limousine Services"
"Wind Electric Power Generation"
"Holding Companies"
"Motion Pictures & Film"
"Retail Building Materials and Garden Equipment"
"Medical and Diagnostic Laboratories"
"Retail Art Dealers"
"Electric Lighting Equipment Manufacturing"
"Social Networking Platforms"
"Commercial and Industrial Equipment Rental"
"Language Schools"
"Animal Feed Manufacturing"
"Funds and Trusts"
"Business Content"
"Theater Companies"
"Internet News"
"Services for the Elderly and Disabled"
"Real Estate and Equipment Rental Services"
"Skiing Facilities"
"IT System Data Services"
"Pet Services"
"Waste Collection"
"Forestry and Logging"
"Data Security Software Products"
"Landscaping Services"
"Architectural and Structural Metal Manufacturing"
"Embedded Software Products"
"Baked Goods Manufacturing"
"Trusts and Estates"
"Wholesale Computer Equipment"
"Soap and Cleaning Product Manufacturing"
"Wholesale Drugs and Sundries"
"Blockchain Services"
"Metal Treatments"
"Public Health"
"Industry Associations"
"Transportation Equipment Manufacturing"
"Metalworking Machinery Manufacturing"
"Primary Metal Manufacturing"
"Hotels and Motels"
"Hydroelectric Power Generation"
"Transportation Programs"
"Housing and Community Development"
"Specialty Trade Contractors"
"Chiropractors"
"Conservation Programs"
"Biomass Electric Power Generation"
"Bed-and-Breakfasts, Hostels, Homestays"
"Physical, Occupational and Speech Therapists"
"Building Structure and Exterior Contractors"
"Online and Mail Order Retail"
"Communications Equipment Manufacturing"
"Fashion Accessories Manufacturing"
"Wholesale Appliances, Electrical, and Electronics"
"Wholesale Alcoholic Beverages"
"Office Furniture and Fixtures Manufacturing"
"Nursing Homes and Residential Care Facilities"
"Farming, Ranching, Forestry"
"Business Intelligence Platforms"
"Professional Organizations"
"Fire Protection"
"Electrical Equipment Manufacturing"
"HVAC and Refrigeration Equipment Manufacturing"
"Measuring and Control Instrument Manufacturing"
"Emergency and Relief Services"
"Retail Recyclable Materials & Used Merchandise"
"Vocational Rehabilitation Services"
"Wholesale Food and Beverage"
"Wholesale Metals and Minerals"
"Spring and Wire Product Manufacturing"
"Wholesale Hardware, Plumbing, Heating Equipment"
"Accessible Architecture and Design"
"Nonmetallic Mineral Mining"
"Glass Product Manufacturing"
"Subdivision of Land"
"Museums"
"Accommodation and Food Services"
"Wholesale Machinery"
"Wineries"
"Electric Power Transmission, Control, and Distribution"
"Electronic and Precision Equipment Maintenance"
"Wholesale Luxury Goods and Jewelry"
"Building Equipment Contractors"
"Administrative and Support Services"
"Hospitals"
"Artificial Rubber and Synthetic Fiber Manufacturing"
"Environmental Quality Programs"
"Wholesale Recyclable Materials"
"Commercial and Service Industry Machinery Manufacturing"
"Agricultural Chemical Manufacturing"
"Wood Product Manufacturing"
"Nuclear Electric Power Generation"
"Residential Building Construction"
"Surveying and Mapping Services"
"Household Appliance Manufacturing"
"Sound Recording"
"Wholesale Chemical and Allied Products"
"Footwear Manufacturing"
"Meat Products Manufacturing"
"Waste Treatment and Disposal"
"Equipment Rental Services"
"Shuttles and Special Needs Transportation Services"
"Cosmetology and Barber Schools"
"Security Guards and Patrol Services"
"Technical and Vocational Training"
"IT System Training and Support"
"Legislative Office"
"Home Health Care Services"
"Wholesale Raw Farm Products"
"Climate Data and Analytics"
"Mobile Computing Software Products"
"Apparel Manufacturing"
"Rubber Products Manufacturing"
"Mobile Gaming Apps"
"Mattress and Blinds Manufacturing"
"Audio and Video Equipment Manufacturing"
"Retail Musical Instruments"
"Sugar and Confectionery Product Manufacturing"
"Wholesale Petroleum and Petroleum Products"
"Horticulture"
"Paint, Coating, and Adhesive Manufacturing"
"Retail Florists"
"Highway, Street, and Bridge Construction"
"Desktop Computing Software Products"
"Performing Arts and Spectator Sports"
"Insurance and Employee Benefit Funds"
"Community Development and Urban Planning"
"Renewable Energy Equipment Manufacturing"
"Telephone Call Centers"
"IT System Testing and Evaluation"
"Climate Technology Product Manufacturing"
"Construction Hardware Manufacturing"
"Chemical Raw Materials Manufacturing"
"Water Supply and Irrigation Systems"
"Distilleries"
"Metal Ore Mining"
"Wholesale Photography Equipment and Supplies"
"Child Day Care Services"
"Dance Companies"
"Courts of Law"
"Utilities Administration"
"Radio and Television Broadcasting"
"Building Finishing Contractors"
"Sheet Music Publishing"
"Geothermal Electric Power Generation"
"Retail Gasoline"
"Loan Brokers"
"Historical Sites"
"Air, Water, and Waste Program Management"
"Utility System Construction"
"Collection Agencies"
"Pipeline Transportation"
"Accessible Hardware Manufacturing"
"Janitorial Services"
"Caterers"
"Retail Pharmacies"
"Retail Office Supplies and Gifts"
"Household Services"
"Nonresidential Building Construction"
"Alternative Fuel Vehicle Manufacturing"
"Engines and Power Transmission Equipment Manufacturing"
"Mobile Food Services"
"Satellite Telecommunications"
"Sightseeing Transportation"
"Metal Valve, Ball, and Roller Manufacturing"
"Amusement Parks and Arcades"
"Laundry and Drycleaning Services"
"Wholesale Footwear"
"Securities and Commodity Exchanges"
"Fine Arts Schools"
"Turned Products and Fastener Manufacturing"
"Oil, Gas, and Mining"
"Robot Manufacturing"
"Telecommunications Carriers"
"Seafood Product Manufacturing"
"Office Administration"
"Optometrists"
"Wholesale Apparel and Sewing Supplies"
"Oil Extraction"
"Boilers, Tanks, and Shipping Container Manufacturing"
"Robotics Engineering"
"Housing Programs"
"Military and International Affairs"
"Cable and Satellite Programming"
"Clay and Refractory Products Manufacturing"
"Insurance Carriers"
"Flight Training"
"Fossil Fuel Electric Power Generation"
"Household and Institutional Furniture Manufacturing"
"Interurban and Rural Bus Services"
"Ranching and Fisheries"
"Natural Gas Extraction"
"Postal Services"
"Reupholstery and Furniture Repair"
"Temporary Help Services"
"Consumer Goods Rental"
"Steam and Air-Conditioning Supply"
"Coal Mining"
"Ambulance Services"
"Women's Handbag Manufacturing"
"Pension Funds"
"Fruit and Vegetable Preserves Manufacturing"
"Credit Intermediation"
"Regenerative Design"
"Circuses and Magic Shows"
"Magnetic and Optical Media Manufacturing"
"Claims Adjusting, Actuarial Services"
"Oil and Coal Product Manufacturing"
"IT System Installation and Disposal"
"Natural Gas Distribution"
"Personal and Laundry Services"
"Footwear and Leather Goods Repair"
"Correctional Institutions"
"Breweries"
"Mobile Games"
"Racetracks"
"Outpatient Care Centers"
"Abrasives and Nonmetallic Minerals Manufacturing"
"School and Employee Bus Services"
"Public Assistance Programs"
"Savings Institutions"
"Family Planning Centers"
"Cutlery and Handtool Manufacturing"
"Lime and Gypsum Products Manufacturing"
"Secretarial Schools"
"null"
"Smart Meter Manufacturing"
"Fuel Cell Manufacturing"
country input values
"Afghanistan"
"Albania"
"Algeria"
"Andorra"
"Angola"
"Antigua and Barbuda"
"Argentina"
"Armenia"
"Australia"
"Austria"
"Azerbaijan"
"Ãland"
"Bahamas"
"Bahrain"
"Bangladesh"
"Barbados"
"Belarus"
"Belgium"
"Belize"
"Benin"
"Bhutan"
"Bolivia"
"Bonaire, Sint Eustatius, and Saba"
"Bosnia and Herzegovina"
"Botswana"
"Brazil"
"Brunei"
"Bulgaria"
"Burkina Faso"
"Burundi"
"Cabo Verde"
"Cambodia"
"Cameroon"
"Canada"
"Central African Republic"
"Chad"
"Chile"
"China"
"Cocos (Keeling) Islands"
"Colombia"
"Comoros"
"Congo"
"Costa Rica"
"Côte d'Ivoire"
"Croatia"
"Cuba"
"Cyprus"
"Czechia"
"Democratic Republic of the Congo"
"Denmark"
"Djibouti"
"Dominica"
"Dominican Republic"
"Ecuador"
"Egypt"
"El Salvador"
"Equatorial Guinea"
"Eritrea"
"Estonia"
"Eswatini"
"Ethiopia"
"Fiji"
"Finland"
"France"
"Gabon"
"Gambia"
"Georgia"
"Germany"
"Ghana"
"Greece"
"Grenada"
"Guatemala"
"Guinea"
"Guinea-Bissau"
"Guyana"
"Haiti"
"Heard and McDonald Islands"
"Holy See"
"Honduras"
"Hong Kong"
"Hungary"
"Iceland"
"India"
"Indonesia"
"Iran"
"Iraq"
"Ireland"
"Israel"
"Italy"
"Jamaica"
"Japan"
"Jersey"
"Jordan"
"Kazakhstan"
"Kenya"
"Kiribati"
"Kosovo"
"Kuwait"
"Kyrgyzstan"
"Laos"
"Latvia"
"Lebanon"
"Lesotho"
"Liberia"
"Libya"
"Liechtenstein"
"Lithuania"
"Luxembourg"
"Madagascar"
"Malawi"
"Malaysia"
"Maldives"
"Mali"
"Malta"
"Marshall Islands"
"Mauritania"
"Mauritius"
"Mexico"
"Micronesia"
"Moldova"
"Monaco"
"Mongolia"
"Montenegro"
"Morocco"
"Mozambique"
"Myanmar"
"Namibia"
"nan"
"Nauru"
"Nepal"
"Netherlands"
"New Zealand"
"Nicaragua"
"Niger"
"Nigeria"
"North Korea"
"North Macedonia"
"Norway"
"Oman"
"Other"
"Pakistan"
"Palau"
"Palestine State"
"Panama"
"Papua New Guinea"
"Paraguay"
"Peru"
"Philippines"
"Poland"
"Portugal"
"Qatar"
"Romania"
"Russia"
"Rwanda"
"Saint Kitts and Nevis"
"Saint Lucia"
"Saint Pierre and Miquelon"
"Saint Vincent and the Grenadines"
"Samoa"
"San Marino"
"Sao Tome and Principe"
"Saudi Arabia"
"Senegal"
"Serbia"
"Seychelles"
"Sierra Leone"
"Singapore"
"Slovakia"
"Slovenia"
"Solomon Islands"
"Somalia"
"South Africa"
"South Georgia and South Sandwich Islands"
"South Korea"
"South Sudan"
"Spain"
"Sri Lanka"
"Sudan"
"Suriname"
"Svalbard and Jan Mayen"
"Sweden"
"Switzerland"
"Syria"
"Taiwan"
"Tajikistan"
"Tanzania"
"Thailand"
"Timor-Leste"
"Togo"
"Tonga"
"Trinidad and Tobago"
"Tunisia"
"Turkey"
"Turkmenistan"
"Tuvalu"
"U.S. Outlying Islands"
"Uganda"
"Ukraine"
"United Arab Emirates"
"United Kingdom"
"United States"
"Uruguay"
"Uzbekistan"
"Vanuatu"
"Venezuela"
"Vietnam"
"Wallis and Futuna"
"Yemen"
"Zambia"
"Zimbabwe"
funding_last_round_type input values
"Seed"
"Pre seed"
"Series A"
"Grant"
"Non equity assistance"
"Private equity"
"Series unknown"
"Post IPO equity"
"Convertible note"
"Series E"
"Series B"
"Equity crowdfunding"
"Secondary market"
"Series C"
"Undisclosed"
"Debt financing"
"Angel"
"Corporate round"
"Series D"
"Post IPO debt"
"Series G"
"Initial coin offering"
"Post IPO secondary"
"Product crowdfunding"
"Series F"
"Series H"
"Series I"
"Series J"
"Serie B"
employment_type input values
| "Full-time" |
| ------------ |
| "Part-time" |
| "Contract" |
| "Internship" |
| "Temporary" |
| "Volunteer" |
| "Other" |
### Sorting options
By default, search filter results are sorted by `last_updated` and `id` in descending order. To sort by `id` in ascending order, add the query parameter `?sort=id` to your request.
# Search Preview
Search Preview returns up to 20 matching records with fresh data in a single API call — no separate Collect request needed. Supports pagination.
## Introduction to Search Preview
The Search Preview feature enhances search functionality by returning up to 20 top matching results with essential contextual data in a single API call. Unlike our standard Search endpoints, which return only record IDs and require subsequent API Collect calls, `preview` endpoints significantly reduce frontend latency, enabling more responsive and user-friendly search experiences and unlocking new capabilities.
### Discover products
Find all APIs with search preview endpoints, request samples and included response fields.
## Requests and queries
* Search Preview endpoints accept the same query structure as their corresponding Search endpoints. You can reference the existing Search endpoint documentation for each entity type, as the filtering and query logic are very similar
* Pagination is not mandatory
* The `_sort` function is not supported, meaning sorting order in the results cannot be specified
## Response behavior and headers
1. Request returns a list of objects. Each object includes:
* A limited set of predefined fields, specific to the entity type
* Record ID
* Elasticsearch match score
2. Response gives up to 20 results per request, without using pagination
### Fewer response headers are returned
* `x-total-results`: Reflects the total number of matched records
* `x-credits-remaining`: Shows remaining credits for the user
## Pagination
Pagination with Search Preview endpoints allows receiving up to 5 pages of results, therefore, accessing up to 100 results. To view a specific page, the user must include the query parameter `page` and provide the corresponding page number they wish to retrieve. Each request to open a new page will consume additional credits.
## Credits
Search Preview requests consume credits by the logic described in the [Credits](/api-introduction/credits) topic.
# Collect and Enrich
## Introduction to Collect requests
A collect request is an API call that requests data from a server. Each request returns a single record. To retrieve multiple records, use [bulk collect](/api-introduction/requests/bulk-collect) requests.
This type of request is commonly used with two kinds of endpoints:
* **Collect Endpoint**: Used to get a set of data using IDs or shorthand names.
* **Enrich Endpoint**: Used to get a set of data using websites or social media profile URLs as input.
### Discover products
Find APIs collect and enrich endpoints, request samples and additional information.
## Supported URL formats for Enrich endpoints
Enrich endpoints should handle a variety of URL formats and match them with the companies in the used dataset.
Examples of different valid inputs include, but are not limited to:
* Full profile URL (e.g., )
* Variations with/without trailing slashes or subdirectories
* Variations with/without `www`
* URLs with/without subdomains (e.g., )
* Variations with/without HTTP/HTTPS URL part.
## Collect all data
This is a simple request that collects a pre-defined set of information and is used to get a large set of data. See available templates below:
{% tabs %}
{% tab title="Collect" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/{entity}/collect/{profile_id/shorthand_name}' \
-H 'accept: application/json' \
-H 'apikey: {API Key}'
```
{% endtab %}
{% tab title="Enrich" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/{entity}/enrich?website={URL}' \
-H 'accept: application/json' \
-H 'apikey: {API Key}'
```
{% endtab %}
{% endtabs %}
## Collect selected data
Field Selection feature for Collect and Enrich APIs enables choosing what fields are required in the output from the available fields in the documentation.
Use a query string parameter `fields` and specify the field you want to collect according to the applied entity. For multiple fields collection, separate each parameter with `&` symbol.
See available templates below:
{% tabs %}
{% tab title="Collect" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/{entity}/collect/{profile_id/shorthand_name}?fields={field_name}&fields={field_name}' \
-H 'accept: application/json' \
-H 'apikey: {API Key}'
```
{% endtab %}
{% tab title="Enrich" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/{entity}/enrich?website={URL}&fields={field_name}&fields={field_name}' \
-H 'accept: application/json' \
-H 'apikey: {API Key}'
```
{% endtab %}
{% endtabs %}
# Bulk Collect
## Introduction to Bulk Collect
**Bulk Collect** allows you to retrieve large sets of data in a single request. It’s designed for efficiency, allowing you to collect multiple records at once instead of making repeated individual queries. Use it when you need to gather high volumes of data quickly and reliably.
### Discover products
Find APIs bulk collect endpoints, request samples and additional information.
## Credits
Credits are deducted for Bulk Collect data requests when the data is ready for download. The deduction amount depends on the number of records and the endpoint used, since you download multiple records at once.
{% hint style="success" %}
Test POST requests using regular endpoints to see how many credits the request will consume. The returned record count indicates the number of credits that will be deducted for the Bulk Collect query.
{% endhint %}
Check the [frequently asked questions](/api-introduction/credits) about credits.
## Count limits
Bulk Collect requests' retrieved profiles **limit is 10,000**. Requests that exceed the limit will be rejected.
* If your **ID list** request to `data_request` endpoint exceeds 10,000 IDs, the request will be rejected. You will not be able to create a request that exceeds the limit.
* If the results that match the Bulk Collect Search filter or the Bulk Collect Elasticsearch DSL exceed 10,000 IDs, the request will be rejected in another way. Here, you will be able to create a request, and when the results exceed the limit while collecting the data, the request will be rejected. Several ways to know about this situation:
* While trying to collect the results
* A webhook about the failure will be sent if a webhook\_url is given
## Limiting the returned record count
Since Bulk Collect queries might use more credits than expected, you can use a limit to control your record count. Parameter `"limit": int` allows control the number of records returned by your queries.
Input the exact number of records you want to be returned, or delete the parameter if you don't need a set limit for your request – this parameter is entirely optional.
{% code title="General request template" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/data_requests/{entity}/{query-type}' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"limit": {optional_integer}
}'
```
{% endcode %}
# POST Requests
## Endpoints usage
Learn how to make Search filters or Elasticsearch DSL requests for data in bulk.
1. Prepare your request using the template below:
* Enter `{entity}` of the used endpoint
* Insert your API Key instead of `{API Key}`
* Input required search filters in the `"filters": {}` section or Elasticsearch DSL filters in the `"es_dsl_query": {}` section
* Input your webhook URL instead of `{optional_webhook_url}`
* Input the required number of records instead of `{optional_integer}` by limit parameter
{% hint style="success" %}
Keep in mind that parameters `webhook_url` and `limit` are **optional.**
{% endhint %}
{% tabs %}
{% tab title="Search filters template" %}
{% code title="Request body template" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/data_requests/{entity}/filter' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"webhook_url": "{optional_webhook_url}",
"limit": {optional_integer},
"filters": {}
}'
```
{% endcode %}
{% endtab %}
{% tab title="Elasticsearch DSL" %}
{% code title="Request body template" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/data_requests/{entity}/es_dsl' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"webhook_url": "{optional_webhook_url}",
"limit": {optional_integer}
"es_dsl_query": {}
}'
```
{% endcode %}
{% endtab %}
{% endtabs %}
2. Import the cURL to any API-compatible application.
3. Send the request.
4. Retrieve the request ID from the response body:
{% code title="Request ID example" %}
```json
{
"request_id": "433869ec-0a98-4dcd-9b13-db4df58260f5"
}
```
{% endcode %}
* `Location` response header provides a URL where the results can be retrieved.
> Location: /v2/data\_requests/e000b0ec-0f00-0b00-0a0a-0b00fa0000d0/files
***
### IDs request
Part of entities support endpoint `/v2/data_requests/{entity}/ids` , when you must submit a list of IDs to the request to receive data in bulk. Please review the API information to see the supported endpoint.
1. Prepare your request using the template below:
* Enter `{entity}` of the used endpoint
* Enter your API Key instead of `{Api Key}` and, optionally, webhook URL in the cURL request template
{% code title="cURL request" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/data_requests/{entity}/ids' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: multipart/form-data' \
-F 'webhook_url={optional_webhook_url}' \
-F 'ids={list of ids}'
```
{% endcode %}
2. Import the edited cURL request to Postman or any other API-compatible application.
3. Send the request.
4. Retrieve the `request_id` from the response body:
{% code title="Request ID" %}
```json
{
"request_id": "433869ec-0a98-4dcd-9b13-db4df58260f5"
}
```
{% endcode %}
* `Location` response header provides a URL where the results can be retrieved.
> Location: /v2/data\_requests/e000b0ec-0f00-0b00-0a0a-0b00fa0000d0/files
### ID File requests
{% hint style="info" %}
Notice that in Base Employee API IDs are passed as a list
{% endhint %}
Part of entities support endpoint `/v2/data_requests/{entity}/id_file` , when you must submit a list of IDs in a .csv or .txt file to request data in bulk. Please review the API information to see the supported endpoint.
Example for the ID list formatting:
ID list example
1
2
3
1. Prepare the IDs file in a .csv or .txt format. Make sure the list only contains numeric IDs.
{% hint style="warning" %}
Avoid any additional headings in the file. The request will fail if non-numeric data is present in the file.
{% endhint %}
2. Prepare your request using the template below:
* Enter `{entity}` of the used endpoint
* Enter your API Key instead of `{Api Key}` and, optionally, webhook URL in the cURL request template
{% code title="cURL request" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/data_requests/{entity}/id_file' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: multipart/form-data' \
-F 'ids_file=@id_list_example.csv;type=text/csv' \
-F 'webhook_url={optional_webhook_url}'
```
{% endcode %}
3. Import the edited cURL request to Postman or any other API-compatible application.
{% hint style="warning" %}
Further instructions are for the POST requests using Postman.
{% endhint %}
4. Open **Body** tab and upload the IDs file by clicking the **Select Files** button in the **Value** column:
5. Send the request.
6. Retrieve the `request_id` from the response body:
{% code title="Request ID" %}
```json
{
"request_id": "433869ec-0a98-4dcd-9b13-db4df58260f5"
}
```
{% endcode %}
* `Location` response header provides a URL where the results can be retrieved.
> Location: /v2/data\_requests/e000b0ec-0f00-0b00-0a0a-0b00fa0000d0/files
***
### Shorthand names and URLs requests
{% hint style="warning" %}
This section is used for [Clean Employee API](/employee-api/clean-employee-api) and [Base Employee API](/employee-api/base-employee-api) indexes
{% endhint %}
You can send up to 10,000 `shorthand_names` or `URLs` per request.
Requirements for `shorthand_names` are listed below:
* Must not be an empty string (`""`)
* Must not contain capital letters
* Must not have leading or trailing spaces (`" john-doe "`)
* Most special characters are not allowed
* Length must be between 3 and 100 characters
#### Endpoint usage
1. Prepare your request using the template below:
* Enter `{entity}` of the used endpoint
* Input the required data in the `"shorthand_names": []` or `"urls": []` section
* Insert your API Key instead of `{API Key}`
{% tabs %}
{% tab title="Template for shorthand\_names" %}
{% code title="Request body template for shorthand\_names" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/data_requests/{entity}/shorthand_names' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"webhook_url": "{optional_webhook_url}",
"shorthand_names": []
}'
```
{% endcode %}
{% endtab %}
{% tab title="Template for urls" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/data_requests/{entity}/urls' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"webhook_url": "{optional_webhook_url}",
"urls": []
}'
```
{% endtab %}
{% endtabs %}
2. Import the cURL to any API-compatible application.
3. Send the request.
# GET Requests
{% columns %}
{% column width="16.666666666666664%" %}
Data type:
URLs:
{% endcolumn %}
{% column %}
All Multi-source, Clean and Base data
{% endcolumn %}
{% endcolumns %}
***
## Overview
Send a GET request to each of the specified GET endpoints to retrieve data in bulk:
1. Make a query in the `/v2/data_requests/{data_request_id}/files` endpoint to see the status of your data request. Collect the file name to use further.
2. Collect the data request ID and the file name. Make a query in the `/v2/data_requests/{data_request_id}/files/{file_name}` endpoint and download the data in a JSON.gz file.
{% hint style="info" %}
You can download the prepared dataset as many times as you like within 30 days of the query submission.
{% endhint %}
***
## Instructions
### GET the file name
Send a GET request to the endpoint `/v2/data_requests/{data_request_id}/files` to see the status of your data request.
#### Endpoint usage
1. Take the data `request ID` (obtained from any of the POST endpoints). Paste it in the following cURL request instead of `{request_id}` and your API Key instead of `{API Key}`:
{% code title="Request body template" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/data_requests/{data_request_id}/files' \
-H 'accept: application/json' \
-H 'apikey: {API Key}'
```
{% endcode %}
2. Import the cURL request to any API-compatible application.
3. Send the request.
* If you do not see a file name, the data request is not ready. Send the request again until you can see the file name.
4. Retrieve the file name.
{% code title="File name example" %}
```json
{
"data_request_files": [
"json/part-00000-2c3d41c2-99c2-43ff-a39c-7ad7h63h7cd9-h000.json.gz"
]
}
```
{% endcode %}
* Each JSON.gz file contains a maximum of 10,000 JSON records. Requests that exceed the limit will be rejected.
#### Insufficient credits
The following message indicates that your query is too expensive.
{% hint style="info" %}
Try adding `"limit": {integer}` parameter to your query.
{% endhint %}
{% code title="Insufficient credits" %}
```json
{
"detail": "Insufficient credits"
}
```
{% endcode %}
***
### GET the file
Request downloadable files by making a GET request to the `/v2/data_requests/{data_request_id}/files/{file_name}` endpoint.
#### Endpoint usage
1. Paste the data `request ID` and `file name` in the following template. Use your API Key instead of `{API Key}`:
{% code title="Request body template" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/data_requests/{data_request_id}/files/{file_name}' \
-H 'accept: application/json' \
-H 'apikey: {API Key}'
```
{% endcode %}
{% hint style="info" %}
The data request ID is obtained via the POST requests.\
The file name is obtained using the data request ID in the */v2/data\_requests/{data\_request\_id}/files* endpoint*.*
{% endhint %}
2. Import the cURL to any API-compatible application.
3. Send the request
4. Download the data
Check for similar methods to retrieve the file using other API-compatible applications.
# Webhooks
Get real-time notifications on employee profile changes via webhooks. Subscriptions are valid for 91 days with no credit deduction per notification.
## Introduction
A webhook subscription allows to receive real-time notifications whenever specific events occur. Instead of constantly polling an API to check for updates, the system automatically sends event data to a predefined URL (the webhook endpoint).
The subscription is valid for 91 days. During this period, you'll receive notifications about all changes in the selected employee profiles.
Experience webhooks
Get notified when a change is made in employee experience
## Manage the subscriptions
There are additional endpoints for subscription management. Use them to list your active subscriptions, see the details, renew subscriptions and delete irrelevant ones.
## Try the feature
Still not sure if this feature is right for you? Use `/v2/subscriptions/simulate` endpoint to simulate an active webhook subscription.
# Experience Webhooks
The Experience Webhooks deliver weekly notifications when employees start new positions, get promotions, or close existing ones. These structured updates help you monitor meaningful career changes across companies and industries without the need for constant manual checks or API calls.
Find endpoints and subscription examples in the corresponding topics:
## Subscription frequency
Delivery frequency is determined by the specific API data source you are utilizing. Corresponding cadence:
| API source | Update frequency |
| ------------------------- | ---------------- |
| Base Employee API | Daily |
| Clean Employee API | Weekly |
| Multi-Source Employee API | Weekly |
{% hint style="info" %}
**Multi-source, Clean, or Base?**
* **Multi-source datasets** contain cleaned and enriched data combining information from multiple sources.
* **Clean datasets** are derived from our Base data and cleaned to ensure the best quality.
* **Base datasets** freshly scraped and structured/updated for easier use.
{% endhint %}
### Webhook payload
Each webhook notification includes the following fields:
* `member_id` – The ID of the employee profile that was updated.
* `status` – Notifies a changed state.
{% code title="Example payload" %}
```json
[
{
"member_id": 125,
"status": "changed"
}
]
```
{% endcode %}
### Status for Experience changes
| Status | Description |
| --------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `changed` |
Profiles that have undergone non-specified changes in the experience section.
To see the exact changes, you will need to query them using Bulk Collect GET or collection endpoints.
|
### Webhook triggers
The subscribed experience change webhook is triggered when:
1. Employee starts a new experience position
2. Employee closes their previously open experience position
# Employee Webhooks
Employee Webhooks send alerts when changes occur in key employee profile data fields. Subscribe and automatically receive updates reflecting the latest changes in employee records that matter to you.
Find endpoints and subscription examples in the corresponding topics:
## Subscription frequency
Delivery frequency is determined by the specific API data source you are utilizing. Corresponding cadence:
| API source | Update frequency |
| ------------------------- | ---------------- |
| Base Employee API | Daily |
| Clean Employee API | Weekly |
| Multi-Source Employee API | Weekly |
{% hint style="info" %}
**Multi-source, Clean, or Base?**
* **Multi-source datasets** contain cleaned and enriched data combining information from multiple sources.
* **Clean datasets** are derived from our Base data and cleaned to ensure the best quality.
* **Base datasets** freshly scraped and structured/updated for easier use.
{% endhint %}
## Functionality
1. Choose the employees you want to track (using an IDs list, search filter query, or Elasticsearch DSL filter query).
2. Provide a callback URL to receive notifications.
3. Receive notifications at your URL and retrieve the data using the corresponding APIs collection or Bulk Collect endpoints.
### Subscribe by field
By default, Employee Webhooks trigger on **any** change to a tracked profile. The **subscribe by field** feature lets you narrow your subscription to only receive notifications when specific fields are updated.
You still define your tracked population using IDs, Search filters, or Elasticsearch DSL queries. The `tracked_fields` parameter acts as an additional filter on top of that population, specifying which fields to watch.
{% hint style="info" %}
#### **Example**
You are tracking 500 employees by IDs but only care about `skills` changes. By adding `"tracked_fields": ["skills"]` to your subscription request, you will only receive webhooks when the `skills` field is updated – all other field changes are ignored.
{% endhint %}
### Webhook triggers
The following employee profile fields are tracked for changes. When any of these fields are updated, a webhook notification is sent.
## Webhook payload
Each webhook notification includes the following fields:
* `member_id` – The ID of the employee profile that was updated.
* `status` – The type of change detected (see status values below).
* `changed_fields` – An array listing the specific fields that were modified on the profile.
{% code title="Example payload" %}
```json
[
{
"member_id": 123,
"status": "started_matching_query",
"changed_fields": null
},
{
"member_id": 124,
"status": "stopped_matching_query",
"changed_fields": null
},
{
"member_id": 125,
"status": "changed",
"changed_fields": ["skills", "headline", "certifications"]
}
]
```
{% endcode %}
The `changed_fields` array tells you exactly which parts of the profile were modified. You can use this to decide whether to retrieve the full updated profile or skip the notification based on your use case.
## Status values
| Status | Description |
| ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `started_matching_query` | Profiles that previously did not match your filters have been updated to meet your criteria. *Example:* a job title was updated to `Project Manager`. |
| `stopped_matching_query` | Profiles that once matched your filters have been updated and no longer match. *Example:* a job title changed from `Project Manager` to `Scrum Master`. |
| `changed` | One or more tracked fields on the profile were updated. The `changed_fields` array in the payload shows which specific fields were modified. To retrieve the new values, use the Collect or Bulk Collect endpoints. |
# Subscription Management
The subscription management endpoints allow you to:
* See all available webhook subscriptions and related information
* See detailed information about your webhook subscriptions
* Renew your subscriptions
* Delete subscriptions
See the endpoints and their functions below:
| Request type | Endpoint | Function |
| ------------ | -------------------------------------------- | ------------------------------------------------------------------------ |
| GET | */v2/subscriptions* | See all your subscriptions, their status, creation, and expiration dates |
| GET | */v2/subscriptions/{subsription\_id}* | See the details of your active subscription |
| POST | */v2/subscriptions/{subscription\_id}/renew* | Renew your subscriptions for up to 1 year |
| DELETE | */v2/subscriptions/{subsription\_id}* | Delete your subscriptions |
## I want to see all my active subscriptions
Use the endpoint `/v2/subscriptions` to see all your available subscriptions, their statuses, creation and expiration dates:
1. Indicate if you want to see the details of active or expired subscriptions (`expired = false` OR `expired = true`)\
You will automatically see only active subscriptions if you omit the `expired = true/false` parameter.
2. Indicate which page number you would like to see.\
Each page contains approximately 1,000 subscriptions, so if you have more than 1,000 subscriptions, you may need to specify the page number.
3. Replace `{API Key}` with your API Key in the template below:
{% code title="Request template" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/subscriptions?expired={false/true}&page={integer}' \
-H 'accept: application/json' \
-H 'apikey: {API Key}'
```
{% endcode %}
4. Send the request.
5. Refer to the *Body* for information about your subscription status:
```json
[
{
"id": "c8e295c0-2f85-473d-8bef-350526e6b30a",
"status": "active",
"created_at": "2024-09-18",
"expiring_at": "2024-12-18"
},
{
"id": "a85b2528-6190-4c91-8489-364b27ccbf81",
"status": "active",
"created_at": "2024-09-18",
"expiring_at": "2024-12-18"
}
]
```
| Data field | Description | Data type |
| ------------- | ------------------------------------------------------------------------------------------------------------------------------------ | ------------- |
| `id` |
Subscription identification key. Subscription ID is displayed in the response body after submitting a webhook subscription
| String |
| `status` | Subscription status (active or expired) | String |
| `created_at` | Subscription start date in `YYYY-MM-DD` format | String (date) |
| `expiring_at` | Subscription expiry date in `YYYY-MM-DD` format | String (date) |
***
## I want to see the details of my subscription
Use the endpoint `/v2/subscriptions/{subscription_id}` to see the details of your expired or active subscriptions:
1. Provide your `{subscription_id}`\*.\
📌\*Subscription ID is displayed in the response body after successfully submitting a webhook subscription.
2. Replace `{API Key}` with your API Key in the template below:
{% code title="Request template" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/subscriptions/{subscription_id}' \
-H 'accept: application/json' \
-H 'apikey: {API Key}'
```
{% endcode %}
3. Send the request.
4. You will see the following information in the *Response body:*
```json
{
"id": "c8e295c0-2f85-473d-8bef-350526e6b30a",
"status": "active",
"created_at": "2024-09-18",
"expiring_at": "2024-12-18",
"entity": "member_changes",
"es_dsl_query": null,
"filters": null,
"last_webhook_sent": "2024-09-30"
}
```
| Data field | Description | Data type |
| ------------------- | -------------------------------------------------------------------------- | ------------- |
| `id` | Subscription identification key. | String |
| `status` | Subscription status (active or expired) | String |
| `created_at` | Subscription start date in `YYYY-MM-DD` format | String (date) |
| `expiring_at` | Subscription expiry date in `YYYY-MM-DD` format | String (date) |
| `entity` | Subscription entity | String |
| `es_dsl_query` | Elasticsearch DSL query used to subscribe to changes in a list of profiles | Object |
| `filters` | Search filters used to subscribe to changes in a list of profiles | Object |
| `last_webhook_sent` | Time of the last message sent to your webhook URL in `YYYY-MM-DD` format | String (date) |
## I want to renew my subscription
Use the endpoint `/v2/subscriptions/{subscription_id}/renew` to renew your subscriptions. A webhook subscription is valid for 91 days from the date it is created. To maintain uninterrupted webhook delivery beyond this window, subscriptions must be renewed before they expire.
Renewals can be scheduled in advance, allowing coverage for up to approximately one year from the initial subscription start date.
{% hint style="info" %}
A subscription can be renewed for up to 1 year. If subscription renewal exceeds this period, the request will be cancelled.
{% endhint %}
1. Provide your `subscription_ID`.
2. Replace `{API Key}` with your API Key.
{% code title="Request template" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/subscriptions/{subscription_id}/renew' \
-H 'accept: application/json' \
-H 'apikey: {API Key}'
```
{% endcode %}
3. Send the request.
4. You will see the following information in the `Response` body:
{% code title="Response" %}
```json
{
"id": "4e11dcae-9bfb-4edb-b4f6-a0991b20fb50",
"status": "active",
"created_at": "2025-04-24T12:00:00.000000",
"expiring_at": "2025-10-24T12:00:00.000000",
}
```
{% endcode %}
| Data field | Description | Data type |
| ------------- | --------------------------------------- | ------------- |
| `id` | Subscription identification key. | String |
| `status` | Subscription status (active or expired) | String |
| `created_at` | Subscription start date | String (date) |
| `expiring_at` | Subscription expiry date | String (date) |
## I want to delete my subscription
Use the endpoint *DELETE* `/v2/subscriptions/{subscription_id}` to make your subscriptions inactive.
Use your subscription ID as input to request updates to employee profiles or experience changes.\
Refer to the template below:
{% code title="Template" %}
```json
curl -X 'DELETE' \
'https://api.coresignal.com/cdapi/v2/subscriptions/{subscription_id}' \
-H 'accept: */*' \
-H 'apikey: {API Key}'
```
{% endcode %}
# Subscription Simulation
{% columns %}
{% column width="16.666666666666664%" %}
URL:
{% endcolumn %}
{% column %}
{% endcolumn %}
{% endcolumns %}
***
Test out our subscription functionality using `simulate` endpoint by creating a fake subscription to receive a webhook example to your callback URL.
## Functionality
The subscription simulation endpoint will allow you to send a test request using your webhook.
As we typically send webhooks once a week, you might have to wait a week to see how our subscription feature works and what kind of payload you receive.
Using the simulation endpoint, you can see how webhooks work and what kind of payload you get the same day.
The endpoint will allow to send one request per second.
## Usage
1. Paste in your webhook URL instead of `{your_webhook_url}` and your API Key in the request template:
{% code title="Template" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/subscriptions/simulate' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"webhook_url": "{your_webhook_url}"
}'
```
{% endcode %}
2. Send your request using an application that supports cURL requests (e.g., Postman).
3. You will see the following response:
```json
{
"message": "Subscriptions webhook simulation initiated"
}
```
4. You should get an example webhook to your URL after a few minutes, e.g.,
```json
{
"member_id": 18330245,
"change_type": "changed"
}
```
The text in the webhook payload is randomized. You will see a random `member_id` and `change_type`.
One of the statuses for change types will be sent. Supported statuses are listed in the main [Webhooks](/api-introduction/webhooks) topic.
# Multi-source Company Data
Enriched fresh company data from multiple sources with firmographics, funding, workforce trends, and historical signals, available via flat files or API.
Multi-source Company data is designed to be used for **sales tech, market research, investment, and risk assessment**.
| **Save engineering resources** | Our Multi-source company data is cleaned, enriched, filtered, and ready to use. |
| -------------------------------------- | --------------------------------------------------------------------------------- |
| **Comprehensive multi-source dataset** | Multiple sources are integrated to provide a complete company overview. |
| **Optimized file sizes and formats** | JSONL and Parquet formats have smaller file sizes, resulting in faster downloads. |
| **Leverage historical data** | Track changes in company metrics over time with aggregated historical data. |
***
## Summary
| Feature | Details |
| ------------------ | --------------------- |
| Available via | Flat files/API |
| Delivery frequency | Monthly and quarterly |
| Available formats | Parquet, JSONL |
| Scraping since | 2016-07 |
## Related links
# Dictionary: Multi-source Company Data
Field-level dictionary for Multi-source Company Data covering fresh firmographics, funding, technographics, workforce trends, salaries, and more.
Find all data fields with explanations available in the Multi-source Company data.
Each category includes a table listing the available data fields, their explanations, and data types.
{% tabs %}
{% tab title="Data fields per category" %}
1. [Metadata](#metadata)
2. [Firmographics](#firmographics)
3. [Company updates](#company-updates)
4. [Locations](#locations)
5. [Public contact details](#public-contact-details)
6. [Follower counts & changes](#follower-counts-and-changes)
7. [Competitors](#competitors)
8. [Product overview](#product-overview)
9. [Financials](#financials)
10. [Funding](#funding)
11. [Acquisitions](#acquisitions)
12. [News features](#news-features)
13. [Technographics](#technographics)
14. [Company websites and social media](#company-websites-and-social-media)
15. [Website traffic](#website-traffic)
16. [Employee review score & changes](#employee-review-scores-and-changes)
17. [Workforce trends](#workforce-trends)
18. [Salaries](#salaries)
{% endtab %}
{% endtabs %}
{% hint style="info" %}
The data fields in the example snippets have been rearranged for better grouping. To see where a specific data field stands, check the full data sample [here](/company-data/multi-source-company-data/sample-multi-source-company-data).
{% endhint %}
{% hint style="info" %}
All personal/company information mentioned within this context is entirely fictional and is solely intended for illustrative purposes.
{% endhint %}
## Metadata
| Data field | Description | Data type |
| ----------------- | --------------------------------------------------------- | --------------- |
| `company_id` | Company record identification key in our database | Integer |
| `source_id` | Identifier assigned by Professional Network | String |
| `expired_domain` | Indicates if the domain is expired | Boolean/integer |
| `unique_domain` | Indicates if the domain is unique | Boolean/integer |
| `unique_website` | Indicates if the website is unique | Boolean/integer |
| `last_updated_at` | Last update date of the record in the `YYYY-MM-DD` format | String (date) |
| `created_at` | Record creation date in the `YYYY-MM-DD` format | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Metadata" %}
```json
"company_id": 8369825,
"source_id": "9082300",
"expired_domain": 0,
"unique_domain": 1,
"unique_website": 1,
"last_updated_at": "2024-04-28"
```
{% endcode %}
## Firmographics
| Data field | Description | Data type |
| -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- | ------------- |
| `company_name` | Company name | String |
| `company_name_alias` | All name variations associated with the company | String |
| `company_legal_name` | Legal company name | String |
| `company_logo` | Base64-encoded image data of the company's logo | String |
| `company_logo_url` | Logo URL (available from Professional network only) | String |
| `is_b2b` |
Indicates if the company operates in a business-to-business model: 1 – b2b company 0 – b2c company
| Integer |
| `industry` | Company's industry | String |
| `type` | Company type | String |
| `founded_year` | Founding year | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Firmographics" %}
```json
"company_name": "Example Company",
"company_legal_name": "Example Company, Inc.",
"company_name_alias": [
"example-company.com",
"Example Company"
"Example Company, Inc. "
]
"company_logo": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoMDAsK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\\r\\nHHqjQ28Rijjkusl2XBJK9Cea8gooAKKKKACiiigAooooAKKKKACiiigD/9k=",
"company_logo_url": "https://www.professional-network.com/logo-url",
"is_b2b": 1,
"industry": "Software Development",
"founded_year": "2000",
```
{% endcode %}
### SIC and NAICS codes
| Data field | Description | Data type |
| ------------- | --------------------- | ---------------- |
| `sic_codes` | Company's SIC codes | Array of strings |
| `naics_codes` | Company's NAICS codes | Array of strings |
**See a snippet of the dataset for reference:**
{% code title="SIC and NAISC codes" %}
```json
"sic_codes": [
"87",
"874"
],
"naics_codes": [
"32",
"325"
],
```
{% endcode %}
### Descriptions
| Data field | Description | Data type |
| -------------------------- | -------------------------------------------------------------------------------------------------- | --------- |
| `description` | Company description | String |
| `description_enriched` | Company description, enriched with LLM | String |
| `description_metadata_raw` |
Company description (parsed from external sources not included in our firmographic data)
| String |
**See a snippet of the dataset for reference:**
{% code title="Descriptions" %}
```json
"description": "Example Company (Nasdaq: EXMP) is a proven cloud CCaaS platform that helps business leaders redefine customer engagement and transform their contact center’s performance. Decision-makers use Example Company to improve customer experience, boost agent productivity, empower their managers, and enhance their system orchestration capabilities. Everything needed to deliver game-changing results can be seamlessly integrated and configured to maximize your success: Omnichannel Communications, AI, a Contact Center CRM, and Workforce Engagement Management tools. For more than 20 years, clients of all sizes and industries have trusted Example Company’s scalable and reliable cloud platform to power billions of omnichannel interactions every year.",
"description_enriched": "Example Company is a cloud-based call and contact center software provider that offers a range of products and solutions for businesses of all sizes. Their platform includes features such as voice, email, SMS, CRM, and workforce management, and they offer a variety of services to support their clients, including training, implementation, and consulting. ",
"description_metadata_raw": "Example Company CCaaS Ups Your Call / Contact Center Platform with Communication Software So You Can Be A Game-Changer: Voice, Email, SMS, CRM, WFM, for Inbound & Outbound Agents.",
```
{% endcode %}
### Company size
| Data field | Description | Data type |
| ----------------- | ------------------------------------------------------------------------------------------------------- | --------- |
| `size_range` |
Company size based on employee count range (as selected by the company profile administrator)
| String |
| `employees_count` | Number of employees on Professional Network who associated their experience with the company | Integer |
**See a snippet of the dataset for reference:**
{% code title="Company size" %}
```json
"size_range": "501-1000 employees",
"employees_count": 594,
```
{% endcode %}
### Inferred employee counts
| Data field | Description | Data type |
| -------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- | ---------------- |
| `employees_count_inferred` | Estimated number of employees, calculated based on inferred employee data | Integer |
| `employees_count_inferred_by_month` | Estimated number of employees, calculated based on inferred employee data, for a three-year rolling window | Array of structs |
| `employees_count_inferred_by_month[].employees_count_inferred` | Estimated number of employees, calculated based on inferred employee data | Integer |
| `employees_count_inferred_by_month[].date` | Date identifier | String |
**See a snippet of the dataset for reference:**
{% code title="Company size" %}
```json
{
"employees_count_inferred": 20,
"employees_count_inferred_by_month": [
{
"employees_count_inferred": 20,
"date": "202504"
},
{
"employees_count_inferred": 18,
"date": "202503"
}
]
}
```
{% endcode %}
### Employee attrition
| Data field | Description | Data type |
| ------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `departures_count` | Count of employees who left the company in the current month | Integer |
| `departures_count_by_month` | Historical monthly departures count with corresponding dates | Array of structs |
| `departures_count_by_month.departures_count` | Number of employee departures | Long |
| `departures_count_by_month.date` | Date for departure count | String |
| `employee_attrition_rate` | Current month attrition rate calculated as (`departures_count` / `employees_count_inferred`) \* 100. Value is `null` when `employees_count_inferred` is `0` or `null` | Double |
| `employee_attrition_rate_by_month` | Historical monthly attrition rates with corresponding dates | Array of structs |
| `employee_attrition_rate_by_month.attrition_rate` | Attrition rate | Double |
| `employee_attrition_rate_by_month.date` | Date for attrition rate | String |
**See a snippet of the dataset for reference:**
{% code title="Employee attrition" %}
```json
{
"departures_count": 20,
"departures_count_by_month": [
{
"departures_count": 20,
"date": "202601"
},
{
"departures_count": 18,
"date": "202602"
}
],
"employee_attrition_rate": 20,
"employee_attrition_rate_by_month": [
{
"attrition_rate": 20,
"date": "202601"
},
{
"attrition_rate": 18,
"date": "202602"
}
]
}
```
{% endcode %}
### Categories & keywords
| Data field | Description | Data type |
| ------------------------- | --------------------------------------------------------------------------------------------- | ---------------- |
| `categories_and_keywords` | Categories and keywords assigned to the company profile and products across various platforms | Array of strings |
**See a snippet of the dataset for reference:**
{% code title="Categories and keywords" %}
```json
"categories_and_keywords": [
"call/contact center software provider",
"call & contact center software",
"contact center software"
],
```
{% endcode %}
### Ownership & status
| Data field | Description | Data type |
| ------------------ | -------------------------------- | ------------------------- |
| `status` | Operational and ownership status | Array of objects (struct) |
| `value` | Current operational status | String |
| `comment` | Current ownership status | String |
| `ownership_status` | Ownership status | String |
**See a snippet of the dataset for reference:**
{% code title="Ownership and status" %}
```json
"status": {
"value": "active",
"comment": "Acquired"
},
"ownership_status": "Public",
```
{% endcode %}
### Parent company
| Data field | Description | Data type |
| ---------------------------- | ---------------------------------------------------- | --------------- |
| `parent_company_information` | Parent company details | Object (struct) |
| `parent_company_id` | Matched parent company ID | String |
| `parent_company_name` | Parent company name | String |
| `parent_company_website` | Parent company website | String |
| `date` | Date of the information provided in `MM/YYYY` format | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Parent company" %}
```json
"parent_company_information": {
"parent_company_id": "1234",
"parent_company_name": "Parent Company",
"parent_company_website": "https://www.parent-company.com/",
"date": "10/2023"
},
```
{% endcode %}
## Company updates
| Data field | Description | Data type |
| ------------------------------- | --------------------------------------------------------------------------------- | ---------------- |
| `company_updates_collection` | Information from posts published by the company | Array of objects |
| `followers` | Profile follower count | Integer |
| `date` | Publish date | String (date) |
| `description` |
Published text
Note: may contain control characters
| String |
| `reactions_count` | Number of reactions on the post | Integer |
| `comments_count` | Number of comments on the post | Integer |
| `reshared_post_author` | Reshared post author | String |
| `reshared_post_author_url` | Profile URL of the reshared post author | String |
| `reshared_post_author_headline` | Headline of the reshared post author | String |
| `reshared_post_description` | Reshared post text | String |
| `reshared_post_followers` | The number of followers of the reshared post author | Integer |
| `reshared_post_date` | Date the reshared post was published | String |
**See a snippet of the dataset for reference:**
{% code title="Company updates" %}
```json
"company_updates_collection": [
{
"followers": 1371,
"date": "2025-03-30",
"description": "Example description",
"reactions_count": 22,
"comments_count": 2,
"reshared_post_author": "John Doe",
"reshared_post_author_url": "https://www.professional-network.com/john-doe",
"reshared_post_author_headline": "Co-Founder at Example Company, TEDx & Keynote Speaker",
"reshared_post_description": "Example description",
"reshared_post_followers": 45,
"reshared_post_date": "1mo"
}
]
```
{% endcode %}
## Locations
| Data field | Description | Data type |
| ------------------------ | --------------------------------------------------------------------------------- | -------------------------- |
| `hq_region` | Region of the company's HQ location | Array of strings |
| `hq_country` | Country where the company's headquarters is located | String |
| `hq_country_iso2` | ISO 2-letter code of the headquarters country | String |
| `hq_country_iso3` | ISO 3-letter code of the headquarters country | String |
| `hq_location` | Headquarters location | String |
| `hq_full_address` | Full address of the headquarters | String |
| `hq_city` | Headquarters city. Data available only for US companies | String |
| `hq_state` | Headquarters state. Data available only for US companies | String |
| `hq_street` | Headquarters street address | String |
| `hq_zipcode` | Headquarters zip code | String |
| `hq_latitude` | Geographic latitude coordinate of the company's headquarters | Double |
| `hq_longitude` | Geographic longitude coordinate of the company's headquarters | Double |
| `hq_apartment` | Apartment number or unit identifier for the headquarters address, when applicable | String |
| `hq_suite` | Suite number or office designator for the headquarters address, when applicable | String |
| `company_locations_full` | List of company locations. Data includes raw values | Array of objects (structs) |
| `location_address` | Company location address | String |
| `is_primary` | Indicates if this is the primary company location | Boolean |
| `state` | State of the company location | String |
| `city` | City name of the company location | String |
| `street` | Street address of the company location | String |
| `zip_code` | Postal or ZIP code of the company location | String |
| `latitude` | Geographic latitude coordinate of the company location | Double |
| `longitude` | Geographic longitude coordinate of the company location | Double |
**See a snippet of the dataset for reference:**
{% code title="Locations" %}
```json
"hq_region": [
"Americas",
"Northern America",
"AMER"
],
"hq_country": "United States",
"hq_country_iso2": "US",
"hq_country_iso3": "USA",
"hq_location": "Austin, TX, United States",
"hq_full_address": "123 Main Street; Suite 500; Austin, TX 78701, US",
"hq_city": "Austin",
"hq_state": "Texas",
"hq_street": "123 Main Street; Suite 500",
"hq_zipcode": "78701",
"hq_apartment": null,
"hq_suite": "Suite 500",
"hq_latitude": 30.266666,
"hq_longitude": -97.733330,
"company_locations_full": [
{
"location_address": "123 Main Street; Suite 500; Austin, TX 78701, US",
"is_primary": true,
"city": "Austin",
"state": "Texas",
"street": "123 Main Street",
"zip_code": "78701",
"latitude": 30.266666,
"longitude": -97.733330
}
],
```
{% endcode %}
## Public contact details
| Data field | Description | Data type |
| ----------------------- | ---------------------- | ---------------- |
| `company_phone_numbers` | Public phone numbers | Array of strings |
| `company_emails` | Public email addresses | Array of strings |
**See a snippet of the dataset for reference:**
{% code title="Public contact details" %}
```json
"company_phone_numbers": [
"(555) 123-4567"
],
"company_emails": [
"info@example-company.com"
],
```
{% endcode %}
## Follower counts & changes
### Follower counts
| Data field | Description | Data type |
| -------------------------------------- | ---------------------------------------------- | -------------- |
| `followers_count_professional_network` | Profile follower count on professional network | Integer |
| `followers_count_twitter` | Profile follower count on Twitter | Integer (long) |
| `followers_count_owler` | Profile follower count on Owler | Integer (long) |
**See a snippet of the dataset for reference:**
{% code title="Follower counts" %}
```json
"followers_count_professionnal_network": 12838,
"followers_count_twitter": 705,
"followers_count_owler": 188,
```
{% endcode %}
### Follower count changes
| Data field | Description | Data type |
| --------------------------------------------- | --------------------------------------------------------------------------------- | --------------- |
| `professional_network_followers_count_change` | Changes in the number of followers over different periods on professional network | Object (struct) |
| `current` | Current number of followers on the professional network | Integer (long) |
| `change_monthly` | Monthly change in follower count on the professional network | Integer (long) |
| `change_monthly_percentage` | Monthly percentage change in follower count on the professional network | Float (double) |
| `change_quarterly` | Quarterly change in follower count on the professional network | Integer (long) |
| `change_quarterly_percentage` | Quarterly percentage change in follower count on the professional network | Float (double) |
| `change_yearly` | Yearly change in follower count on the professional network | Integer (long) |
| `change_yearly_percentage` | Yearly percentage change in follower count on the professional network | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Professional network followers" %}
```json
"professional_network_followers_count_change": {
"current": 12779,
"change_monthly": 70,
"change_monthly_percentage": 0.5507907781886852,
"change_quarterly": 891,
"change_quarterly_percentage": 7.494952893674293,
"change_yearly": 1845,
"change_yearly_percentage": 16.873971099323214
},
```
{% endcode %}
| Data field | Description | Data type |
| ----------------------------------------------- | ---------------------------------------------------------------------------------------------------------- | ------------------------- |
| `professional_network_followers_count_by_month` |
Professional network follower count changes by month. Counts available from 2019.01
| Array of objects (struct) |
| `follower_count` | Number of followers | Integer (long) |
| `date` | Record date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Professional network followers" %}
```json
"professional_network_followers_count_by_month": [
{
"follower_count": 0,
"date": "2019-11"
},
{
"follower_count": 1,
"date": "2021-01"
}
],
```
{% endcode %}
## Competitors
| Data field | Description | Data type |
| ------------------------------ | ----------------------------------------------------- | ----------------------------------- |
| `competitors` | Competitors and their similarity scores | Array of objects (struct) |
| `company_name` | Competitor's name | String |
| `similarity_score` | Score indicating the similarity to the record company | Integer (long) |
| `competitors_websites` | Details on the competitors' websites |
Array of objects (struct)
|
| `website` | Competitor's website URL | String |
| `total_website_visits_monthly` | Total number of monthly competitor's website visits | Integer (long) |
| `category` | Competitor's website category | String |
| `rank_category` | Competitor's website rank within its category | Integer |
**See a snippet of the dataset for reference:**
{% code title="Competitors" %}
```json
"competitors": [
{
"company_name": "first competitor",
"similarity_score": 5321
},
{
"company_name": "second competitor",
"similarity_score": 5605
}
],
"competitors_websites": [
{
"website": "example-website.com",
"similarity_score": 100,
"total_website_visits_monthly": 91600,
"category": "Law and Government > Government",
"rank_category": 13758
},
{
"website": "example-website2.com",
"similarity_score": 100,
"total_website_visits_monthly": 403700,
"category": "Law and Government > Government",
"rank_category": 3510
}
],
```
{% endcode %}
## Product overview
| Data field | Description | Data type |
| ---------------------- | ---------------------------------------------------------- | --------- |
| `pricing_available` | Marks if service pricing information is available online | Boolean |
| `free_trial_available` | Marks if the company offers a free trial of their services | Boolean |
| `demo_available` | Marks if the company offers a demo | Boolean |
| `is_downloadable` | Marks if the company offers a downloadable file/service | Boolean |
| `mobile_apps_exist` | Marks if the company has mobile apps | Boolean |
| `online_reviews_exist` | Marks if the company has any online reviews | Boolean |
| `api_docs_exist` | Marks if the company has public API docs | Boolean |
**See a snippet of the dataset for reference:**
{% code title="Product and services overview" %}
```json
"pricing_available": false,
"free_trial_available": false,
"demo_available": false,
"is_downloadable": false,
"mobile_apps_exist": false,
"online_reviews_exist": false,
"documentation_exist": false,
```
{% endcode %}
### Product pricing
| Data field | Description | Data type |
| ------------------------- | -------------------------------- | -------------------------- |
| `product_pricing_summary` | Summary of product pricing plans | Array of objects (structs) |
| `type` | Pricing plan type | String |
| `price` | Plan price | String |
| `details` | Pricing plan details | String |
**See a snippet of the dataset for reference:**
{% code title="Product pricing" %}
```json
"product_pricing_summary": [
{
"type": "First plan",
"price": "38.00",
"details": "Per Month"
},
{
"type": "Second plan",
"price": "85",
"details": "per month (Annual Plan)"
}
],
```
{% endcode %}
### Product review scores
| Data field | Description | Data Type |
| --------------------------------- | -------------------------------- | -------------- |
| `product_reviews_count` | Total number of product reviews | Integer (long) |
| `product_reviews_aggregate_score` | Average score of product reviews | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Product review fluctuations" %}
```json
"product_reviews_count": 74,
"product_reviews_aggregate_score": 4.513513513513513,
```
{% endcode %}
| Data field | Description | Data type |
| -------------------------------- | ------------------------------------------------------------------------------------ | -------------------------- |
| `product_reviews_score_by_month` |
Product review scores by month. Counts available from 2021.04
| Array of objects (structs) |
| `product_reviews_score` | Product review score | Float (double) |
| `date` | Record date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Review score" %}
```json
"product_reviews_score_by_month": [
{
"product_reviews_score": 4.4,
"date": "2019-11"
},
{
"product_reviews_score": 4.6,
"date": "2021-01"
}
],
```
{% endcode %}
### Product review score distribution
| Data field | Description | Data Type |
| ------------------------------------ | ------------------------------------- | --------------- |
| `product_reviews_score_distribution` | Distribution of product review scores | Object (struct) |
| `score_1` | Number of 1-star reviews | Integer (long) |
| `score_2` | Number of 2-star reviews | Integer (long) |
| `score_3` | Number of 3-star reviews | Integer (long) |
| `score_4` | Number of 4-star reviews | Integer (long) |
| `score_5` | Number of 5-star reviews | Integer (long) |
**See a snippet of the dataset for reference:**
{% code title="Product review score distribution" %}
```json
"product_reviews_score_distribution": {
"score_1": 0,
"score_2": 0,
"score_3": 4,
"score_4": 28,
"score_5": 42
},
```
{% endcode %}
### Product review score changes
| Data field | Description | Data type |
| ------------------------------ | ---------------------------------------------------------- | --------------- |
| `product_reviews_score_change` | Changes in the product review score over different periods | Object (struct) |
| `current` | Current product review score | Float (double) |
| `change_monthly` | Monthly change in product review score | Float (double) |
| `change_quarterly` | Quarterly change in product review score | Float (double) |
| `change_yearly` | Yearly change in product review score | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Product review fluctuations" %}
```json
"product_reviews_score_change": {
"current": 4.3,
"change_monthly": 0.0,
"change_quarterly": 0.0,
"change_yearly": 0.0
},
```
{% endcode %}
## Financials
### Annual revenue range
| Data field | Description | Data type |
| ------------------------------------------------------------------------------------------------------- | ----------------------------------------------------- | --------------- |
| `revenue_annual_range` | Annual revenue range information from various sources | Object (struct) |
|
| Company's acquisition information from various sources | Array of objects (struct) |
| `acquiree_name` | Acquired company name | String |
| `announced_date` | Date when the acquisition was announced in `YYYY-MM-DD` format | String (date) |
| `price` | Acquisition price | Integer |
| `currency` | Acquisition price currency | String |
**See a snippet of the dataset for reference:**
{% code title="Acquisitions" %}
```json
"num_acquisitions_source_1": 2,
"acquisition_list_source_1": [
{
"acquiree_name": "First Acquiree",
"announced_date": "2019-12-10",
"price": 350000000,
"currency": "USD"
},
{
"acquiree_name": "Second Acquiree",
"announced_date": "2020-01-27",
"price": 350000000,
"currency": "USD"
}
],
"num_acquisitions_source_2": 2,
"acquisition_list_source_2": [
{
"acquiree_name": "First Acquiree",
"announced_date": "2020-01-27",
"price": 350000000,
"currency": "USD"
},
{
"acquiree_name": "Second Acquiree",
"announced_date": "2019-12-10",
"price": 350000000,
"currency": "USD"
}
],
```
{% endcode %}
## News features
| Data field | Description | Data type |
| ------------------- | --------------------------------------------------------- | -------------------------- |
| `num_news_articles` | Number of news articles that mention the record company | Integer |
| `news_articles` | Details about the news articles featuring company updates | Array of objects (structs) |
| `headline` | News article headline | String |
| `published_date` | Date the article was published in `YYYY-MM-DD` format | String (date) |
| `summary` | News article summary | String |
| `article_url` | Full news article URL | String |
| `source` | Source of the news | String |
**See a snippet of the dataset for reference:**
{% code title="Media mentions" %}
```json
"num_news_articles": 1,
"news_articles": [
{
"headline": "Example Company Layoffs Hit Channel and Sales Team",
"published_date": "2024-01-08",
"summary": "Professional networking sites such as have seen the influx of Example Company employees posting about getting layoff notices in the past week. The cuts have come following the acquisition by contact-center-as-a-service (CCaaS) giant NICE.",
"article_url": "https://www.channelfutures.com/unified-communications/voicestream-technologies-inc-layoffs-hit-channel-and-sales-team",
"source": "News source example"
}
],
```
{% endcode %}
## Technographics
| Data field | Description | Data type |
| ----------------------- | ------------------------------------------------------ | ---------------- |
| `num_technologies_used` | Number of technologies used by the company | Integer |
| `technologies_used` | List of technologies used by the company | Array of strings |
| `technology` | Technology name | String |
| `first_verified_at` | Date this technology was first assigned to the company | String (date) |
| `last_verified_at` | Date this technology was last assigned to the company | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Technographics" %}
```json
"num_technologies_used": 40,
"technologies_used": [
{
"technology": "React",
"first_verified_at": "2022-03-15",
"last_verified_at": "2024-10-15"
}
]
```
{% endcode %}
## Company websites and social media
| Data field | Description | Data type |
| ----------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `website` | Website URL | String |
| `website_domain` | Normalized domain output (e.g., `example-company.com`) | String |
| `website_alias` |
All possible company website variations (collected from our firmographic sources)
| String |
| `professional_network_url` | Professional network URL where the company was first discovered. It can be outdated if the company has changed its profile | String |
| `professional_network_shorthand_name` | Shorthand name of the company's Professional network URL | String |
| `canonical_professional_network_url` | The current official Professional network URL for the company, reflecting the most recent updates | String |
| `canonical_professional_network_shorthand_name` | The current shorthand name of the company's Professional network URL | String |
| `twitter_url` | Twitter profile URL | Array of strings |
| `discord_url` | Discord server URL | Array of strings |
| `facebook_url` | Facebook page URL | Array of strings |
| `instagram_url` | Instagram profile URL | Array of strings |
| `pinterest_url` | Pinterest profile URL | Array of strings |
| `tiktok_url` | TikTok profile URL | Array of strings |
| `youtube_url` | YouTube channel URL | Array of strings |
| `github_url` | GitHub profile URL | Array of strings |
| `reddit_url` | Reddit profile URL | Array of strings |
| `financial_website_url` | Financial network profile URL | String |
**See a snippet of the dataset for reference:**
## Website traffic
### Web traffic and topics
| Data field | Description | Data type |
| -------------------------------- | ----------------------------------------------------------------- | ---------------- |
| `total_website_visits_monthly` | Monthly website visits | Integer (long) |
| `visits_change_monthly` | Monthly change in website visits, shown in percentage | Float (double) |
| `rank_global` | Global rank of the website | Integer |
| `rank_country` | Country-specific rank of the website | Integer |
| `rank_category` | Category-specific rank of the website | Integer |
| `bounce_rate` | Percentage of visitors who leave the site after visiting one page | Float (double) |
| `pages_per_visit` | Average number of pages viewed per visit | Float (double) |
| `average_visit_duration_seconds` | Average duration of a visit in seconds | Float (double) |
| `similarly_ranked_websites` | List of websites with similar rankings | Array of strings |
| `top_topics` | List of top topics associated with the website | Array of strings |
**See a snippet of the dataset for reference:**
{% code title="Web traffic and topics" %}
```json
"total_website_visits_monthly": 72600,
"visits_change_monthly": 14.12,
"rank_global": 573826,
"rank_country": 119057,
"rank_category": 2160,
"bounce_rate": 41.26,
"pages_per_visit": 5.5,
"average_visit_duration_seconds": 287.0,
"similarly_ranked_websites": [
"example-website.com",
"examplary-website.com"
],
"top_topics": [
"google",
"social network",
"social",
"social media",
"google apps"
],
```
{% endcode %}
| Data field | Description | Data type |
| ----------------------------- | -------------------------------------------------------------------- | --------------- |
| `total_website_visits_change` | Changes in the total number of website visits over different periods | Object (struct) |
| `current` | Current number of total website visits | Integer (long) |
| `change_monthly` | Monthly change in total website visits | Integer (long) |
| `change_monthly_percentage` | Monthly percentage change in total website visits | Float (double) |
| `change_quarterly` | Quarterly change in total website visits | Integer (long) |
| `change_quarterly_percentage` | Quarterly percentage change in total website visits | Float (double) |
| `change_yearly` | Yearly change in total website visits | Integer (long) |
| `change_yearly_percentage` | Yearly percentage change in total website visits | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Web traffic and topics" %}
```json
"total_website_visits_change": {
"current": 15432,
"change_monthly": 89,
"change_monthly_percentage": 0.576321854392679,
"change_quarterly": 1043,
"change_quarterly_percentage": 6.781293846102947,
"change_yearly": 1983,
"change_yearly_percentage": 13.425986213489573
}
```
{% endcode %}
| Data field | Description | Data type |
| ------------------------------- | ---------------------------------------------------------------------------- | -------------------------- |
| `total_website_visits_by_month` |
Website visits by month. Counts available from 202404
| Array of objects (structs) |
| `total_website_visits` | Website visits | Float (double) |
| `date` | Record date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Website visits" %}
```json
"total_website_visits_by_month": [
{
"total_website_visits": 60,
"date": "2019-11"
},
{
"total_website_visits": 75,
"date": "2021-01"
}
],
```
{% endcode %}
### Visits by country
| Data field | Description | Data type |
| ----------------------------- | ------------------------------------------------------- | -------------------------- |
| `visits_breakdown_by_country` | Breakdown of website visits by country | Array of objects (structs) |
| `country` | Visitor's country | String |
| `percentage` | Percentage of visits from one country | Float (double) |
| `percentage_monthly_change` | Monthly change in percentage of visits from one country | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Visits by country" %}
```json
"visits_breakdown_by_country": [
{
"country": "United States",
"percentage": 74.9,
"percentage_monthly_change": 31.74
}
],
```
{% endcode %}
### Visits by gender
| Data field | Description | Data type |
| ---------------------------- | ------------------------------------- | --------------- |
| `visits_breakdown_by_gender` | Breakdown of website visits by gender | Object (struct) |
| `male_percentage` | Percentage of visits by males | Float (double) |
| `female_percentage` | Percentage of visits by females | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Visits by gender" %}
```json
"visits_breakdown_by_gender": {
"male_percentage": 64.04,
"female_percentage": 35.96
},
```
{% endcode %}
### Visits by age
| Data field | Description | Data type |
| ------------------------- | ----------------------------------------------- | --------------- |
| `visits_breakdown_by_age` | Breakdown of website visits by age group | Object (struct) |
| `age_18_24_percentage` | Percentage of visits by users aged 18-24 | Float |
| `age_25_34_percentage` | Percentage of visits by users aged 25-34 | Float |
| `age_35_44_percentage` | Percentage of visits by users aged 35-44 | Float |
| `age_45_54_percentage` | Percentage of visits by users aged 45-54 | Float |
| `age_55_64_percentage` | Percentage of visits by users aged 55-64 | Float |
| `age_65_plus_percentage` | Percentage of visits by users aged 65 and above | Float |
**See a snippet of the dataset for reference:**
{% code title="Visits by age" %}
```json
"visits_breakdown_by_age": {
"age_18_24_percentage": 22.92,
"age_25_34_percentage": 32.22,
"age_35_44_percentage": 15.47,
"age_45_54_percentage": 13.31,
"age_55_64_percentage": 10.3,
"age_65_plus_percentage": 5.78
},
```
{% endcode %}
## Employee review scores & changes
### Review count
| Data field | Description | Data type |
| ------------------------------------------ | --------------------------------- | -------------- |
| `company_employee_reviews_count` | Total number of employee reviews | Integer (long) |
| `company_employee_reviews_aggregate_score` | Average score of employee reviews | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Review count" %}
```json
"company_employee_reviews_count": 145,
"company_employee_reviews_aggregate_score": 4.1,
```
{% endcode %}
### Review score breakdown
| Data field | Description | Data type |
| ---------------------------------- | ------------------------------------------------ | --------------- |
| `employee_reviews_score_breakdown` | Breakdown of employee review ratings by category | Object (struct) |
| `business_outlook` | Business outlook rating | Float (double) |
| `career_opportunities` | Career opportunities rating | Float (double) |
| `ceo_approval` | CEO approval rating | Float (double) |
| `compensation_benefits` | Compensation and benefits rating | Float (double) |
| `culture_values` | Culture and values rating | Float (double) |
| `diversity_inclusion` | Diversity and inclusion rating | Float (double) |
| `recommend` | Recommendation rating | Float (double) |
| `senior_management` | Senior management rating | Float (double) |
| `work_life_balance` | Work-life balance rating | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Review score breakdown" %}
```json
"employee_reviews_score_breakdown": {
"business_outlook": 0.55,
"career_opportunities": 3.6,
"ceo_approval": 0.57,
"compensation_benefits": 4.2,
"culture_values": 4.1,
"diversity_inclusion": 3.7,
"recommend": 0.76,
"senior_management": 3.4,
"work_life_balance": 4.2
},
```
{% endcode %}
### Review score distribution
| Data field | Description | Data type |
| ------------------------------------- | ------------------------------------------- | --------------- |
| `employee_reviews_score_distribution` | Distribution of star ratings in the reviews | Object (struct) |
| `score_1` | Number of 1-star reviews | Integer (long) |
| `score_2` | Number of 2-star reviews | Integer (long) |
| `score_3` | Number of 3-star reviews | Integer (long) |
| `score_4` | Number of 4-star reviews | Integer (long) |
| `score_5` | Number of 5-star reviews | Integer (long) |
**See a snippet of the dataset for reference:**
{% code title="Review rating distribution" %}
```json
"employee_reviews_score_distribution": {
"score_1": 2,
"score_2": 7,
"score_3": 9,
"score_4": 14,
"score_5": 28
},
```
{% endcode %}
### Total rating change
| Data field | Description | Data type |
| ------------------------------------------ | --------------------------------------------------------------------------------- | --------------- |
| `employee_reviews_score_aggregated_change` | Changes in the aggregated rating score of employee reviews over different periods | Object (struct) |
| `current` | Current aggregated score of employee reviews | Float (double) |
| `change_monthly` | Monthly change in the aggregated score | Float (double) |
| `change_quarterly` | Quarterly change in the aggregated score | Float (double) |
| `change_yearly` | Yearly change in the aggregated score | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Total rating change" %}
```json
"employee_reviews_score_aggregated_change": {
"current": 4.3,
"change_monthly": 0.05,
"change_quarterly": 0.1,
"change_yearly": -0.2
}
```
{% endcode %}
| Data field | Description | Data type |
| -------------------------------------------- | ------------------------------------------------------------------------------------- | -------------------------- |
| `employee_reviews_score_aggregated_by_month` |
Aggregated review score by month Counts available from 2022.06
| Array of objects (structs) |
| `aggregated_score` | Aggregated score | Float (double) |
| `date` | Record date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Aggregated reviews by month" %}
```json
"employee_reviews_score_aggregated_by_month": [
{
"aggregated_score": 3.4,
"date": "2023-03"
},
{
"aggregated_score": 3.7,
"date": "2024-07"
}
],
```
{% endcode %}
### Rating change in the business outlook category
| Data field | Description | Data type |
| ------------------------------------------------ | ---------------------------------------------- | --------------- |
| `employee_reviews_score_business_outlook_change` | Changes in the business outlook rating score | Object (struct) |
| `current` | Current business outlook score | Float (double) |
| `change_monthly` | Monthly change in the business outlook score | Float (double) |
| `change_quarterly` | Quarterly change in the business outlook score | Float (double) |
| `change_yearly` | Yearly change in the business outlook score | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Rating change in the business outlook category" %}
```json
"employee_reviews_score_business_outlook_change": {
"current": 0.45,
"change_monthly": 0.02,
"change_quarterly": -0.05,
"change_yearly": 0.3
}
```
{% endcode %}
| Data field | Description | Data type |
| -------------------------------------------------- | ------------------------------------------------------------------------------------- | -------------------------- |
| `employee_reviews_score_business_outlook_by_month` |
Business outlook score by month. Counts available from 2022.06
| Array of objects (structs) |
| `business_outlook_score` | Business outlook score | Float (double) |
| `date` | Record date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Business outlook score by month" %}
```json
"employee_reviews_score_business_outlook_by_month": [
{
"business_outlook_score": 49.0,
"date": "2025-01"
},
{
"business_outlook_score": 49.0,
"date": "2024-09"
}
],
```
{% endcode %}
### Rating change in the career opportunities category
| Data field | Description | Data type |
| ---------------------------------------------------- | ---------------------------------------------------------------------- | --------------- |
| `employee_reviews_score_career_opportunities_change` | Changes in the career opportunities rating score from employee reviews | Object (struct) |
| `current` | Current career opportunities score | Float (double) |
| `change_monthly` | Monthly change in the career opportunities score | Float (double) |
| `change_quarterly` | Quarterly change in the career opportunities score | Float (double) |
| `change_yearly` | Yearly change in the career opportunities score | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Rating change in the career opportunities category" %}
```json
"employee_reviews_score_career_opportunities_change": {
"current": 3.8,
"change_monthly": -0.1,
"change_quarterly": -0.15,
"change_yearly": -0.3
}
```
{% endcode %}
| Data field | Description | Data type |
| ------------------------------------------------------ | ----------------------------------------------------------------------------------------- | -------------------------- |
| `employee_reviews_score_career_opportunities_by_month` |
Career opportunities score by month. Counts available from 2022.06
| Array of objects (structs) |
| `career_opportunities_score` | Business outlook score | Float (double) |
| `date` | Record date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Career opportunities score by month" %}
```json
"employee_reviews_score_career_opportunities_by_month": [
{
"career_opportunities_score": 3.6,
"date": "2024-10"
},
{
"career_opportunities_score": 3.6,
"date": "2023-06"
}
],
```
{% endcode %}
### Rating change in the CEO approval category
| Data field | Description | Data type |
| -------------------------------------------- | -------------------------------------------------------------- | --------------- |
| `employee_reviews_score_ceo_approval_change` | Changes in the CEO approval rating score from employee reviews | Object (struct) |
| `current` | Current approval score of the CEO | Float (double) |
| `change_monthly` | Monthly change in the CEO approval score | Float (double) |
| `change_quarterly` | Quarterly change in the CEO approval score | Float (double) |
| `change_yearly` | Yearly change in the CEO approval score | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Rating change in the CEO approval category" %}
```json
"employee_reviews_score_ceo_approval_change": {
"current": 0.58,
"change_monthly": -0.03,
"change_quarterly": -0.05,
"change_yearly": -45.12
}
```
{% endcode %}
| Data field | Description | Data type |
| ---------------------------------------------- | --------------------------------------------------------------------------------- | -------------------------- |
| `employee_reviews_score_ceo_approval_by_month` |
CEO approval score by month. Counts available from 2022.06
| Array of objects (structs) |
| `ceo_approval_score` | CEO approval score | Float (double) |
| `date` | Record date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="CEO approval score by month" %}
```json
"employee_reviews_score_ceo_approval_by_month": [
{
"ceo_approval_score": 4.0,
"date": "2023-03"
},
{
"ceo_approval_score": 3.0,
"date": "2022-08"
}
],
```
{% endcode %}
### Rating change in the compensation and benefits category
| Data field | Description | Data type |
| ----------------------------------------------------- | --------------------------------------------------------------------------- | --------------- |
| `employee_reviews_score_compensation_benefits_change` | Changes in the compensation and benefits rating score from employee reviews | Object (struct) |
| `current` | Current compensation and benefits score | Float (double) |
| `change_monthly` | Monthly change in the compensation and benefits score | Float (double) |
| `change_quarterly` | Quarterly change in the compensation and benefits score | Float (double) |
| `change_yearly` | Yearly change in the compensation and benefits score | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Rating change in the compensation and benefits category" %}
```json
"employee_reviews_score_compensation_benefits_change": {
"current": 4.3,
"change_monthly": 0.05,
"change_quarterly": 0.07,
"change_yearly": -0.08
}
```
{% endcode %}
| Data field | Description | Data type |
| ------------------------------------------------------- | ---------------------------------------------------------------------------------------------- | -------------------------- |
| `employee_reviews_score_compensation_benefits_by_month` |
Compensation and benefits score by month. Counts available from 2022.06
| Array of objects (structs) |
| `compensation_benefits_score` | Compensation and benefits score | Float (double) |
| `date` | Record date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Comensation benefits by month" %}
```json
"employee_reviews_score_compensation_benefits_by_month": [
{
"compensation_benefits_score": 3.6,
"date": "2025-02"
},
{
"compensation_benefits_score": 3.6,
"date": "2024-12"
}
],
```
{% endcode %}
### Rating change in the culture and values category
| Data field | Description | Data type |
| ------------------------------------------------ | --------------------------------------------------------------------------------------- | -------------------------- |
| `employee_reviews_score_culture_values_by_month` |
Culture and values score by month. Counts available from 2022.06
| Array of objects (structs) |
| `culture_values_score` | Culture and values score | Float (double) |
| `date` | Record date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Culture and values score by month" %}
```json
"employee_reviews_score_culture_values_by_month": [
{
"culture_values_score": 3.9,
"date": "2025-03"
},
{
"culture_values_score": 3.9,
"date": "2024-11"
}
],
```
{% endcode %}
| Data field | Description | Data type |
| ---------------------------------------------- | -------------------------------------------------------------------- | --------------- |
| `employee_reviews_score_culture_values_change` | Changes in the culture and values rating score from employee reviews | Object (struct) |
| `current` | Current culture and values score | Float (double) |
| `change_monthly` | Monthly change in the culture and values score | Float (double) |
| `change_quarterly` | Quarterly change in the culture and values score | Float (double) |
| `change_yearly` | Yearly change in the culture and values score | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Rating change in the culture and values category" %}
```json
"employee_reviews_score_culture_values_change": {
"current": 4.1,
"change_monthly": 0.1,
"change_quarterly": 0.2,
"change_yearly": -0.3
}
```
{% endcode %}
### Rating change in the diversity and inclusion category
| Data field | Description | Data type |
| --------------------------------------------------- | ------------------------------------------------------------------------- | --------------- |
| `employee_reviews_score_diversity_inclusion_change` | Changes in the diversity and inclusion rating score from employee reviews | Object (struct) |
| `current` | Current diversity and inclusion score | Float (double) |
| `change_monthly` | Monthly change in the diversity and inclusion score | Float (double) |
| `change_quarterly` | Quarterly change in the diversity and inclusion score | Float (double) |
| `change_yearly` | Yearly change in the diversity and inclusion score | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Rating change in the diversity and inclusion category" %}
```json
"employee_reviews_score_diversity_inclusion_change": {
"current": 3.7,
"change_monthly": -0.1,
"change_quarterly": -0.2,
"change_yearly": -0.3
}
```
{% endcode %}
| Data field | Description | Data type |
| ----------------------------------------------------- | -------------------------------------------------------------------------------------------- | -------------------------- |
| `employee_reviews_score_diversity_inclusion_by_month` |
Diversity and inclusion score by month. Counts available from 2022.06
| Array of objects (structs) |
| `diversity_inclusion_score` | Diversity and inclusion score | Float (double) |
| `date` | Record date | String (date) |
{% code title="Diversity and inclusion score by month" %}
```json
"employee_reviews_score_diversity_inclusion_by_month": [
{
"diversity_inclusion_score": 4.5,
"date": "2025-03"
},
{
"diversity_inclusion_score": 4.5,
"date": "2024-11"
}
],
```
{% endcode %}
### Rating change in the recommendations category
| Data field | Description | Data type |
| ----------------------------------------- | ---------------------------------------------------------------- | --------------- |
| `employee_reviews_score_recommend_change` | Changes in the recommendation rating score from employee reviews | Object (struct) |
| `current` | Current recommendation score | Float (double) |
| `change_monthly` | Monthly change in the recommendation score | Float (double) |
| `change_quarterly` | Quarterly change in the recommendation score | Float (double) |
| `change_yearly` | Yearly change in the recommendation score | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Rating change in the recommendations category" %}
```json
"employee_reviews_score_recommend_change": {
"current": 0.76,
"change_monthly": -0.12,
"change_quarterly": -0.12,
"change_yearly": -0.24
}
```
{% endcode %}
| Data field | Description | Data type |
| ------------------------------------------- | -------------------------------------------------------------------------------------------- | -------------------------- |
| `employee_reviews_score_recommend_by_month` |
Likelihood to recommend score by month. Counts available from 2022.06
| Array of objects (structs) |
| `recommend_score` | Likelihood to recommend score | Float (double) |
| `date` | Record date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Recommendation score by month" %}
```json
"employee_reviews_score_recommend_by_month": [
{
"recommend_score": 0.54,
"date": "2024-07"
},
{
"recommend_score": 0.54,
"date": "2024-06"
}
],
```
{% endcode %}
### Rating change in the senior management category
| Data field | Description | Data type |
| ------------------------------------------------- | ------------------------------------------------------------------- | --------------- |
| `employee_reviews_score_senior_management_change` | Changes in the senior management rating score from employee reviews | Object (struct) |
| `current` | Current senior management score | Float (double) |
| `change_monthly` | Monthly change in the senior management score | Float (double) |
| `change_quarterly` | Quarterly change in the senior management score | Float (double) |
| `change_yearly` | Yearly change in the senior management score | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Rating change in the senior management category" %}
```json
"employee_reviews_score_senior_management_change": {
"current": 3.4,
"change_monthly": -0.1,
"change_quarterly": -0.1,
"change_yearly": -0.3
}
```
{% endcode %}
| Data field | Description | Data type |
| --------------------------------------------------- | -------------------------------------------------------------------------------------- | -------------------------- |
| `employee_reviews_score_senior_management_by_month` |
Senior management score by month. Counts available from 2022.06
| Array of objects (structs) |
| `senior_management_score` | Senior management score | Float (double) |
| `date` | Record date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Senior management score by month" %}
```json
"employee_reviews_score_senior_management_by_month": [
{
"senior_management_score": 3.9,
"date": "2025-03"
},
{
"senior_management_score": 3.9,
"date": "2024-11"
}
],
```
{% endcode %}
### Rating change in the work and life balance category
| Data field | Description | Data type |
| ------------------------------------------------- | ------------------------------------------------------------------- | --------------- |
| `employee_reviews_score_work_life_balance_change` | Changes in the work-life balance rating score from employee reviews | Object (struct) |
| `current` | Current work-life balance score | Float (double) |
| `change_monthly` | Monthly change in the work-life balance score | Float (double) |
| `change_quarterly` | Quarterly change in the work-life balance score | Float (double) |
| `change_yearly` | Yearly change in the work-life balance score | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Rating change in the work and life balance category" %}
```json
"employee_reviews_score_work_life_balance_change": {
"current": 4.2,
"change_monthly": 0.0,
"change_quarterly": 0.0,
"change_yearly": -0.1
}
```
{% endcode %}
| Data field | Description | Data type |
| --------------------------------------------------- | -------------------------------------------------------------------------------------- | -------------------------- |
| `employee_reviews_score_work_life_balance_by_month` |
Work-life balance score by month. Counts available from 2022.06
| Array of objects (structs) |
| `work_life_balance_score` | Work-life balance score | Float (double) |
| `date` | Record date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Work-life balance score by month" %}
```json
"employee_reviews_score_work_life_balance_by_month": [
{
"work_life_balance_score": 3.8,
"date": "2025-01"
},
{
"work_life_balance_score": 3.8,
"date": "2024-09"
}
],
```
{% endcode %}
## Workforce trends
### Key executives
| Data field | Description | Data type |
| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------- |
| `key_executives` |
List of key executives. Key executives are considered people who match any of the following management levels based on their position titles: Director, Head, President/Vice President, C-level, Partner, Founder, Owner
| Array of objects (structs) |
| `parent_id` | Executive's identifier | String |
| `member_full_name` | Executive's name | String |
| `member_position_title` | Executive's job title | String |
**See a snippet of the dataset for reference:**
{% code title="Key executives" %}
```json
"key_executives": [
{
"parent_id": 86953887,
"member_full_name": "John Doe",
"member_position_title": "Partner"
}
],
```
{% endcode %}
### Key employee change events
| Data field | Description | Data type |
| ---------------------------- | ---------------------------------------------------------------- | -------------------------- |
| `key_employee_change_events` | List of key employee change events and corresponding information | Array of objects (structs) |
| `employee_change_event_name` | Employee change event | String |
| `employee_change_event_date` | Employee change event date in `YYYY-MM-DD` format | String (date) |
| `employee_change_event_url` | Event article URL | String |
**See a snippet of the dataset for reference:**
{% code title="Key employee change events" %}
```json
"key_employee_change_events": [
{
"employee_change_event_name": "Example Company Appoints John Doe as Chief Investment Officer",
"employee_change_event_date": "2024-01-18",
"employee_change_event_url" : "https://www.vcaonline.com/news/2024011822/voicestream-technologies-appoints-john-doe-as-chief-investment-officer/"
}
],
```
{% endcode %}
### Key executive arrivals
| Data field | Description | Data type |
| ------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `key_executive_arrivals` |
List of new executives in the company. Executives are considered employees that have a decision\_maker = true flag
| Array of strings |
| `parent_id` | Employee identifier | Integer (long) |
| `member_full_name` | Full name | String |
| `member_position_title` | Position title | String |
| `arrival_date` | Start date in the position | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Key employee arrivals" %}
```json
"key_executive_arrivals": [
{
"parent_id": 423235614,
"member_full_name": "John Doe",
"member_position_title": "Partner",
"arrival_date": "Apr 2024"
},
{
"parent_id": 2241368,
"member_full_name": "Marry Moe",
"member_position_title": "Partner",
"arrival_date": "May 2024"
}
],
```
{% endcode %}
### Key executive departures
| Data field | Description | Data type |
| -------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `key_executive_departures` |
List of former executives in the company. Executives are considered employees that have a decision\_maker = true flag
| Array of strings |
| `parent_id` | Employee identifier | Integer (long) |
| `member_full_name` | Full name | String |
| `member_position_title` | Position title | String |
| `departure_date` | Employment end date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Key employee departures" %}
```json
"key_executive_departures": [
{
"parent_id": 692515608,
"member_full_name": "John Doe",
"member_position_title": "Partner",
"departure_date": "May 2024"
},
{
"parent_id": 83299323,
"member_full_name": "Danny Doe",
"member_position_title": "Partner",
"departure_date": "Aug 2024"
}
],
```
{% endcode %}
### Top companies
| Data field | Description | Data type |
| ------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `top_previous_companies` | Top ten companies that likely were former workplaces for the current workforce. `Likely to switch` counts are based on `member_experience` data | Array of objects |
| `company_id` | Company identification key | Integer (long) |
| `company_name` | Company name | String |
| `count` | Count to identify the number of transitions | Integer (long) |
| `top_next_companies` |
Top ten companies, people will likely switch after their current job. Likely to switch counts are based on member\_experience data
| Array of objects |
| `company_id` | Company identification key | Integer (long) |
| `company_name` | Company name | String |
| `count` | Count to identify the number of transitions | Integer (long) |
**See a snippet of the dataset for reference:**
{% code title="Top companies" %}
```json
"top_previous_companies": [
{
"company_id": 110,
"company_name": "Example Company",
"count": 5
}
],
"top_next_companies": [
{
"company_id": 110,
"company_name": "Example Sister Company",
"count": 3
}
]
```
{% endcode %}
### Employee count by department
| Data field | Description | Data type |
| ----------------------------------------- | ------------------------------------------------ | --------------- |
| `employees_count_breakdown_by_department` | Breakdown of employee count by department | Object (struct) |
| `employees_count_medical` | Number of employees in the medical department | Integer (long) |
| `employees_count_sales` | Number of employees in the sales department | Integer (long) |
| `employees_count_hr` | Number of employees in the HR department | Integer (long) |
| `employees_count_legal` | Number of employees in the legal department | Integer (long) |
| `employees_count_marketing` | Number of employees in the marketing department | Integer (long) |
| `employees_count_finance` | Number of employees in the finance department | Integer (long) |
| `employees_count_tech` | Number of employees in the tech department | Integer (long) |
| `employees_count_consulting` | Number of employees in the consulting department | Integer (long) |
| `employees_count_operations` | Number of employees in the operations department | Integer (long) |
| `employees_count_other_department` | Number of employees in other departments | Integer (long) |
| `employees_count_product` | Number of employees in the product department | Integer (long) |
**See a snippet of the dataset for reference:**
{% code title="Employee count by department" %}
```json
"employees_count_breakdown_by_department": {
"employees_count_medical": 0,
"employees_count_sales": 24,
"employees_count_hr": 8,
"employees_count_legal": 2,
"employees_count_marketing": 5,
"employees_count_finance": 7,
"employees_count_tech": 63,
"employees_count_consulting": 2,
"employees_count_operations": 2,
"employees_count_other_department": 99,
"employees_count_product": 12
},
```
{% endcode %}
| Data field | Description | Data type |
| -------------------------------------------------- | ----------------------------------------------------------------- | -------------- |
| `employees_count_breakdown_by_department_by_month` | Employee count changes by month and department | Object |
| `employees_count_medical` | Employee count in the medical department | Integer (long) |
| `employees_count_sales` | Employee count in the sales department | Integer (long) |
| `employees_count_hr` | Employee count in the HR department | Integer (long) |
| `employees_count_legal` | Employee count in the legal department | Integer (long) |
| `employees_count_marketing` | Employee count in the marketing department | Integer (long) |
| `employees_count_finance` | Employee count in the finance department | Integer (long) |
| `employees_count_technical` | Employee count in the technical department | Integer (long) |
| `employees_count_consulting` | Employee count in the consulting department | Integer (long) |
| `employees_count_operations` | Employee count in the operations department | Integer (long) |
| `employees_count_product` | Employee count in the product department | Integer (long) |
| `employees_count_general_management` | Employee count in the general management department | Integer (long) |
| `employees_count_administrative` | Employee count in the administrative department | Integer (long) |
| `employees_count_customer_service` | Employee count in the customer service department | Integer (long) |
| `employees_count_project_management` | Employee count in the project management department | Integer (long) |
| `employees_count_design` | Employee count in the design department | Integer (long) |
| `employees_count_research` | Employee count in the research department | Integer (long) |
| `employees_count_trades` | Employee count in the trades department | Integer (long) |
| `employees_count_real_estate` | Employee count in the real estate department | Integer (long) |
| `employees_count_education` | Employee count in the education department | Integer (long) |
| `employees_count_other_department` | Employee count in the other departments | Integer (long) |
| `date` |
Record date. Counts available from 2020.01
| String (date) |
**See a snippet of the dataset for reference:**
{% code title="Employee count by department" %}
```json
"employees_count_breakdown_by_department_by_month": [
{
"employees_count_breakdown_by_department": {
"employees_count_sales": 0,
"employees_count_hr": 0,
"employees_count_legal": 45,
"employees_count_marketing": 4,
"employees_count_finance": 0,
"employees_count_technical": 0,
"employees_count_consulting": 0,
"employees_count_operations": 55,
"employees_count_product": 0,
"employees_count_general_management": 0,
"employees_count_administrative": 0,
"employees_count_customer_service": 0,
"employees_count_project_management": 34,
"employees_count_design": 0,
"employees_count_research": 0,
"employees_count_trades": 56,
"employees_count_real_estate": 0,
"employees_count_education": 0,
"employees_count_other_department": 0,
"employees_count_other_department": 5
},
"date": "2021-01"
}
]
```
{% endcode %}
| Data field | Description | Data type |
| ---------------------------- | ------------------------- | ---------------- |
| `employees_count_by_country` | Employee count by country | Array of objects |
| `country` | Country | String |
| `employee_count` | Employee count | Integer |
**See a snippet of the dataset for reference:**
{% code title="Employee count by country" %}
```json
"employees_count_by_country": [
{
"country": "Germany",
"employee_count": 1
},
{
"country": "France",
"employee_count": 1
}
],
```
{% endcode %}
| Data field | Description | Data type |
| ------------------------------------- | ----------------------------------------------------------------- | -------------------------- |
| `employees_count_by_country_by_month` | Employee count by country and month | Array of objects (structs) |
| `employees_count_by_country` | Employee count by country | Object (struct) |
| `country` | Country | String |
| `employee_count` | Employee count | Integer (long) |
| `date` |
Record date. Counts available from 2020.01
| String (date) |
**See a snippet of the dataset for reference:**
{% code title="Employee count by country" %}
```json
"employees_count_by_country_by_month": [
{
"employees_count_by_country": [
{
"country": "United States",
"employee_count": 43
}
],
"date": "2021-01"
}
]
```
{% endcode %}
| Data field | Description | Data type |
| --------------------------------------- | ------------------------------------------- | --------- |
| `employees_count_breakdown_by_region` | Employee count breakdown by region | Object |
| `employees_count_eastern_europe` | Employee count in Eastern Europe | Integer |
| `employees_count_latin_america` | Employee count in Latin America | Integer |
| `employees_count_southern_europe` | Employee count in Southern Europe | Integer |
| `employees_count_sub_saharan_africa` | Employee count in Sub-Saharan Africa | Integer |
| `employees_count_central_asia` | Employee count in Central Asia | Integer |
| `employees_count_northern_america` | Employee count in Northern America | Integer |
| `employees_count_australia_new_zealand` | Employee count in Australia and New Zealand | Integer |
| `employees_count_northern_europe` | Employee count in Northern Europe | Integer |
| `employees_count_south_eastern_asia` | Employee count in Southeast Asia | Integer |
| `employees_count_polynesia` | Employee count in Polynesia | Integer |
| `employees_count_southern_asia` | Employee count in Southern Asia | Integer |
| `employees_count_northern_africa` | Employee count in Northern Africa | Integer |
| `employees_count_melanesia` | Employee count in Melanesia | Integer |
| `employees_count_western_europe` | Employee count in Western Europe | Integer |
| `employees_count_western_asia` | Employee count in Western Asia | Integer |
| `employees_count_eastern_asia` | Employee count in Eastern Asia | Integer |
| `employees_count_micronesia` | Employee count in Micronesia | Integer |
| `employees_count_unknown` | Employee count in unassigned region | Integer |
**See a snippet of the dataset for reference:**
{% code title="Employee count by department" %}
```json
"employees_count_breakdown_by_region": {
"employees_count_eastern_europe" : 4,
"employees_count_latin_america": 5,
"employees_count_southern_europe": 6,
"employees_count_sub_saharan_africa": 0,
"employees_count_central_asia": 15,
"employees_count_northern_america": 0,
"employees_count_australia_new_zealand": 0,
"employees_count_northern_europe": 0,
"employees_count_south_eastern_asia": 0,
"employees_count_polynesia": 2,
"employees_count_southern_asia": 0,
"employees_count_northern_africa": 3,
"employees_count_melanesia": 0,
"employees_count_western_europe": 0,
"employees_count_western_asia": 0,
"employees_count_eastern_asia": 9,
"employees_count_micronesia": 0,
"employees_count_unknown": 0
}
```
{% endcode %}
| Data field | Description | Data type |
| ---------------------------------------------- | ----------------------------------------------------------------- | ---------------- |
| `employees_count_breakdown_by_region_by_month` | Employee count breakdown by region and date | Array of objects |
| `employees_count_breakdown_by_region` | Employee count breakdown by region | Object (struct) |
| `employees_count_eastern_europe` | Employee count in Eastern Europe | Integer |
| `employees_count_latin_america` | Employee count in Latin America | Integer |
| `employees_count_southern_europe` | Employee count in Southern Europe | Integer |
| `employees_count_sub_saharan_africa` | Employee count in Sub-Saharan Africa | Integer |
| `employees_count_central_asia` | Employee count in Central Asia | Integer |
| `employees_count_northern_america` | Employee count in Northern America | Integer |
| `employees_count_australia_new_zealand` | Employee count in Australia and New Zealand | Integer |
| `employees_count_northern_europe` | Employee count in Northern Europe | Integer |
| `employees_count_south_eastern_asia` | Employee count in Southeast Asia | Integer |
| `employees_count_polynesia` | Employee count in Polynesia | Integer |
| `employees_count_southern_asia` | Employee count in Southern Asia | Integer |
| `employees_count_northern_africa` | Employee count in Northern Africa | Integer |
| `employees_count_melanesia` | Employee count in Melanesia | Integer |
| `employees_count_western_europe` | Employee count in Western Europe | Integer |
| `employees_count_western_asia` | Employee count in Western Asia | Integer |
| `employees_count_eastern_asia` | Employee count in Eastern Asia | Integer |
| `employees_count_micronesia` | Employee count in Micronesia | Integer |
| `employees_count_unknown` | Employee count in unassigned region | Integer |
| `date` |
Record date. Counts available from 2020.01
| String (date) |
**See a snippet of the dataset for reference:**
{% code title="Employee count by department" %}
```json
"employees_count_breakdown_by_region_by_month": [
{
"employees_count_breakdown_by_region": {
"employees_count_eastern_europe": 3,
"employees_count_latin_america": 0,
"employees_count_southern_europe": 0,
"employees_count_sub_saharan_africa": 0,
"employees_count_central_asia": 0,
"employees_count_northern_america": 5,
"employees_count_australia_new_zealand": 0,
"employees_count_northern_europe": 7,
"employees_count_south_eastern_asia": 0,
"employees_count_polynesia": 0,
"employees_count_southern_asia": 0,
"employees_count_northern_africa": 0,
"employees_count_melanesia": 5,
"employees_count_western_europe": 0,
"employees_count_western_asia": 6,
"employees_count_eastern_asia": 0,
"employees_count_micronesia": 0,
"employees_count_unknown": 0
},
"date": "2021-01"
}
]
```
{% endcode %}
### Employee count by seniority
| Data field | Description | Data type |
| ---------------------------------------- | ------------------------------------------------------- | --------------- |
| `employees_count_breakdown_by_seniority` | Breakdown of employee count by seniority level | Object (struct) |
| `employees_count_owner` | Number of employees with the `Owner` job title | Integer (long) |
| `employees_count_founder` | Number of employees with the `Founder` job title | Integer (long) |
| `employees_count_clevel` | Number of C-level employees | Integer (long) |
| `employees_count_partner` | Number of employees with the `Partner` job title | Integer (long) |
| `employees_count_vp` | Number of employees with the `Vice President` job title | Integer (long) |
| `employees_count_head` | Number of employees with the `Head` job title | Integer (long) |
| `employees_count_director` | Number of employees with the `Director` job title | Integer (long) |
| `employees_count_manager` | Number of employees with the `Manager` job title | Integer (long) |
| `employees_count_senior` | Number of senior-level employees | Integer (long) |
| `employees_count_mid` | Number of mid-level employees | Integer (long) |
| `employees_count_junior` | Number of junior-level employees | Integer (long) |
| `employees_count_intern` | Number of interns | Integer (long) |
| `employees_count_other_management` | Number of employees in other management roles | Integer (long) |
**See a snippet of the dataset for reference:**
{% code title="Employee count by seniority" %}
```json
"employees_count_breakdown_by_seniority": {
"employees_count_owner": 0,
"employees_count_founder": 0,
"employees_count_clevel": 3,
"employees_count_partner": 1,
"employees_count_vp": 8,
"employees_count_head": 1,
"employees_count_director": 10,
"employees_count_manager": 31,
"employees_count_senior": 73,
"employees_count_mid": 8,
"employees_count_junior": 1,
"employees_count_intern": 0,
"employees_count_other_management": 88
},
```
{% endcode %}
| Data field | Description | Data type |
| ------------------------------------------------- | ----------------------------------------------------------------- | ---------------- |
| `employees_count_breakdown_by_seniority_by_month` | Employee count breakdown seniority and date | Array of objects |
| `employees_count_breakdown_by_seniority` | Employee count breakdown by seniority | Object (struct) |
| `employees_count_owner` | Number of owners in the company | Integer |
| `employees_count_founder` | Number of founders in the company | Integer |
| `employees_count_clevel` | Number of C-level employees in the company | Integer |
| `employees_count_partner` | Number of partners in the company | Integer |
| `employees_count_vp` | Number of vice presidents in the company | Integer |
| `employees_count_head` | Number of head-level employees in the company | Integer |
| `employees_count_director` | Number of directors in the company | Integer |
| `employees_count_manager` | Number of managers in the company | Integer |
| `employees_count_senior` | Number of seniors in the company | Integer |
| `employees_count_intern` | Number of interns in the company | Integer |
| `employees_count_specialist` | Number of specialists in the company | Integer |
| `employees_count_other_management` | Number of other management employees in the company | Integer |
| `date` |
Record date. Counts available from 2020.01
| Integer |
{% code title="Employee count by seniority" %}
```json
"employees_count_breakdown_by_seniority_by_month": [
{
"employees_count_breakdown_by_seniority": {
"employees_count_owner": 5,
"employees_count_founder": 3,
"employees_count_clevel": 7,
"employees_count_partner": 2,
"employees_count_vp": 6,
"employees_count_head": 4,
"employees_count_director": 10,
"employees_count_manager": 15,
"employees_count_senior": 20,
"employees_count_intern": 8,
"employees_count_specialist": 12,
"employees_count_other_management": 5
},
"date": "2023-09"
}
]
```
{% endcode %}
### Employee count changes
| Data field | Description | Data type |
| ----------------------------- | -------------------------------------------------------------- | --------------- |
| `employees_count_change` | Changes in the number of employees over different time periods | Object (struct) |
| `current` | Current number of employees | Integer (long) |
| `change_monthly` | Monthly change in employee count | Integer (long) |
| `change_monthly_percentage` | Monthly percentage change in employee count | Float (double) |
| `change_quarterly` | Quarterly change in employee count | Integer (long) |
| `change_quarterly_percentage` | Quarterly percentage change in employee count | Float (double) |
| `change_yearly` | Yearly change in employee count | Integer (long) |
| `change_yearly_percentage` | Yearly percentage change in employee count | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Employee count change" %}
```json
"employees_count_change": {
"current": 324,
"change_monthly": -26,
"change_monthly_percentage": -7.428571428571429,
"change_quarterly": -213,
"change_quarterly_percentage": -39.66480446927375,
"change_yearly": -244,
"change_yearly_percentage": -42.95774647887324
},
```
{% endcode %}
| Data field | Description | Data type |
| -------------------------- | ------------------------------------------------------------------------------------- | -------------------------- |
| `employees_count_by_month` |
Employee count changes by month. Counts available from 2019.01
| Array of objects (structs) |
| `employees_count` | Number of employees | Integer (long) |
| `date` | Record date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Employees count by month" %}
```json
"employees_count_by_month": [
{
"employees_count": 0,
"date": "2023-11"
},
{
"employees_count": 0,
"date": "2024-01"
}
],
```
{% endcode %}
### Active job postings
| Data field | Description | Data type |
| --------------------------- | --------------------------------------------------------- | ---------------- |
| `active_job_postings_count` | Number of active job postings associated with the company | Integer (long) |
| `active_job_postings` | Active job postings | Array of structs |
| `job_posting_id` | Professional network Job ID | Long |
| `job_posting_title` | Job title posted by the recruiter | String |
**See a snippet of the dataset for reference:**
{% code title="Active jobs" %}
```json
"active_job_postings_count": 1,
"active_job_postings": [
{
"job_posting_id": 1234567890,
"job_posting_title": "Product Manager",
}
]
```
{% endcode %}
| Data field | Description | Data type |
| ------------------------------------ | ----------------------------------------------------------------- | --------------- |
| `active_job_postings_count_by_month` | Active job postings by month | Object (struct) |
| `active_job_postings_count` | Job posting count | Integer (long) |
| `date` |
# Dictionary: Clean Company Data
Data dictionary for Clean Company Data – field-level reference covering fresh firmographics, funding, contact info, social media, and location fields.
Clean Company Data provides high-quality, structured business data ready for immediate use. Our data is meticulously cleaned and enriched, allowing organizations to streamline their workflows and confidently make data-driven decisions. By leveraging Clean Company Data, businesses can reduce engineering overhead, gain access to additional insights, and work with optimized data formats for improved efficiency.
With multiple retrieval options, including flat file downloads in JSONL, Parquet, and CSV formats, as well as API access, our solution adapts to your needs, ensuring seamless integration into your existing data infrastructure.
Clean Company Data is derived from our [Base Company Data](/company-data/base-company-data).
{% hint style="info" %}
The data fields are separated into collections to visualize the data better. The data provided in the samples is strictly intended for illustrative purposes, allowing you to understand its appearance and format better.
{% endhint %}
{% tabs %}
{% tab title="Data fields per category" %}
1. [Metadata](#metadata)
2. [Identifiers](#identifiers)
3. [Firmographics](#firmographics)
4. [Product and services overview](#product-and-services-overview)
5. [Contact information](#contact-information)
6. [Social media and websites](#social-media-and-websites)
7. [Location](#location)
8. [Funding information](#funding-information)
9. [Technologies](#technologies)
10. [Supporting fields](#supporting-fields)
11. [Company updates](#company-updates)
{% endtab %}
{% endtabs %}
## Metadata
| Data field | Processing | Description | Data type |
| -------------------------------- | ---------- | ---------------------------------------------------------- | ------------- |
| `company_last_updated` | Cleaned | Record update date | String (date) |
| `company_created_at` | Cleaned | Record creation date | String (date) |
| `professional_network_source_id` | Raw | Record identification key assigned by professional network | String |
{% code title="Meta data" %}
```json
"company_created_at": "2023-12-06",
"company_last_updated": "2024-12-06",
"professional_network_source_id": "60191",
```
{% endcode %}
Cleaning actions
| Data field | Cleaning action |
| ---------------------- | ---------------------------------------------- |
| `company_last_updated` | Value is converted to the *yyyy-mm-dd* format. |
| `company_created_at` | Value is converted to the *yyyy-mm-dd* format. |
***
## Identifiers
| Data field | Processing | Description | Data type |
| --------------------------------------- | ---------- | ------------------------------------------------------- | ---------------- |
| `company_id` | Raw | Company ID in our database | Number (integer) |
| `company_hash` | Raw | Company profile URL processed by the MD5 algorithm. | String |
| `company_canonical_shorthand_name_hash` | Raw | Canonical shorthand name processed by the MD5 algorithm | String |
| `company_name` | Cleaned | Company name | String |
| `company_logo` | Cleaned | BASE64 encoded JPEG image of the company's logo | String |
| `company_ticker` | Cleaned | Company's stock ticker | String |
| `company_exchange` | Cleaned | Company's stock exchange | String |
{% code title="Identifiers" %}
```json
"company_id": 7811468,
"company_hash": "8ef8d364df382df483f47fe3e56dc4cd",
"company_canonical_shorthand_name_hash": "8631ca96b6f656040bf3326deeb38df6",
"company_name": "Example Company",
"company_logo": "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",
"company_ticker": "EXMP",
"company_exchange": "NYSE",
```
{% endcode %}
Cleaning and enriching actions
| Data field | Cleaning/enriching action |
| ---------------- | -------------------------------------------------------------------------------------------------------------------- |
| `company_name` | Values *\["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"]* are replaced with value `None`. |
| `company_logo` | Image is resized to 50x50px. |
| `company_ticker` | Values *\["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"]* are replaced with value `None`. |
***
## Firmographics
| Data field | Processing | Description | Data type |
| --------------------------------------- | ---------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `company_industry` | Cleaned | Company's industry | String |
| `company_type` | Cleaned | Company type | String |
| `company_founded` | Cleaned | Company's founding year | String |
| `company_size_range` | Cleaned | Company size range | String |
| `company_size_employees_count` | Enriched | The number of employees working in the company | Number (integer) |
| `company_size_employees_count_inferred` | Enriched | Estimated number of employees, calculated based on inferred employee data | Number (integer) |
| `company_followers` | Cleaned | The number of company followers | Number (integer) |
| `company_description` | Cleaned | Company description | String |
| `company_specialities` | Raw | Company specialties | String |
| `metadata_title` | Enriched | Company title parsed from additional sources | String |
| `metadata_description` | Enriched | Company description parsed from additional sources | String |
| `company_enriched_summary` | Enriched | LLM enriched company summary | String |
| `company_enriched_category` | Enriched | Company category assigned with LLM | String |
| `company_enriched_keywords` | Enriched | LLM enriched company keywords | Array of strings |
| `company_enriched_b2b` | Enriched |
Marks if the company offers B2B products/services enriched with the help of LLM 1 – B2B company 0 – not B2B company
| Integer |
{% code title="Firmographics" %}
```json
"company_type": "Partnership",
"company_founded": "2010",
"company_followers": 0,
"company_size_range": "1-10 employees",
"company_size_employees_count": 2,
"company_size_employees_count_inferred": 2,
"company_industry": "Advertising Services",
"company_description": "We help SMEs grow their businesses through effective online marketing strategies. ",
"company_specialities": "Email Marketing, Web Sites, Search Engine Optimisation, Inbound Marketing, Social media Marketing",
"company_enriched_summary": "Company1 is a premier web design and digital marketing agency based in London, UK. Specializing in custom, responsive websites, they provide professional design services, training, easy content management, and ongoing support.",
"company_enriched_keywords": [
"website design",
"digital marketing",
"professional",
"custom responsive websites",
"training"
],
"company_enriched_b2b": 1.0,
"company_enriched_category": "Web Design",
"metadata_title": "Marketing, London,Cost Effective Web Design",
"metadata_description": null
```
{% endcode %}
Cleaning and enriching actions
| Data field | Cleaning/enriching action |
| ------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `company_industry` | Values *\["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"]* are replaced with value `None`. |
| `company_type` | Values *\["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"]* are replaced with value `None`. |
| `company_founded` |
Values \["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None;
Values are replaced with None if the year is not between 500 and the current year.
|
| `company_followers` |
Values \["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value 0;
Every value is converted to an integer.
|
| `company_size_range` |
Some inconsistencies are fixed with overlapping values:
"1 employee" – "Myself Only";
"2-10 employees" – "1-10 employees";
"501-1,000 employees" – "501-1000 employees";
"1,001-5,000 employees" – "1001-5000 employees".
|
| `company_size_employees_count` | When `company_size_employees_count` is `0`, we check if we have any scraped profiles of employees working at this company. If yes, then we count how many employees are associated with it and change the value to that number. This can occur in cases when the public profile does not show some of the employees. |
| `company_industry` | Values *\["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"]* are replaced with value `None`. |
| `company_description` |
Values \["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None;
Value is replaced to None if the description is shorter than 3 characters;
Text styling tags removed;
Multiple spaces are replaced with single ones.
|
***
## Product and services overview
| Data field | Processing | Description | Data type |
| ---------------------- | ---------- | ---------------------------------------------------------- | --------- |
| `pricing_available` | Enriched | Marks if the company service pricing is available online | Boolean |
| `free_trial_available` | Enriched | Marks if the company offers a free trial of their services | Boolean |
| `demo_available` | Enriched | Marks if the company offers a demo | Boolean |
| `is_downloadable` | Enriched | Marks if the company offers a downloadable file/service | Boolean |
| `mobile_apps_exist` | Enriched | Marks if the company has mobile apps for their service | Boolean |
| `online_reviews_exist` | Enriched | Marks if the company has any online reviews | Boolean |
| `api_docs_exist` | Enriched | Marks if the company has API docs published | Boolean |
{% code title="Product and services overview" %}
```json
"pricing_available": true,
"free_trial_available": false,
"demo_available": false,
"is_downloadable": false,
"mobile_apps_exist": false,
"online_reviews_exist": false,
"api_docs_exist": false,
```
{% endcode %}
Enriching actions
| Data field | Enriching action |
| ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------- |
|
| Information taken from the official company website. |
***
## Contact information
| Data field | Processing | Description | Data type |
| ----------------------- | ---------- | ---------------------------------------- | ---------------- |
| `company_phone_numbers` | Enriched | Publicly available company phone number | Array of strings |
| `company_emails` | Enriched | Publicly available company email address | Array of strings |
{% code title="Contact information" %}
```json
"company_phone_numbers": [
"0000 000 000"
],
"company_emails": [
"info@company123.com"
],
```
{% endcode %}
Enriching actions
| Data field | Enriching action |
| ---------------------------------------------------------------------------- | ---------------------------------------------------- |
|
company\_phone\_numbers, company\_emails
| Information taken from the official company website. |
***
## Social media and websites
| Data field | Processing | Description | Data type |
| ------------------------------------------------- | ---------- | -------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `company_websites_main_original` | Raw | Company website | String |
| `company_websites_main` | Enriched | Cleaned and resolved website URL | String |
| `company_websites_facebook` | Enriched | Facebook profile URL | String |
| `company_websites_twitter` | Enriched | Twitter profile URL | String |
| `company_websites_professional_network` | Raw | Professional network URL where the company was first discovered. It can be outdated if the company has changed its profile | String |
| `company_websites_professional_network_canonical` | Raw | The current official Professional network URL for the company, reflecting the most recent updates | String |
| `company_social_discord_urls` | Enriched | Discord channel URL | Array of strings |
| `company_social_facebook_urls` | Enriched | Facebook profile URL | Array of strings |
| `company_social_instagram_urls` | Enriched | Instagram profile URL | Array of strings |
| `company_social_professional_network_urls` | Enriched | Company professional network profile URL | Array of strings |
| `company_social_pinterest_urls` | Enriched | Pinterest profile URL | Array of strings |
| `company_social_tiktok_urls` | Enriched | TikTok profile URL | Array of strings |
| `company_social_twitter_urls` | Enriched | Twitter profile URL | Array of strings |
| `company_social_x_urls` | Enriched | X profile URL | Array of strings |
| `company_social_youtube_urls` | Enriched | YouTube channel/profile URL | Array of strings |
| `company_social_github_urls` | Enriched | Github page/profile URL | Array of strings |
| `company_social_reddit_urls` | Enriched | Reddit profile URL | Array of strings |
{% tabs %}
{% tab title="Social media and websites" %}
{% code title="Social media and websites" %}
```json
"company_websites_main_original": "http://www.example-company.com.",
"company_websites_main": "https://example-company.com.",
"company_websites_facebook": "https://www.facebook.com/example-company",
"company_websites_twitter": "https://www.twitter.com/example-company",
"company_websites_professional_network": "https://www.professional_network.com/company/example-company-international-limited",
"company_websites_professional_network_canonical": "https://www.professional_network.com/company/example-company-international-limited",
```
{% endcode %}
{% endtab %}
{% tab title="Company social links" %}
{% code title="Company social links" %}
```json
"company_social_discord_urls": [
"https://discord.gg/example-company"
],
"company_social_facebook_urls": [
"https://www.facebook.com/example-company"
],
"company_social_instagram_urls": [
"https://www.instagram.com/example_company"
],
"company_social_professional_network_urls": [
"https://www.professional_network.com/company/example-company"
],
"company_social_pinterest_urls": [
"https://www.pinterest.com/example_company"
],
"company_social_tiktok_urls": [
"https://www.tiktok.com/@example_company"
],
"company_social_twitter_urls": [
"https://twitter.com/example_company"
],
"company_social_x_urls": [
"https://www.example-company-x.com"
],
"company_social_youtube_urls": [
"https://www.youtube.com/c/example-company"
],
"company_social_github_urls": [
"https://github.com/example-company"
],
"company_social_reddit_urls": [
"https://www.reddit.com/user/example_company"
]
```
{% endcode %}
{% endtab %}
{% endtabs %}
Cleaning and enriching actions
| Data field | Cleaning/enriching action |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `company_websites_main` |
Every company\_website\_main\_original URL is resolved;
Each URL we collect is parsed, parameters are removed and added to the company\_websites\_main column. URL format in values is seen as \://\.\/\;
Only one company can have a unique \.\/\. If multiple companies have the same URL, we assign it to the company that has the highest number of employees;
Expired domains are removed;
Additional enrichment actions are completed
|
| `company_websites_twitter` | If \ (from `company_websites_main`) == `twitter`, we move the URL value to `company_websites_twitter`*.* |
| `company_websites_facebook` | If \ (from `company_websites_main`) == `facebook`, we move the URL value to `company_websites_facebook`. |
| `company_websites_professional_network` | If \ (from `company_websites_main`) == `professional_network`, we move the URL value to `company_websites_professional_network`. |
|
| URLs taken from the official company website. |
***
## Location
| Data field | Processing | Description | Data type |
| ----------------------------------- | ---------- | ------------------------------------------------------------------------------------------------------- | ---------------- |
| `company_location_hq_raw_address` | Cleaned | Detailed company location | String |
| `company_location_hq_country` | Cleaned | Headquarters country | String |
| `company_location_hq_country_iso_2` | Enriched | ISO 2-letter country code for company headquarters location | String |
| `company_location_hq_country_iso_3` | Enriched | ISO 3-letter country code for company headquarters location | String |
| `company_location_hq_state` | Enriched | Company headquarters state | String |
| `company_location_hq_city` | Enriched | Company headquarters city | String |
| `company_location_hq_regions` | Enriched | Geographical region(s) the company is associated with based on the `company_location_hq_country` value. | String |
| **`company_locations_full`** | Raw | Full company location information | Array of objects |
| `location_address` | Raw | Company HQ location | String |
| `is_primary` | Raw | Marks if the listed location is the primary | Boolean |
| `city` | Enriched | Location city | String |
| `state` | Enriched | Location state | String |
| `country_code` | Enriched | Country code | String |
| `country` | Enriched | Country | String |
| `country_iso_2` | Enriched | ISO 2-letter code of the location country | String |
| `country_iso_3` | Enriched | ISO 3-letter code of the location country | String |
| **`regions`** | Enriched | Regions list | Struct |
| `region` | Enriched | Region | String |
{% code title="Locations" %}
```json
"company_location_hq_raw_address": "Los Angeles, CA, United States",
"company_location_hq_country": "United States",
"company_location_hq_country_iso_2": "US",
"company_location_hq_country_iso_3": "USA",
"company_location_hq_state": "CA",
"company_location_hq_city": "Exampleville",
"company_location_hq_regions": "[Northern America, Northern America, AMER]",
"company_locations_full": [
{
"location_address": "Sample St; Exampleville, CA, USA",
"is_primary": true,
"city": "Exampleville",
"state": "CA",
"country": "United States",
"country_iso_2": "US",
"country_iso_3": "USA",
"regions": [
{
"region": "Northern America"
}
}
],
```
{% endcode %}
Cleaning actions
| Data field | Cleaning action |
| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `location_hq_country` | Values *\["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"]* are replaced with value `None`. |
| `location_hq_raw_address` |
Values \["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None;
Special trailing characters trimmed;
Value company\_location\_hq\_country added to the end of the string (separated by a comma).
|
***
## Funding information
| Data field | Processing | Description | Data type |
| ---------------------------- | ---------- | ------------------------------------------------------------------- | ---------------- |
| `company_funding_rounds` | | Funding round details | Array of objects |
| `last_round_investors_count` | Cleaned | The number of investors that participated in the last funding round | Number (integer) |
| `total_rounds_count` | Cleaned | Total number of funding rounds | Number (integer) |
| `last_round_type` | Cleaned | Last funding round type | String |
| `last_round_date` | Cleaned | Last funding round date | String |
| `last_round_money_raised` | Cleaned | Total funds raised | number (integer) |
| `financial_website_url` | Raw | Financial website URL of the last funding round | String |
{% code title="Funding information" %}
```json
"company_funding_rounds": [
{
"last_round_investors_count": 5,
"total_rounds_count": 3,
"last_round_type": "Series A",
"last_round_date": "2020-11-10",
"last_round_money_raised": 15600000,
"financial_website_url": "https://www.financial_website.com/funding_round/example-company-series-a--f1687fe3"
}
]
}
```
{% endcode %}
Cleaning actions
| Data field | Cleaning action |
| ---------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `company_funding_rounds` |
Duplicate data fields filtered out;
Removed empty/irrelevant data fields.
|
| `last_round_investors_count` |
Values \["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value 0;
Every value is converted to an integer.
|
| `total_rounds_count` |
Values \["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value 0;
Every value is converted to an integer.
|
| `last_round_type` | Values *\["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"]* are replaced with value `0`. |
| `last_round_date` | Value is converted to the *yyyy-mm-dd* format. |
| `last_round_money_raised` |
Values \["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value 0;
Every value is converted to an integer (integer value is parsed from the text value).
|
***
## Technologies
| Data field | Processing | Description | Data type |
| ---------------------- | ---------- | ------------------------------------------------------------------------------------------------------ | ---------------- |
| `company_technologies` | Enriched | Technologies used by the company | Array of structs |
| `technology` | Enriched | Technology name | String |
| `first_verified_at` | Enriched |
Date this technology was first assigned to the company. Date format: YYYY-MM-DD
Date this technology was last assigned to the company. Date format: YYYY-MM-DD
| String (date) |
{% code title="Technologies" %}
```json
"company_technologies": [
{
"technology": "React",
"first_verified_at": "2022-03-15",
"last_verified_at": "2025-02-15"
}
]
```
{% endcode %}
Enriching actions
| Data field | Enriching action |
| ---------------------- | ----------------------------------------------- |
| `company_technologies` | Enriched by our ML model from multiple sources. |
***
## Supporting fields
| Data field | Processing | Description | Data type |
| ------------------ | ---------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- | --------- |
| `expired_domain` | Enriched |
Indicates that the company\_websites\_main\_original URL redirects to a domain dealer
| Integer |
| `unique_subdomain` | Enriched | Indicates that only the record company owns the subdomain | Integer |
| `unique_domain` | Enriched | Indicates that only this company has the right to have this unique domain, e.g., `company_websites_main:` `https://ibm.com` | Integer |
| `unique_website` | Enriched | Indicates that only this company has a unique website but not necessarily a unique domain, e.g., `company_websites_main: https://ibm.com/generation` | Integer |
{% code title="Supporting fields" %}
```json
"expired_domain": 0,
"unique_domain": 1,
"unique_subdomain": 1,
"unique_website": 0,
```
{% endcode %}
***
## Company updates
| Data field | Processing | Description | Data type |
| ------------------------------- | ---------- | --------------------------------------------------------------------------------- | ---------------- |
| `company_updates` | | Company posts and related details | Array of objects |
| `urn` | Raw |
String-based identifier
| String |
| `followers` | Raw | Number of followers | String |
| `date` | Raw |
Post publish date (e.g., 1 month ago)
| String |
| `description` | Raw |
Published text
Note: may contain control characters
| String |
| `reactions_count` | Raw | Number of reactions on the post | Integer |
| `comments_count` | Raw | Number of comments on the post | Integer |
| `reshared_post_author` | Raw | Reshared post author | String |
| `reshared_post_author_url` | Raw | Author's profile URL | String |
| `reshared_post_author_headline` | Raw | Author's headline | String |
| `reshared_post_description` | Raw | Reshared post text | String |
| `reshared_post_followers` | Raw | The number of followers of the reshared post author | Integer |
| `reshared_post_date` | Raw |
Date the reshared post was published (e.g., 1 month ago)
| String |
{% code title="Company updates" %}
```json
"company_updates_collection": [
{
"urn": "urn:pn:activity:6991335602751201281",
"followers": 1371,
"date": "1mo",
"description": "Example description",
"reactions_count": 22,
"comments_count": 2,
"reshared_post_author": "John Doe",
"reshared_post_author_url": "https://www.professional_network.com/john-doe",
"reshared_post_author_headline": "Co-Founder at Example Company, TEDx & Keynote Speaker",
"reshared_post_description": "Example description",
"reshared_post_followers": 45,
"reshared_post_date": "1mo"
}
]
```
{% endcode %}
# Sample: Clean Company Data
Explore a full Clean Company Data sample, including firmographics, funding rounds, tech stack, and up-to-date social and contact details.
Review Coresignal's Clean Company Data sample below, or [contact sales](https://coresignal.com/contact-us/?utm_source=web\&utm_medium=public-docs\&utm_campaign=data-consultation) for more information.
Interested in checking out more data samples? **Visit our self-service platform**:
* Visit Clean Company API playground
* Search, download, or enrich company data
* No credit card required
Start 7-day free trial
{% hint style="info" %}
All personal/company information mentioned within this context is entirely fictional and is solely intended for illustrative purposes.
{% endhint %}
{% code title="Clean company sample" %}
```json
{
"company_id": 1234567,
"company_hash": "4ef4d434df444df444f44fe4e44dc4cd",
"company_canonical_shorthand_name_hash": "1234ca56b6f123456bf1234deeb38df6",
"company_name": "Company 123",
"company_type": "Private",
"company_founded": "2019",
"company_followers": 121000,
"company_websites_main_original": "http://www.company123.com",
"company_websites_main": "http://wwww.company123.com",
"company_websites_resolved": "https://www.company123.com",
"company_websites_facebook": "https://www.facebook.com/company123",
"company_websites_twitter": "https://www.twitter.com/company123",
"company_websites_professional_network": "https://www.professional_network.com/company/company123",
"company_websites_professional_network_canonical": "https://www.professional_network.com/company/company123",
"company_size_range": "51-200 employees",
"company_size_employees_count": 150,
"company_size_employees_count_inferred": 150,
"company_industry": "Software",
"company_description": "Company 123 provides innovative software solutions for businesses.",
"company_location_hq_raw_address": "Cityville, CA, USA",
"company_location_hq_country": "United States",
"company_location_hq_country_iso_2": "US",
"company_location_hq_country_iso_3": "USA",
"company_location_hq_state": "CA",
"company_location_hq_city": "Cityville",
"company_location_hq_regions": [
"North America",
"AMER",
"Northern America"
],
"company_created_at": "2005-06-15",
"company_last_updated": "2024-03-30",
"company_logo": "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",
"company_specialities": "Machine learning, Cloud Computing, AI, SaaS",
"company_ticker": "CMP123",
"company_exchange": "NASDAQ",
"expired_domain": 0,
"unique_domain": 1,
"unique_subdomain": 0,
"unique_website": 1,
"company_enriched_summary": "Company 123 specializes in AI-powered business tools.",
"company_enriched_keywords": [
"AI",
"Cloud",
"SaaS",
"Business Tools"
],
"company_enriched_b2b": 1.0,
"company_enriched_category": "Technology",
"metadata_title": "Company 123 - Leading AI Solutions",
"metadata_description": "Discover Company 123's innovative AI-powered business solutions.",
"company_phone_numbers":[
"+1-800-123-4567"
],
"company_emails": [
"contact@company123.com"
],
"company_social_discord_urls": ["https://discord.gg/company123"],
"company_social_facebook_urls": ["https://facebook.com/company123"],
"company_social_instagram_urls": ["https://instagram.com/company123"],
"company_social_professional_network_urls": ["https://www.professional_network.com/company/company123"],
"company_social_pinterest_urls": ["https://pinterest.com/company123"],
"company_social_tiktok_urls": ["https://tiktok.com/@company123"],
"company_social_twitter_urls": ["https://twitter.com/company123"],
"company_social_x_urls": ["https://x.com/company123"],
"company_social_youtube_urls": ["https://youtube.com/company123"],
"company_social_github_urls": ["https://github.com/company123"],
"company_social_reddit_urls": ["https://reddit.com/r/company123"],
"pricing_available": true,
"free_trial_available": true,
"demo_available": true,
"is_downloadable": false,
"mobile_apps_exist": true,
"online_reviews_exist": true,
"api_docs_exist": true,
"professional_network_source_id": "12345",
"company_funding_rounds": [
{
"last_round_investors_count": 3,
"total_rounds_count": 4,
"last_round_type": "Series B",
"last_round_date": "2022-08-10",
"last_round_money_raised": 50000000,
"financial_website_url": "https://www.financial_website.com/company123"
}
],
"company_locations_full": [
{
"location_address": "Sample St; Exampleville, CA, USA",
"is_primary": true,
"city": "Exampleville",
"state": "CA",
"country": "United States",
"country_iso_2": "US",
"country_iso_3": "USA",
"regions": [
{
"region": "Northern America"
}
}
],
"company_technologies": [
{
"technology": "AWS",
"first_verified_at": "2024-02-20",
"last_verified_at": "2025-03-30"
}
],
"company_updates": [
{
"urn": "urn:pn:activity:111133522751201281",
"followers": 120000,
"date": "2025-03-28",
"description": "Company 123 launches a new AI-powered tool",
"reactions_count": 503,
"comments_count": 102,
"reshared_post_author": "Jane Doe",
"reshared_post_author_url": "https://www.professional_network.com/janedoe",
"reshared_post_author_headline": "Tech Analyst",
"reshared_post_description": "Excited about Company 123's latest AI tool!",
"reshared_post_date": "1mo",
"reshared_post_followers": 124
}
]
}
```
{% endcode %}
# Base Company Data
Regularly updated base company data with core firmographics, company structure, and location signals, available via flat files or API.
Base Company Data is designed to be used for **investment, HR intelligence, and market research.**
| **See the full company picture** | Group and target companies by size, industry, category, and age. |
| ------------------------------------ | ------------------------------------------------------------------------------------------------ |
| **Map company locations** | Map and target companies located in relevant areas. |
| **Dig deep into company structures** | Understand specific relationships between different companies to inform your business decisions. |
***
## Summary
| Feature | Details |
| ------------------ | ------------------------- |
| Available via | Flat files/API |
| Delivery frequency | Daily, weekly and monthly |
| Available formats | JSONL |
| Scraping since | 2016-07 |
## Related links
# Dictionary: Base Company Data
Complete data dictionary for Base Company Data, covering all fields related to company information, funding rounds, locations, investors, and recent updates.
This data dictionary shows all available data fields, explains their values, and provides samples of company data. All tables have snippets of the Base Company dataset.
{% tabs %}
{% tab title="Data fields per category" %}
1. [Main company information](#main-company-information)
2. [Affiliated pages](#affiliated-pages)
3. [Featured employees](#featured-employees)
4. [Investors](#investors)
5. [Funding rounds](#funding-rounds)
6. [Locations](#locations)
7. [Headquarters location](#headquarters-location)
8. [Similar companies](#similar-companies)
9. [Company updates](#company-updates)
10. [Company updates: reshared posts](#company-updates-reshared-posts)
{% endtab %}
{% endtabs %}
## Data fields
### Main company information
| Data field | Description | Data type |
| ---------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------- |
| `id` | Identification key for a company profile record | Integer |
| `source_id` | Professional network static company ID | String |
| `name` | Company name | String |
| `url` | The Professional network URL where the company was first discovered. It can be outdated if the company has changed its profile | String |
| `canonical_url` | The current official Professional network URL for the company, reflecting the most recent updates | String |
| `logo_url` | Company's logo | String |
| `financial_website_url` | Company's financial website profile URL | String |
| `shorthand_name` | Dynamic part of the URL used to update the profile and identify companies | String |
| `canonical_shorthand_name` | The current official shorthand name | String |
| `shorthand_names` | A list of all historical company shorthand names that were captured | Array of structs |
| `shorthand_names[].shorthand_name` | Historical shorthand name value | String |
| `website` | Company website | String |
| `size` | Company size category | String |
| `industry` | Company's industry | String |
| `type` | Company type (e.g., Public Company, Privately Held) | String |
| `description` | Company description | String |
| `tagline` | Company tagline/headline as displayed on Professional network | String |
| `employees_count` | Number of employees on Professional network who associated their experience with the company | Integer |
| `followers` | Profile follower count | Integer |
| `founded` | Company's founding year | Integer |
| `headquarters` | Company headquarters address | String |
| `other_investors` | Count of additional investors not individually listed | Integer |
| `deleted` |
Record deletion status: 1 – the profile returned "Page not found" or was deleted; 0 – the record exists
| Integer |
| `specialties` | List of company specialties | Array of strings |
| `created_at` | The time and date when the record was first scraped | Timestamp |
| `updated_at` | The time and date when the record was last updated | Timestamp |
**Refer to the table example from the data:**
{% code title="Main company information" expandable="true" %}
```json
{
"id": 4412398,
"source_id": "5523409",
"name": "Example Company",
"url": "https://www.professional-network.com/company/example-company",
"canonical_url": "https://www.professional-network.com/company/example-company",
"logo_url": "https://media.licdn.com/dms/image/D4E0BAQGk3mXpT7vYw/company-logo_200_200/0/1700000000000",
"financial_website_url": "https://www.financial-website.com/organization/example-company",
"shorthand_name": "example-company",
"canonical_shorthand_name": "example-company",
"shorthand_names": [
{ "shorthand_name": "example-company" },
{ "shorthand_name": "exampleco" },
{ "shorthand_name": "example-co" }
],
"website": "https://www.example-company.com",
"size": "1001-5000",
"industry": "Software Development",
"type": "Public Company",
"description": "Example Company is a global leader in enterprise software, helping over 10,000 businesses streamline operations through AI-powered solutions. Founded in 2005, we operate across 30+ countries and are committed to building technology that drives real-world impact.",
"tagline": "Empowering Enterprises Through Innovation",
"employees_count": 3200,
"followers": 142300,
"founded": 2005,
"specialties": [
"Enterprise Software",
"Artificial Intelligence",
"Cloud Infrastructure"
],
"deleted": 0,
"other_investors": 3,
"headquarters": "San Francisco, California, United States",
"created_at": "2025-12-24 13:36:58",
"updated_at": "2026-03-24 09:25:24"
}
```
{% endcode %}
### Affiliated pages
| Data field | Description | Data type |
| ------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------- |
| `affiliated_pages` | Profiles of affiliated companies | Array of structs |
| `affiliated_pages[].id` | Identification key for the affiliated company record | String |
| `affiliated_pages[].affiliated_company_id` | Identification key relating to the company table for the affiliate company | Integer |
| `affiliated_pages[].url` | Affiliate company profile URL | String |
| `affiliated_pages[].name` | Affiliate company name | String |
| `affiliated_pages[].location` | Affiliate company location | String |
| `affiliated_pages[].industry` | Affiliate company industry | String |
| `affiliated_pages[].order_in_profile` | Section record order on profile | Integer |
| `affiliated_pages[].deleted` |
Record deletion status: 1 – the profile returned "Page not found" or was deleted; 0 – the record exists
| Integer |
| `affiliated_pages[].created_at` | The time and date when the record was first scraped | Timestamp |
| `affiliated_pages[].updated_at` | The time and date when the record was last updated | Timestamp |
**Refer to the table example from the data:**
{% code title="Affiliated pages" expandable="true" %}
```json
"affiliated_pages": [
{
"id": "94e5cb6f34a2df456d78a23b56adc678",
"affiliated_company_id": 1000001,
"url": "https://www.professional-network.com/company/example-company-europe",
"name": "Example Company Europe",
"location": "Amsterdam, Netherlands",
"industry": "Software Development",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2026-01-15 10:22:33",
"updated_at": "2026-03-27 08:58:20"
}
],
```
{% endcode %}
### Featured employees
| Data field | Description | Data type |
| --------------------------------------- | --------------------------------------------------- | ---------------- |
| `featured_employees` | List of featured employees | Array of structs |
| `featured_employees[].id` | Identification key for the employee record | String |
| `featured_employees[].full_name` | Employee full name | String |
| `featured_employees[].headline` | Employee profile headline | String |
| `featured_employees[].profile_url` | Employee profile URL | String |
| `featured_employees[].order_in_profile` | Section record order on profile | Integer |
| `featured_employees[].deleted` | Record deletion status | Integer |
| `featured_employees[].created_at` | The time and date when the record was first scraped | Timestamp |
| `featured_employees[].updated_at` | The time and date when the record was last updated | Timestamp |
**Refer to the table example from the data:**
{% code title="Featured employees" expandable="true" %}
```json
"featured_employees": [
{
"id": "fa56c2a89fcfede8f12e10c78901c3c9",
"full_name": "John Doe",
"headline": "CEO & Co-Founder at Example Company",
"profile_url": "https://www.professional-network.com/john-doe-12345",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2026-01-15 10:22:33",
"updated_at": "2026-03-26 15:21:44"
}
],
```
{% endcode %}
### Investors
| Data field | Description | Data type |
| -------------------------------------------- | --------------------------------------------------- | ---------------- |
| `featured_investors` | Company investors | Array of structs |
| `featured_investors[].id` | Identification key for the investor record | String |
| `featured_investors[].name` | Investor's name | String |
| `featured_investors[].financial_website_url` | Investor's financial website profile URL | String |
| `featured_investors[].order_in_profile` | Section record order on profile | Integer |
| `featured_investors[].deleted` | Record deletion status | Integer |
| `featured_investors[].created_at` | The time and date when the record was first scraped | Timestamp |
| `featured_investors[].updated_at` | The time and date when the record was last updated | Timestamp |
**Refer to the table example from the data:**
{% code title="Featured investors " expandable="true" %}
```json
"featured_investors": [
{
"id": "ef901234a9e78e7d890123b56a678955",
"name": "Sequoia Capital",
"financial_website_url": "https://www.financial-website.com/organization/sequoia-capital",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2025-09-20 11:00:00",
"updated_at": "2026-03-25 22:15:03"
}
],
```
{% endcode %}
### Funding rounds
| Data field | Description | Data type |
| ------------------------------------------ | ------------------------------------------------ | --------- |
| `funding_rounds` | Last funding round details | Struct |
| `funding_rounds[].total_rounds_count` | Total number of completed funding round | Integer |
| `funding_rounds[].last_round_type` | Last funding round type | String |
| `funding_rounds[].last_round_date` | Last funding round date | String |
| `funding_rounds[].last_round_url` | Funding round record URL on financial website | String |
| `funding_rounds[].last_round_money_raised` | Amount of money raised in the last funding round | String |
**Refer to the table example from the data:**
{% code title="Funding rounds" %}
```json
"funding_rounds": {
"total_rounds_count": 5,
"last_round_type": "Series D",
"last_round_date": "2024-09-18",
"last_round_url": "https://www.financial-website.com/funding_round/example-company-series-d",
"last_round_money_raised": "$120000000"
},
```
{% endcode %}
### Locations
| Data field | Description | Data type |
| ------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------- |
| `locations` | Company locations | Array of structs |
| `locations[].id` | Identification key for the location record | String |
| `locations[].address` | Full company address | String |
| `locations[].country` | Displayed location (country) parsed from the location value | String |
| `locations[].country_iso_2` | ISO 2-letter code of the country | String |
| `locations[].country_iso_3` | ISO 3-letter code of the country | String |
| `locations[].regions` | Region information | Array of structs |
| `locations[].regions[].region` | Location region | String |
| `locations[].state` | Company location state | String |
| `locations[].city` | Company location city | String |
| `locations[].street` | Company location street | String |
| `locations[].apartment` | Company location apartment/suite | String |
| `locations[].zip_code` | Company location zip/postal code | String |
| `locations[].is_primary` |
Denotes if the location is the company's primary location 0 – not a primary location 1 – primary location
| Integer |
| `locations[].order_in_profile` | Section record order on profile | Integer |
| `locations[].deleted` | Record deletion status | Integer |
| `locations[].created_at` | The time and date when the record was first scraped | Timestamp |
| `locations[].updated_at` | The time and date when the record was last updated | Timestamp |
**Refer to the table example from the data:**
{% code title="Locations" expandable="true" %}
```json
"locations": [
{
"id": "fd891ff91c6b7d891234ba678901234f",
"address": "100 Market Street, Suite 800, San Francisco, CA 94105",
"country": "United States",
"country_iso_2": "US",
"country_iso_3": "USA",
"regions": [
{
"region": "Americas"
},
{
"region": "Northern America"
},
{
"region": "AMER"
}
],
"state": "California",
"city": "San Francisco",
"street": "100 Market Street",
"apartment": "Suite 800",
"zip_code": "94105",
"is_primary": 1,
"order_in_profile": 1,
"deleted": 0,
"created_at": "2026-01-15 10:22:33",
"updated_at": "2026-03-28 14:59:59"
}
]
```
{% endcode %}
### Headquarters location
| Data field | Description | Data type |
| ---------------------------------------- | --------------------------------------------- | ---------------- |
| `headquarters_location` | Structured headquarters location breakdown | Struct |
| `headquarters_location.address` | Full company address | String |
| `headquarters_location.country` | Displayed country name | String |
| `headquarters_location.country_iso_2` | ISO 2-letter country code | String |
| `headquarters_location.country_iso_3` | ISO 3-letter country code | String |
| `headquarters_location.state` | State / province | String |
| `headquarters_location.city` | City of the company headquarters | String |
| `headquarters_location.street` | Street address of the company headquarters | String |
| `headquarters_location.apartment` | Apartment number of the company headquarters | String |
| `headquarters_location.suite` | Suite of the company headquarters | String |
| `headquarters_location.zip_code` | ZIP / postal code of the company headquarters | String |
| `headquarters_location.regions` | Region information | Array of structs |
| `headquarters_location.regions[].region` | Location region | String |
**Refer to the table example from the data:**
{% code title="Headquarters locations" expandable="true" %}
```json
"headquarters_location": [
{
"address": "100 Market Street, Suite 800, San Francisco, CA 94105",
"country": "United States",
"country_iso_2": "US",
"country_iso_3": "USA",
"state": "California",
"city": "San Francisco",
"street": "100 Market Street",
"apartment": null,
"suite": "Suite 800",
"zip_code": "94105",
"regions": [
{
"region": "Americas"
},
{
"region": "Northern America"
},
{
"region": "AMER"
}
]
}
]
```
{% endcode %}
### Similar companies
| Data field | Description | Data type |
| ---------------------------------- | --------------------------------------------------- | ---------------- |
| `similar_pages` | Companies similar to the record company | Array of structs |
| `similar_pages[].id` | Identification key for the similar company record | String |
| `similar_pages[].name` | Similar company name | String |
| `similar_pages[].industry` | Similar company industry | String |
| `similar_pages[].url` | Similar company profile URL | String |
| `similar_pages[].location` | Similar company location | String |
| `similar_pages[].order_in_profile` | Section record order on profile | Integer |
| `similar_pages[].deleted` | Record deletion status | Integer |
| `similar_pages[].created_at` | The time and date when the record was first scraped | Timestamp |
| `similar_pages[].updated_at` | The time and date when the record was last updated | Timestamp |
**Refer to the table example from the data:**
{% code title="Similar companies" expandable="true" %}
```json
"similar_pages": [
{
"id": "bbe56789012dd45f9123d4567891234ca",
"name": "Partner Company",
"industry": "Software Development",
"url": "https://www.professional-network.com/company/partner-company",
"location": "New York, New York, United States",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2026-01-15 10:22:33",
"updated_at": "2026-03-25 23:57:20"
}
],
```
{% endcode %}
### Company updates
| Data field | Description | Data type |
| ------------------------------------------ | ----------------------------------------------------------------------- | ---------------- |
| `updates` | Details of the company's posts | Array of structs |
| `updates[].id` | Identification key for the update record | String |
| `updates[].urn` | String-based identifier | String |
| `updates[].logo_url` | Post author logo URL | String |
| `updates[].image_url` | Main post image URL | String |
| `updates[].article_body` | Published text content. Note: may contain control characters | String |
| `article_images` | Array of image URLs in the post | Array of strings |
| `updates[].article_video` | Video attachment details | Struct |
| `updates[].article_video.url` | Video URL | String |
| `updates[].article_video.image_url` | Video thumbnail image URL | String |
| `updates[].article_video.type` | Video type | String |
| `updates[].article_video.captions` | Video captions | String |
| `updates[].article_video.urn` | Video URN identifier | String |
| `updates[].article_document` | Document attachment details | Struct |
| `updates[].article_document[].title` | Document title | String |
| `updates[].article_document[].cover_pages` | Document cover page image URLs | Array of strings |
| `updates[].hashtags` | Hashtags used in the post | Array of strings |
| `updates[].mentions` | Mentioned companies or people | Array of structs |
| `updates[].mentions[].company_name` | Mentioned company name | String |
| `updates[].mentions[].member_name` | Mentioned member name | String |
| `updates[].mentions[].url` | Mentioned entity URL | String |
| `updates[].reaction_count` | Number of reactions on the post | Integer |
| `updates[].comment_count` | Number of comments on the post | Integer |
| `updates[].url` | Direct URL to the post | String |
| `updates[].name` | Title of the article (if link present) or reposter name (when reshared) | String |
| `updates[].order_in_profile` | Section record order on profile | Integer |
| `updates[].deleted` | Record deletion status | Integer |
| `updates[].created_at` | The time and date when the post record was first created | Timestamp |
| `updates[].updated_at` | The time and date when the record was last updated | Timestamp |
**Refer to the table example from the data:**
{% code title="Company updates" expandable="true" %}
```json
"updates": [
{
"id": "e4a0123ce7891234c3cbcd563b91ce89e",
"urn": "urn:activity:7192837465019283746",
"logo_url": "https://media.licdn.com/dms/image/D4E0BAQGk3mXpT7vYw/company-logo_200_200/0/1700000000000",
"image_url": "https://media.licdn.com/dms/image/D4E22AQF3k9mXpT7vYw/feedshare-shrink_800/0/1700000000000",
"article_body": "Excited to share our latest insights on the future of AI in enterprise. The past year has been transformative for our team, and we're just getting started. Drop your thoughts below 👇 #AI #Innovation #FutureOfWork",
"article_images": [
"https://media.licdn.com/dms/image/D4E22AQF9kLmPx3vZw/feedshare-shrink_800/img1.jpg"
],
"article_video": null,
"article_document": null,
"hashtags": [
"AI",
"Innovation",
"FutureOfWork"
],
"mentions": [
{
"company_name": null,
"member_name": "John Doe",
"url": "https://www.professional-network.com/john-doe-12345"
}
],
"reaction_count": 847,
"comment_count": 34,
"url": "https://www.professional-network.com/posts/example-company_ai-innovation-futureofwork-activity-7192837465019283746-xK9p",
"name": "Example Company",
"reshared_post": null,
"order_in_profile": 1,
"deleted": 0,
"created_at": "2026-01-09 20:50:51",
"updated_at": "2026-01-22 22:02:44"
},
```
{% endcode %}
### Company updates: reshared posts
| Data field | Description | Data type |
| ------------------------------------------------------ | ------------------------------------- | ---------------- |
| `updates[].reshared_post` | Details of the original reshared post | Structs |
| `updates[].reshared_post.urn` | Original post URN | String |
| `updates[].reshared_post.logo_url` | Original post author logo | String |
| `updates[].reshared_post.image_url` | Original post image | String |
| `updates[].reshared_post.article_body` | Original post text | String |
| `updates[].reshared_post.article_images` | Original post images | Array of strings |
| `updates[].reshared_post.article_video` | Original post video details | Struct |
| `updates[].reshared_post.article_video.url` | Original post video URL | String |
| `updates[].reshared_post.article_video.image_url` | Original post video thumbnail | String |
| `updates[].reshared_post.article_video.type` | Original post video type | String |
| `updates[].reshared_post.article_video.captions` | Original post video captions | String |
| `updates[].reshared_post.article_video.urn` | Original post video URN | String |
| `updates[].reshared_post.article_document` | Original post document details | Struct |
| `updates[].reshared_post.article_document.title` | Original post document title | String |
| `updates[].reshared_post.article_document.cover_pages` | Original post document cover pages | Array of strings |
| `updates[].reshared_post.hashtags` | Original post hashtags | Array of strings |
| `updates[].reshared_post.mentions` | Original post mentions | Array of structs |
| `updates[].reshared_post.mentions[].company_name` | Original post mentioned company name | String |
| `updates[].reshared_post.mentions[].member_name` | Original post mentioned member name | String |
| `updates[].reshared_post.mentions[].url` | Original post mentioned entity URL | String |
| `updates[].reshared_post.reaction_count` | Original post reaction count | Integer |
| `updates[].reshared_post.comment_count` | Original post comment count | Integer |
| `updates[].reshared_post.date_published` | Original post publish date | String |
| `updates[].reshared_post.author_name` | Original post author name | String |
| `updates[].reshared_post.author_profile_url` | Original post author profile URL | String |
| `updates[].reshared_post.author_headline` | Original post author headline | String |
**Refer to the table example from the data:**
{% code title="Company updates table" expandable="true" %}
```json
"updates": [
{
"id": "4567e7f91f9aeb0df78db12db3befa6e",
"urn": "urn:activity:7178234561827364920",
"logo_url": "https://media.licdn.com/dms/image/D4E0BAQGk3mXpT7vYw/company-logo_200_200/0/1700000000000",
"image_url": null,
"article_body": "Great piece from our partners at Partner Company on where enterprise AI is headed in 2025. Couldn't agree more with the key takeaways. #EnterpriseAI #DigitalTransformation",
"article_images": [],
"article_video": null,
"article_document": null,
"hashtags": ["EnterpriseAI", "DigitalTransformation"],
"mentions": [
{
"company_name": "Partner Company",
"member_name": null,
"url": "https://www.professional-network.com/company/partner-company"
}
],
"reaction_count": 412,
"comment_count": 29,
"url": "https://www.professional-network.com/posts/example-company_enterpriseai-digitaltransformation-activity-7178234561827364920-bQ2r",
"name": "Example Company",
"reshared_post": {
"urn": "urn:activity:7171234560918273645",
"logo_url": "https://media.licdn.com/dms/image/D4E0BAQPm2nWqT9vBd/company-logo_200_200/0/1699000000000",
"image_url": "https://media.licdn.com/dms/image/D4E22AQF9kLmPx3vZw/feedshare-shrink_800/reshare_thumbnail.jpg",
"article_body": "The next wave of enterprise transformation is here. We've just published our 2025 AI Outlook report covering adoption trends, ROI benchmarks, and strategies top-performing companies are using to stay ahead. Download below 👇 #EnterpriseAI #DigitalTransformation",
"article_images": [
"https://media.licdn.com/dms/image/D4E22AQF9kLmPx3vZw/feedshare-shrink_800/img1.jpg"
],
"article_video": null,
"article_document": {
"title": "2025 Enterprise AI Outlook — Full Report",
"cover_pages": [
"https://media.licdn.com/dms/image/D4E22AQDocCover/cover_page_1.jpg"
]
},
"hashtags": ["EnterpriseAI", "DigitalTransformation"],
"mentions": [
{
"company_name": null,
"member_name": "Jane Smith",
"url": "https://www.professional-network.com/jane-smith-67890"
}
],
"reaction_count": 1876,
"comment_count": 143,
"date_published": "20256-04-10T07:45:00Z",
"author_name": "Partner Company",
"author_profile_url": "https://www.professional-network.com/company/partner-company",
"author_headline": "Enterprise AI Solutions | Trusted by Fortune 500 Companies"
},
"order_in_profile": 3,
"deleted": 0,
"created_at": "2026-03-26 15:21:44",
"updated_at": "2026-03-26 15:21:44"
}
],
```
{% endcode %}
# Sample: Base Company Data
Complete data dictionary for Base Company Data, covering all fields related to company information, funding rounds, locations, investors, and recent updates.
Review Coresignal's Base Company Data sample below, or [contact sales](https://coresignal.com/contact-us/?utm_source=web\&utm_medium=public-docs\&utm_campaign=data-consultation) for more information.
Interested in checking out more data samples? **Visit our self-service platform**:
* Visit Base Company API playground
* Search, download, or enrich company data
* No credit card required
Start 7-day free trial
{% hint style="info" %}
All personal/company information mentioned within this context is entirely fictional and is solely intended for illustrative purposes.
{% endhint %}
{
"id": 4412398,
"source_id": "5523409",
"name": "Example Company",
"url": "https://www.professional-network.com/company/example-company",
"canonical_url": "https://www.professional-network.com/company/example-company",
"logo_url": "https://media.licdn.com/dms/image/D4E0BAQGk3mXpT7vYw/company-logo_200_200/0/1700000000000",
"financial_website_url": "https://www.financial-website.com/organization/example-company",
"shorthand_name": "example-company",
"canonical_shorthand_name": "example-company",
"shorthand_names": [
{ "shorthand_name": "example-company" },
{ "shorthand_name": "exampleco" },
{ "shorthand_name": "example-co" }
],
"website": "https://www.example-company.com",
"size": "1001-5000",
"industry": "Software Development",
"type": "Public Company",
"description": "Example Company is a global leader in enterprise software, helping over 10,000 businesses streamline operations through AI-powered solutions. Founded in 2005, we operate across 30+ countries and are committed to building technology that drives real-world impact.",
"tagline": "Empowering Enterprises Through Innovation",
"employees_count": 3200,
"followers": 142300,
"founded": 2005,
"specialties": [
"Enterprise Software",
"Artificial Intelligence",
"Cloud Infrastructure"
],
"deleted": 0,
"other_investors": 3,
"headquarters": "San Francisco, California, United States",
"affiliated_pages": [
{
"id": "94e5cb6f34a2df456d78a23b56adc678",
"affiliated_company_id": 1000001,
"url": "https://www.professional-network.com/company/example-company-europe",
"name": "Example Company Europe",
"location": "Amsterdam, Netherlands",
"industry": "Software Development",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2026-01-15 10:22:33",
"updated_at": "2026-03-27 08:58:20"
}
],
"featured_employees": [
{
"id": "fa56c2a89fcfede8f12e10c78901c3c9",
"full_name": "John Doe",
"headline": "CEO & Co-Founder at Example Company",
"profile_url": "https://www.professional-network.com/john-doe-12345",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2026-01-15 10:22:33",
"updated_at": "2026-03-26 15:21:44"
}
],
"featured_investors": [
{
"id": "ef901234a9e78e7d890123b56a678955",
"name": "Sequoia Capital",
"financial_website_url": "https://www.financial-website.com/organization/sequoia-capital",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2025-09-20 11:00:00",
"updated_at": "2026-03-25 22:15:03"
}
],
"funding_rounds": {
"total_rounds_count": 5,
"last_round_type": "Series D",
"last_round_date": "2024-09-18",
"last_round_url": "https://www.financial-website.com/funding_round/example-company-series-d",
"last_round_money_raised": "$120000000"
},
"locations": [
{
"id": "fd891ff91c6b7d891234ba678901234f",
"address": "100 Market Street, Suite 800, San Francisco, CA 94105",
"country": "United States",
"country_iso_2": "US",
"country_iso_3": "USA",
"street": "100 Market Street",
"apartment": null,
"suite": "Suite 800",
"zip_code": "94105",
"regions": [
{ "region": "Americas" },
{ "region": "Northern America" },
{ "region": "AMER" }
],
"state": "California",
"city": "San Francisco",
"street": "100 Market Street",
"apartment": "Suite 800",
"zip_code": "94105",
"is_primary": 1,
"order_in_profile": 1,
"deleted": 0,
"created_at": "2026-01-15 10:22:33",
"updated_at": "2026-03-28 14:59:59"
}
],
"headquarters_location": [
{
"address": "100 Market Street, Suite 800, San Francisco, CA 94105",
"country": "United States",
"country_iso_2": "US",
"country_iso_3": "USA",
"regions": [
{ "region": "Americas" },
{ "region": "Northern America" },
{ "region": "AMER" }
],
"state": "California",
"city": "San Francisco"
}
],
"similar_pages": [
{
"id": "bbe56789012dd45f9123d4567891234ca",
"name": "Partner Company",
"industry": "Software Development",
"url": "https://www.professional-network.com/company/partner-company",
"location": "New York, New York, United States",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2026-01-15 10:22:33",
"updated_at": "2026-03-25 23:57:20"
}
],
"updates": [
{
"id": "e4a0123ce7891234c3cbcd563b91ce89e",
"urn": "urn:activity:7192837465019283746",
"logo_url": "https://media.licdn.com/dms/image/D4E0BAQGk3mXpT7vYw/company-logo_200_200/0/1700000000000",
"image_url": "https://media.licdn.com/dms/image/D4E22AQF3k9mXpT7vYw/feedshare-shrink_800/0/1700000000000",
"article_body": "Excited to share our latest insights on the future of AI in enterprise. The past year has been transformative for our team, and we're just getting started. Drop your thoughts below 👇 #AI #Innovation #FutureOfWork",
"article_images": [
"https://media.licdn.com/dms/image/D4E22AQF9kLmPx3vZw/feedshare-shrink_800/img1.jpg"
],
"article_video": null,
"article_document": null,
"hashtags": [
"AI",
"Innovation",
"FutureOfWork"
],
"mentions": [
{
"company_name": null,
"member_name": "John Doe",
"url": "https://www.professional-network.com/john-doe-12345"
}
],
"reaction_count": 847,
"comment_count": 34,
"url": "https://www.professional-network.com/posts/example-company_ai-innovation-futureofwork-activity-7192837465019283746-xK9p",
"name": "Example Company",
"reshared_post": null,
"order_in_profile": 1,
"deleted": 0,
"created_at": "2026-01-09 20:50:51",
"updated_at": "2026-01-22 22:02:44"
},
{
"id": "4567e7f91f9aeb0df78db12db3befa6e",
"urn": "urn:activity:7178234561827364920",
"logo_url": "https://media.licdn.com/dms/image/D4E0BAQGk3mXpT7vYw/company-logo_200_200/0/1700000000000",
"image_url": null,
"article_body": "Great piece from our partners at Partner Company on where enterprise AI is headed in 2025. Couldn't agree more with the key takeaways. #EnterpriseAI #DigitalTransformation",
"article_images": [],
"article_video": null,
"article_document": null,
"hashtags": ["EnterpriseAI", "DigitalTransformation"],
"mentions": [
{
"company_name": "Partner Company",
"member_name": null,
"url": "https://www.professional-network.com/company/partner-company"
}
],
"reaction_count": 412,
"comment_count": 29,
"url": "https://www.professional-network.com/posts/example-company_enterpriseai-digitaltransformation-activity-7178234561827364920-bQ2r",
"name": "Example Company",
"reshared_post": {
"urn": "urn:activity:7171234560918273645",
"logo_url": "https://media.licdn.com/dms/image/D4E0BAQPm2nWqT9vBd/company-logo_200_200/0/1699000000000",
"image_url": "https://media.licdn.com/dms/image/D4E22AQF9kLmPx3vZw/feedshare-shrink_800/reshare_thumbnail.jpg",
"article_body": "The next wave of enterprise transformation is here. We've just published our 2025 AI Outlook report covering adoption trends, ROI benchmarks, and strategies top-performing companies are using to stay ahead. Download below 👇 #EnterpriseAI #DigitalTransformation",
"article_images": [
"https://media.licdn.com/dms/image/D4E22AQF9kLmPx3vZw/feedshare-shrink_800/img1.jpg"
],
"article_video": null,
"article_document": {
"title": "2025 Enterprise AI Outlook — Full Report",
"cover_pages": [
"https://media.licdn.com/dms/image/D4E22AQDocCover/cover_page_1.jpg"
]
},
"hashtags": ["EnterpriseAI", "DigitalTransformation"],
"mentions": [
{
"company_name": null,
"member_name": "Jane Smith",
"url": "https://www.professional-network.com/jane-smith-67890"
}
],
"reaction_count": 1876,
"comment_count": 143,
"date_published": "20256-04-10T07:45:00Z",
"author_name": "Partner Company",
"author_profile_url": "https://www.professional-network.com/company/partner-company",
"author_headline": "Enterprise AI Solutions | Trusted by Fortune 500 Companies"
},
"order_in_profile": 3,
"deleted": 0,
"created_at": "2026-03-26 15:21:44",
"updated_at": "2026-03-26 15:21:44"
}
],
"created_at": "2025-12-24 13:36:58",
"updated_at": "2026-03-24 09:25:24"
}
# Company Posts Data
The most recent and historical company posts and communications data from public companies for tracking launches, hiring signals, partnerships, and other updates.
Company Posts data is designed to be used in **Sales Tech, Investment, Market Research, and AI/ML applications.**
| **Company intelligence** | Captures companies' public communications, like product launches, hiring signals, and partnerships, without news aggregator filtering. Ideal for ABM tools and sales teams that need to act on business development moments as they happen. |
| ------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Embedded within the company's ecosystem** | Posts are natively joined to existing company datasets via `company_source_id` or `company_id` – no separate identity resolution step is required. |
| **Historical depth** | With historical data reaching back to 2016, the archive offers robust longitudinal context, enabling trend analysis, baseline benchmarking, and long-cycle account monitoring. |
***
### Summary
| Feature | Details |
| ------------------ | ----------------- |
| Available via | Flat files/API |
| Delivery frequency | Daily and monthly |
| Available formats | JSONL, Parquet |
| Scraping since | 2026-05 |
#### Related links
# Dictionary: Company Posts Data
Data dictionary for Company Posts Data – all fields explained across post content, author details, engagement metrics, and reshared post data.
## Overview
This data dictionary shows all available data fields, explains their values, and provides data samples from the Company Posts dataset.
{% tabs %}
{% tab title="Data fields per category" %}
1. [Author](#author)
2. [Metadata](#metadata)
3. [Post content](#post-content)
4. [Engagement](#engagement)
5. [Reshared post](#reshared-post)
{% endtab %}
{% endtabs %}
## Author
| Data field | Description | Data type |
| ------------------ | ----------------------------------------------- | --------- |
| `company_name` | Company name | String |
| `company_url` | Company profile URL | String |
| `company_headline` | Headline or title of the company (if available) | String |
**Refer to the table example from the data:**
{% code title="Author" %}
```json
{
"company_name": "Example Company",
"company_url": "https://www.professional-network.com/company/example-company",
"company_headline": "Global Leader in Enterprise Software | Empowering 10,000+ Businesses Worldwide",
}
```
{% endcode %}
## Metadata
| Data field | Description | Data type |
| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ | --------- |
| `company_id` | Company record identification key in our database | Long |
| `company_source_id` | Identifier assigned by Professional network | Long |
| `id` | Post's ID | String |
| `url` | Post's URL | String |
| `date_published` | Post publication date | String |
| `deleted` |
Record deletion status: 1 – the profile returned "Page not found" or was deleted; 0 – the record exists
| Integer |
| `created_at` | Record creation timestamp in `ISO 8601` format | Timestamp |
| `updated_at` | Record update timestamp in `ISO 8601` format | Timestamp |
**Refer to the table example from the data:**
{% code title="Metadata" %}
```json
{
"company_id": 9871234560,
"company_source_id": 4412398,
"id": "7192837465019283746",
"url": "https://www.professional-network.com/posts/example-company_ai-innovation-futureofwork-activity",
"date_published": "2026-05-14",
"deleted": 0,
"created_at": "2026-05-14 08:15:22.104837",
"updated_at": "2026-05-17 10:42:55.837291"
}
```
{% endcode %}
## Post content
| Data field | Description | Data type |
| ---------------------- | --------------------------------------------------- | ---------------- |
| `article_body` | Content of the post | String |
| `image_url` | URL of an image attached to the post (if available) | String |
| `hashtags` | Hashtags used in the post | Array of strings |
| `mentions` | Mentioned companies or people | Array of structs |
| `mentions[].full_name` | Name mentioned in the post | String |
| `mentions[].url` | Mentioned entity URL | String |
**Refer to the table example from the data:**
{% code title="Post content" expandable="true" %}
```json
{
"article_body": "Excited to share our latest insights on the future of AI in enterprise. The past year has been transformative for our team, and we're just getting started. Drop your thoughts in the comments below 👇 #AI #Innovation",
"image_url": "https://media.licdn.com/dms/image/D4E22AQF3k9mXpT7vYw/feedshare-shrink_800/0/1700000000000",
"hashtags": [
"AI",
"Innovation"
],
"mentions": [
{
"full_name": "John Doe",
"url": "https://www.professional-network.com/john-doe-12345"
},
{
"full_name": "Jane Smith",
"url": "https://www.professional-network.com/jane-smith-67890"
}
]
}
```
{% endcode %}
## Engagement
| Data field | Description | Data type |
| --------------------------- | ------------------------------------------------------------------------------------------------------------ | ---------------- |
| `reaction_count` | Number of reactions on the post | Integer |
| `comment_count` | Number of comments on the post | Integer |
| `comments` | List of comments on the post | Array of objects |
| `comments[].full_name` | Name of the person who wrote the comment | String |
| `comments[].headline` | Headline or title of the commenter (if available) | String |
| `comments[].profile_url` | URL of the commenter’s profile | String |
| `comments[].body` | Content of the comment | String |
| `comments[].date_published` | Time when comment was published | String |
| `comments[].reaction_count` | Reactions number on the comment | Integer |
| `comments[].deleted` |
| Integer |
| `comments[].created_at` | Comment creation timestamp | Timestamp |
| `comments[].updated_at` | Comment update timestamp | Timestamp |
**Refer to the table example from the data:**
{% code title="Engagement" expandable="true" %}
```json
{
"reaction_count": 847,
"comment_count": 1,
"comments": [
{
"full_name": "Alice Johnson",
"headline": "Head of Product @ Sample Corp",
"profile_url": "https://www.professional-network.com/alice-johnson-48291",
"body": "This is such a timely post. We've been seeing the exact same trends on our end — enterprises are finally moving past the pilot phase and into real production deployments. Would love to connect and exchange notes!",
"reaction_count": 34,
"date_published": "2026-05-15",
"deleted": 0,
"created_at": "2026-05-15 09:23:14.582341",
"updated_at": "2026-05-15 09:23:14.582341"
}
]
}
```
{% endcode %}
## Reshared post
| Data field | Description | Data type |
| ---------------------------------- | ------------------------------------------------------------------------------------ | ---------------- |
| `reshared_post` | Reshared posts information | Struct |
| `reshared_post.id` | Unique identifier of the reshared post. Used to distinguish it from other posts | String |
| `reshared_post.url` | Direct URL to the reshared post on Professional network | String |
| `reshared_post.author_name` | Name of the original author of the reshared post | String |
| `reshared_post.author_profile_url` | Professional network profile URL of the original post’s author | String |
| `reshared_post.author_headline` | Headline or summary text shown under the author’s name, often follower count or role | String |
| `reshared_post.company_id` | The `company_id` of the reshared post in case the post belongs to a company | Long |
| `reshared_post.date_published` | Time since the post was reshared | String |
| `reshared_post.article_body` | Main text content of the reshared post | String |
| `reshared_post.image_url` | Original post image URL | Array of strings |
| `reshared_post.hashtags` | List of hashtags included in the reshared post text | Array of strings |
**Refer to the table example from the data:**
{% code title="Reshared post" expandable="true" %}
```json
{
"reshared_post": {
"id": "7185647382910274651",
"url": "https://www.professional-network.com/posts/partner-company_digitaltransformation-enterpriseai-activity-7185647382910274651-mP4q",
"author_name": "Partner Company",
"author_profile_url": "https://www.professional-network.com/company/partner-company",
"author_headline": "Enterprise AI Solutions | Trusted by Fortune 500 Companies",
"company_id": 5523109,
"date_published": "2026-05-17",
"article_body": "The next wave of enterprise transformation is here. We've just published our 2024 State of AI report, covering adoption trends, ROI benchmarks, and the strategies top-performing companies are using to stay ahead. Download the full report below 👇 #DigitalTransformation #EnterpriseAI",
"image_url": [
"https://media.licdn.com/dms/image/D4E22AQF9kLmPx3vZw/feedshare-shrink_800/reshare_thumbnail.jpg"
],
"hashtags": [
"DigitalTransformation",
"EnterpriseAI"
]
}
```
{% endcode %}
# Sample: Company Posts Data
Review a full Company Posts Data sample including post content, engagement metrics, author details, and freshly collected publication timestamps.
Review Coresignal's Company Posts Data sample below, or [contact sales](https://coresignal.com/contact-us/?utm_source=web\&utm_medium=public-docs\&utm_campaign=data-consultation) for more information.
{% hint style="info" %}
All personal/company information mentioned within this context is entirely fictional and is solely intended for illustrative purposes.
{% endhint %}
{% code title="Data sample" overflow="wrap" %}
```json
{
"company_id": 9871234560,
"company_source_id": 4412398,
"id": "7192837465019283746",
"url": "https://www.professional-network.com/posts/example-company_ai-innovation-futureofwork-activity",
"company_name": "Example Company",
"company_url": "https://www.professional-network.com/company/example-company",
"company_headline": "Global Leader in Enterprise Software | Empowering 10,000+ Businesses Worldwide",
"date_published": "2026-05-14",
"article_body": "Excited to share our latest insights on the future of AI in enterprise. The past year has been transformative for our team, and we're just getting started. Drop your thoughts in the comments below 👇 #AI #Innovation",
"image_url": "https://media.licdn.com/dms/image/D4E22AQF3k9mXpT7vYw/feedshare-shrink_800/0/1700000000000",
"hashtags": [
"AI",
"Innovation"
],
"mentions": [
{
"full_name": "John Doe",
"url": "https://www.professional-network.com/john-doe-12345"
},
{
"full_name": "Jane Smith",
"url": "https://www.professional-network.com/jane-smith-67890"
}
],
"reaction_count": 847,
"comment_count": 1,
"comments": [
{
"full_name": "Alice Johnson",
"headline": "Head of Product @ Sample Corp",
"profile_url": "https://www.professional-network.com/alice-johnson-48291",
"body": "This is such a timely post. We've been seeing the exact same trends on our end — enterprises are finally moving past the pilot phase and into real production deployments. Would love to connect and exchange notes!",
"reaction_count": 34,
"date_published": "2026-05-15",
"deleted": 0,
"created_at": "2026-05-15 09:23:14.582341",
"updated_at": "2026-05-15 09:23:14.582341"
}
],
"reshared_post": {
"id": "7185647382910274651",
"url": "https://www.professional-network.com/posts/partner-company_digitaltransformation-enterpriseai-activity-7185647382910274651-mP4q",
"author_name": "Partner Company",
"author_profile_url": "https://www.professional-network.com/company/partner-company",
"author_headline": "Enterprise AI Solutions | Trusted by Fortune 500 Companies",
"company_id": 5523109,
"date_published": "2026-05-17",
"article_body": "The next wave of enterprise transformation is here. We've just published our 2024 State of AI report, covering adoption trends, ROI benchmarks, and the strategies top-performing companies are using to stay ahead. Download the full report below 👇 #DigitalTransformation #EnterpriseAI",
"image_url": [
"https://media.licdn.com/dms/image/D4E22AQF9kLmPx3vZw/feedshare-shrink_800/reshare_thumbnail.jpg"
],
"hashtags": [
"DigitalTransformation",
"EnterpriseAI"
]
},
"deleted": 0,
"created_at": "2026-05-14 08:15:22.104837",
"updated_at": "2026-05-17 10:42:55.837291"
}
```
{% endcode %}
# Multi-source Company API
Multi-source Company API overview: endpoints and rate limits for accessing enriched fresh company data via Search, Collect, Enrich, and Bulk Collect.
## Overview
This section covers basic information on the Multi-source Company API.\
To learn more about the API and its endpoints, follow the links below:
## Multi-source Company API endpoints
{% hint style="info" %}
Our API is a data retrieval tool. The endpoints do not support analytic features.
{% endhint %}
Multi-source Company API features **three search** and **three collect** endpoints for searching and collecting relevant Multi-source Company data.
{% hint style="warning" %}
All Multi-source Company API requests must be made over HTTPS. Requests made over HTTP will fail or be redirected to HTTPS.
{% endhint %}
Multi-source Company API supports two types of requests:
* **Search** endpoints support POST requests only.
* **Collect** endpoints support the GET requests only.
Collect Multi-source company data using website URLs
20
\*📌 Full profile URL example: [www.professional-network.com/company/\*\*example-company\*\*.\\](http://www.professional-network.com/company/**example-company**.\\)
Shorthand name example: example-company.
### Bulk Collect
Bulk Collect (Bulk API) expands upon Multi-source Company API's functionality, featuring **two POST** and **two GET** endpoints. Bulk Collect allows you to search and collect company data in bulk using company IDs and Elasticsearch DSL queries.
| Request type | Endpoint |
| ------------ | -------------------------------------------------------------------------------------------------------------------------------------------------- |
| POST | [*/v2/data\_requests/company\_multi\_source/ids*](/company-api/multi-source-company-api/bulk-collect/post-requests#ids-requests) |
| POST | [*/v2/data\_requests/company\_multi\_source/es\_dsl*](/company-api/multi-source-company-api/bulk-collect/post-requests#elasticsearch-dsl-requests) |
| GET | */v2/data\_requests/{data\_request\_id}/files* |
| GET | */v2/data\_requests/{data\_request\_id}/files/{file\_name}* |
Read more about Bulk Collect in the following article:
# Data Dictionary: Multi-source Company API
Data dictionary for Multi-source Company API – field definitions across fresh firmographics, funding, technographics, workforce trends, and salaries.
Find all data fields with explanations available in the Multi-source Company API data.
Each category includes a table listing the available data fields, their explanations, and data types.
{% tabs %}
{% tab title="Data fields per category" %}
1. [Metadata](#metadata)
2. [Firmographics](#firmographics)
3. [Company updates](#company-updates)
4. [Locations](#locations)
5. [Public contact details](#public-contact-details)
6. [Follower counts & changes](#follower-counts-and-changes)
7. [Competitors](#competitors)
8. [Product overview](#product-overview)
9. [Financials](#financials)
10. [Funding](#funding)
11. [Acquisitions](#acquisitions)
12. [News features](#news-features)
13. [Technographics](#technographics)
14. [Company websites and social media](#company-websites-and-social-media)
15. [Website traffic](#website-traffic)
16. [Employee review score & changes](#employee-review-scores-and-changes)
17. [Workforce trends](#workforce-trends)
18. [Salaries](#salaries)
{% endtab %}
{% endtabs %}
{% hint style="info" %}
The data fields in the example snippets have been rearranged for better grouping. To see where a specific data field stands, check the full data sample [here](/company-api/multi-source-company-api/sample-multi-source-company-api-data).
{% endhint %}
{% hint style="info" %}
All personal/company information mentioned within this context is entirely fictional and is solely intended for illustrative purposes.
{% endhint %}
## Metadata
| Data field | Description | Data type |
| ----------------- | --------------------------------------------------------- | --------------- |
| `id` | Company record identification key in our database | Integer |
| `source_id` | Identifier assigned by Professional Network | String |
| `expired_domain` | Indicates if the domain is expired | Boolean/integer |
| `unique_domain` | Indicates if the domain is unique | Boolean/integer |
| `unique_website` | Indicates if the website is unique | Boolean/integer |
| `last_updated_at` | Last update date of the record in the `YYYY-MM-DD` format | String (date) |
| `created_at` | Record creation date in the `YYYY-MM-DD` format | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Metadata" %}
```json
"id": 8369825,
"source_id": "9082300",
"expired_domain": 0,
"unique_domain": 1,
"unique_website": 1,
"last_updated_at": "2025-04-28",
"created_at": "2022-01-21"
```
{% endcode %}
## Firmographics
| Data field | Description | Data type |
| -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- | ------------- |
| `company_name` | Company name | String |
| `company_name_alias` | All name variations associated with the company | String |
| `company_legal_name` | Legal company name | String |
| `company_logo` | Base64-encoded image data of the company's logo | String |
| `company_logo_url` | Logo URL (available from Professional network only) | String |
| `is_b2b` |
Indicates if the company operates in a business-to-business model: 1 – b2b company 0 – b2c company
| Integer |
| `industry` | Company's industry | String |
| `type` | Company type | String |
| `founded_year` | Founding year | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Firmographics" %}
```json
"company_name": "Example Company",
"company_legal_name": "Example Company, Inc.",
"company_name_alias": [
"example-company.com",
"Example Company"
"Example Company, Inc. "
]
"company_logo": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoMDAsK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\\r\\nHHqjQ28Rijjkusl2XBJK9Cea8gooAKKKKACiiigAooooAKKKKACiiigD/9k=",
"company_logo_url": "https://www.professional-network.com/logo-url",
"is_b2b": 1,
"industry": "Software Development",
"founded_year": "2000",
```
{% endcode %}
### SIC and NAICS codes
| Data field | Description | Data type |
| ------------- | --------------------- | ---------------- |
| `sic_codes` | Company's SIC codes | Array of strings |
| `naics_codes` | Company's NAICS codes | Array of strings |
**See a snippet of the dataset for reference:**
{% code title="SIC and NAISC codes" %}
```json
"sic_codes": [
"87",
"874"
],
"naics_codes": [
"32",
"325"
],
```
{% endcode %}
### Descriptions
| Data field | Description | Data type |
| -------------------------- | -------------------------------------------------------------------------------------------------- | --------- |
| `description` | Company description | String |
| `description_enriched` | Company description, enriched with LLM | String |
| `description_metadata_raw` |
Company description (parsed from external sources not included in our firmographic data)
| String |
**See a snippet of the dataset for reference:**
{% code title="Descriptions" %}
```json
"description": "Example Company (Nasdaq: EXMP) is a proven cloud CCaaS platform that helps business leaders redefine customer engagement and transform their contact center’s performance. Decision-makers use Example Company to improve customer experience, boost agent productivity, empower their managers, and enhance their system orchestration capabilities. Everything needed to deliver game-changing results can be seamlessly integrated and configured to maximize your success: Omnichannel Communications, AI, a Contact Center CRM, and Workforce Engagement Management tools. For more than 20 years, clients of all sizes and industries have trusted Example Company’s scalable and reliable cloud platform to power billions of omnichannel interactions every year.",
"description_enriched": "Example Company is a cloud-based call and contact center software provider that offers a range of products and solutions for businesses of all sizes. Their platform includes features such as voice, email, SMS, CRM, and workforce management, and they offer a variety of services to support their clients, including training, implementation, and consulting. ",
"description_metadata_raw": "Example Company CCaaS Ups Your Call / Contact Center Platform with Communication Software So You Can Be A Game-Changer: Voice, Email, SMS, CRM, WFM, for Inbound & Outbound Agents.",
```
{% endcode %}
### Company size
| Data field | Description | Data type |
| ----------------- | ------------------------------------------------------------------------------------------------------- | --------- |
| `size_range` |
Company size based on employee count range (as selected by the company profile administrator)
| String |
| `employees_count` | Number of employees on Professional Network who associated their experience with the company | Integer |
**See a snippet of the dataset for reference:**
{% code title="Company size" %}
```json
"size_range": "501-1000 employees",
"employees_count": 594,
```
{% endcode %}
### Inferred employee counts
| Data field | Description | Data type |
| -------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- | ---------------- |
| `employees_count_inferred` | Estimated number of employees, calculated based on inferred employee data | Integer |
| `employees_count_inferred_by_month` | Estimated number of employees, calculated based on inferred employee data, for a three-year rolling window | Array of structs |
| `employees_count_inferred_by_month[].employees_count_inferred` | Estimated number of employees, calculated based on inferred employee data | Integer |
| `employees_count_inferred_by_month[].date` | Date identifier | String |
**See a snippet of the dataset for reference:**
{% code title="Company size" %}
```json
{
"employees_count_inferred": 20,
"employees_count_inferred_by_month": [
{
"employees_count_inferred": 20,
"date": "202604"
},
{
"employees_count_inferred": 18,
"date": "202603"
}
]
}
```
{% endcode %}
### Employee attrition
| Data field | Description | Data type |
| ------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `departures_count` | Count of employees who left the company in the current month | Integer |
| `departures_count_by_month` | Historical monthly departures count with corresponding dates | Array of structs |
| `departures_count_by_month.departures_count` | Number of employee departures | Long |
| `departures_count_by_month.date` | Date for departure count | String |
| `employee_attrition_rate` | Current month attrition rate calculated as (`departures_count` / `employees_count_inferred`) \* 100. Value is `null` when `employees_count_inferred` is `0` or `null` | Double |
| `employee_attrition_rate_by_month` | Historical monthly attrition rates with corresponding dates | Array of structs |
| `employee_attrition_rate_by_month.attrition_rate` | Attrition rate | Double |
| `employee_attrition_rate_by_month.date` | Date for attrition rate | String |
**See a snippet of the dataset for reference:**
{% code title="Employee attrition" %}
```json
{
"departures_count": 20,
"departures_count_by_month": [
{
"departures_count": 20,
"date": "202601"
},
{
"departures_count": 18,
"date": "202602"
}
],
"employee_attrition_rate": 20,
"employee_attrition_rate_by_month": [
{
"attrition_rate": 20,
"date": "202601"
},
{
"attrition_rate": 18,
"date": "202602"
}
]
}
```
{% endcode %}
### Categories & keywords
| Data field | Description | Data type |
| ------------------------- | --------------------------------------------------------------------------------------------- | ---------------- |
| `categories_and_keywords` | Categories and keywords assigned to the company profile and products across various platforms | Array of strings |
**See a snippet of the dataset for reference:**
{% code title="Categories and keywords" %}
```json
"categories_and_keywords": [
"call/contact center software provider",
"call & contact center software",
"contact center software"
],
```
{% endcode %}
### Ownership & status
| Data field | Description | Data type |
| ------------------ | -------------------------------- | ------------------------- |
| `status` | Operational and ownership status | Array of objects (struct) |
| `value` | Current operational status | String |
| `comment` | Current ownership status | String |
| `ownership_status` | Ownership status | String |
**See a snippet of the dataset for reference:**
{% code title="Ownership and status" %}
```json
"status": {
"value": "active",
"comment": "Acquired"
},
"ownership_status": "Public",
```
{% endcode %}
### Parent company
| Data field | Description | Data type |
| ---------------------------- | ---------------------------------------------------- | --------------- |
| `parent_company_information` | Parent company details | Object (struct) |
| `parent_company_id` | Matched parent company ID | String |
| `parent_company_name` | Parent company name | String |
| `parent_company_website` | Parent company website | String |
| `date` | Date of the information provided in `MM/YYYY` format | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Parent company" %}
```json
"parent_company_information": {
"parent_company_id": "1234",
"parent_company_name": "Parent Company",
"parent_company_website": "https://www.parent-company.com/",
"date": "10/2023"
},
```
{% endcode %}
## Company updates
| Data field | Description | Data type |
| ------------------------------- | --------------------------------------------------------------------------------- | ---------------- |
| `company_updates` | Information from posts published by the company | Array of objects |
| `followers` | Profile follower count | Integer |
| `date` | Publish date | String |
| `description` |
Published text
Note: may contain control characters
| String |
| `reactions_count` | Number of reactions on the post | Integer |
| `comments_count` | Number of comments on the post | Integer |
| `reshared_post_author` | Reshared post author | String |
| `reshared_post_author_url` | Profile URL of the reshared post author | String |
| `reshared_post_author_headline` | Headline of the reshared post author | String |
| `reshared_post_description` | Reshared post text | String |
| `reshared_post_followers` | The number of followers of the reshared post author | Integer |
| `reshared_post_date` | Date the reshared post was published | String |
**See a snippet of the dataset for reference:**
{% code title="Company updates" %}
```json
"company_updates": [
{
"followers": 1371,
"date": "2025-03-30",
"description": "Example description",
"reactions_count": 22,
"comments_count": 2,
"reshared_post_author": "John Doe",
"reshared_post_author_url": "https://www.professional-network.com/john-doe",
"reshared_post_author_headline": "Co-Founder at Example Company, TEDx & Keynote Speaker",
"reshared_post_description": "Example description",
"reshared_post_followers": 45,
"reshared_post_date": "1mo"
}
]
```
{% endcode %}
## Locations
| Data field | Description | Data type |
| ------------------- | --------------------------------------------------- | -------------------------- |
| `hq_region` | Region of the company's HQ location | Array of strings |
| `hq_country` | Country where the company's headquarters is located | String |
| `hq_country_iso2` | ISO 2-letter code of the headquarters country | String |
| `hq_country_iso3` | ISO 3-letter code of the headquarters country | String |
| `hq_location` | Headquarters location | String |
| `hq_full_address` | Full address of the headquarters | String |
| `hq_city` | Headquarters city | String |
| `hq_state` | Headquarters state | String |
| `hq_street` | Headquarters street address | String |
| `hq_zipcode` | Headquarters zip code | String |
| `company_locations` | List of company locations | Array of objects (structs) |
| `location_address` | Company location address | String |
| `is_primary` | Indicates if this is the primary company location | Boolean |
**See a snippet of the dataset for reference:**
{% code title="Locations" %}
```json
"hq_region": [
"Americas",
"Northern America",
"AMER"
],
"hq_country": "United States",
"hq_country_iso2": "US",
"hq_country_iso3": "USA",
"hq_location": "Austin, TX, United States",
"hq_full_address": "123 Main Street; Suite 500; Austin, TX 78701, US",
"hq_city": "Austin",
"hq_state": "Texas",
"hq_street": "123 Main Street; Suite 500",
"hq_zipcode": "78701",
"company_locations": [
{
"location_address": "123 Main Street; Suite 500; Austin, TX 78701, US",
"is_primary": true
}
],
```
{% endcode %}
## Public contact details
| Data field | Description | Data type |
| ----------------------- | ---------------------- | ---------------- |
| `company_phone_numbers` | Public phone numbers | Array of strings |
| `company_emails` | Public email addresses | Array of strings |
**See a snippet of the dataset for reference:**
{% code title="Public contact details" %}
```json
"company_phone_numbers": [
"(555) 123-4567"
],
"company_emails": [
"info@example-company.com"
],
```
{% endcode %}
## Follower counts & changes
### Follower counts
| Data field | Description | Data type |
| -------------------------------------- | ---------------------------------------------- | -------------- |
| `followers_count_professional_network` | Profile follower count on professional network | Integer |
| `followers_count_twitter` | Profile follower count on Twitter | Integer (long) |
| `followers_count_owler` | Profile follower count on Owler | Integer (long) |
**See a snippet of the dataset for reference:**
{% code title="Follower counts" %}
```json
"followers_count_professionnal_network": 12838,
"followers_count_twitter": 705,
"followers_count_owler": 188,
```
{% endcode %}
### Follower count changes
| Data field | Description | Data type |
| --------------------------------------------- | --------------------------------------------------------------------------------- | --------------- |
| `professional_network_followers_count_change` | Changes in the number of followers over different periods on professional network | Object (struct) |
| `current` | Current number of followers on the professional network | Integer (long) |
| `change_monthly` | Monthly change in follower count on the professional network | Integer (long) |
| `change_monthly_percentage` | Monthly percentage change in follower count on the professional network | Float (double) |
| `change_quarterly` | Quarterly change in follower count on the professional network | Integer (long) |
| `change_quarterly_percentage` | Quarterly percentage change in follower count on the professional network | Float (double) |
| `change_yearly` | Yearly change in follower count on the professional network | Integer (long) |
| `change_yearly_percentage` | Yearly percentage change in follower count on the professional network | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Professional network followers" %}
```json
"professional_network_followers_count_change": {
"current": 12779,
"change_monthly": 70,
"change_monthly_percentage": 0.5507907781886852,
"change_quarterly": 891,
"change_quarterly_percentage": 7.494952893674293,
"change_yearly": 1845,
"change_yearly_percentage": 16.873971099323214
},
```
{% endcode %}
| Data field | Description | Data type |
| ----------------------------------------------- | ---------------------------------------------------- | ------------------------- |
| `professional_network_followers_count_by_month` | Professional network follower count changes by month | Array of objects (struct) |
| `follower_count` | Number of followers | Integer (long) |
| `date` | Record date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Professional network followers" %}
```json
"professional_network_followers_count_by_month": [
{
"follower_count": 0,
"date": "2019-11-01"
},
{
"follower_count": 1,
"date": "2021-01-01"
}
],
```
{% endcode %}
## Competitors
| Data field | Description | Data type |
| ------------------------------ | ----------------------------------------------------- | ----------------------------------- |
| `competitors` | Competitors and their similarity scores | Array of objects (struct) |
| `company_name` | Competitor's name | String |
| `similarity_score` | Score indicating the similarity to the record company | Integer (long) |
| `competitors_websites` | Details on the competitors' websites |
Array of objects (struct)
|
| `website` | Competitor's website URL | String |
| `total_website_visits_monthly` | Total number of monthly competitor's website visits | Integer (long) |
| `category` | Competitor's website category | String |
| `rank_category` | Competitor's website rank within its category | Integer |
**See a snippet of the dataset for reference:**
{% code title="Competitors" %}
```json
"competitors": [
{
"company_name": "first competitor",
"similarity_score": 5321
},
{
"company_name": "second competitor",
"similarity_score": 5605
}
],
"competitors_websites": [
{
"website": "example-website.com",
"similarity_score": 100,
"total_website_visits_monthly": 91600,
"category": "Law and Government > Government",
"rank_category": 13758
},
{
"website": "example-website2.com",
"similarity_score": 100,
"total_website_visits_monthly": 403700,
"category": "Law and Government > Government",
"rank_category": 3510
}
],
```
{% endcode %}
## Product overview
| Data field | Description | Data type |
| ---------------------- | ---------------------------------------------------------- | --------- |
| `pricing_available` | Marks if service pricing information is available online | Boolean |
| `free_trial_available` | Marks if the company offers a free trial of their services | Boolean |
| `demo_available` | Marks if the company offers a demo | Boolean |
| `is_downloadable` | Marks if the company offers a downloadable file/service | Boolean |
| `mobile_apps_exist` | Marks if the company has mobile apps | Boolean |
| `online_reviews_exist` | Marks if the company has any online reviews | Boolean |
| `documentation_exist` | Marks if the company has public API docs | Boolean |
**See a snippet of the dataset for reference:**
{% code title="Product and services overview" %}
```json
"pricing_available": false,
"free_trial_available": false,
"demo_available": false,
"is_downloadable": false,
"mobile_apps_exist": false,
"online_reviews_exist": false,
"documentation_exist": false,
```
{% endcode %}
### Product pricing
| Data field | Description | Data type |
| ------------------------- | -------------------------------- | -------------------------- |
| `product_pricing_summary` | Summary of product pricing plans | Array of objects (structs) |
| `type` | Pricing plan type | String |
| `price` | Plan price | String |
| `details` | Pricing plan details | String |
**See a snippet of the dataset for reference:**
{% code title="Product pricing" %}
```json
"product_pricing_summary": [
{
"type": "First plan",
"price": "38.00",
"details": "Per Month"
},
{
"type": "Second plan",
"price": "85",
"details": "per month (Annual Plan)"
}
],
```
{% endcode %}
### Product review scores
| Data field | Description | Data Type |
| --------------------------------- | -------------------------------- | -------------- |
| `product_reviews_count` | Total number of product reviews | Integer (long) |
| `product_reviews_aggregate_score` | Average score of product reviews | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Product review fluctuations" %}
```json
"product_reviews_count": 74,
"product_reviews_aggregate_score": 4.513513513513513,
```
{% endcode %}
| Data field | Description | Data type |
| -------------------------------- | ------------------------------ | -------------------------- |
| `product_reviews_score_by_month` | Product review scores by month | Array of objects (structs) |
| `product_reviews_score` | Product review score | Float (double) |
| `date` | Record date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Review score" %}
```json
"product_reviews_score_by_month": [
{
"product_reviews_score": 4.4,
"date": "2019-11-01"
},
{
"product_reviews_score": 4.6,
"date": "2021-01-01"
}
],
```
{% endcode %}
### Product review score distribution
| Data field | Description | Data Type |
| ------------------------------------ | ------------------------------------- | --------------- |
| `product_reviews_score_distribution` | Distribution of product review scores | Object (struct) |
| `score_1` | Number of 1-star reviews | Integer (long) |
| `score_2` | Number of 2-star reviews | Integer (long) |
| `score_3` | Number of 3-star reviews | Integer (long) |
| `score_4` | Number of 4-star reviews | Integer (long) |
| `score_5` | Number of 5-star reviews | Integer (long) |
**See a snippet of the dataset for reference:**
{% code title="Product review score distribution" %}
```json
"product_reviews_score_distribution": {
"score_1": 0,
"score_2": 0,
"score_3": 4,
"score_4": 28,
"score_5": 42
},
```
{% endcode %}
### Product review score changes
| Data field | Description | Data type |
| ------------------------------ | ---------------------------------------------------------- | --------------- |
| `product_reviews_score_change` | Changes in the product review score over different periods | Object (struct) |
| `current` | Current product review score | Float (double) |
| `change_monthly` | Monthly change in product review score | Float (double) |
| `change_quarterly` | Quarterly change in product review score | Float (double) |
| `change_yearly` | Yearly change in product review score | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Product review fluctuations" %}
```json
"product_reviews_score_change": {
"current": 4.3,
"change_monthly": 0.0,
"change_quarterly": 0.0,
"change_yearly": 0.0
},
```
{% endcode %}
## Financials
### Annual revenue range
| Data field | Description | Data type |
| -------------------------------------------------------------------------------------------------------- | ----------------------------------------------------- | --------------- |
| `revenue_annual_range` | Annual revenue range information from various sources | Object (struct) |
|
| Company's acquisition information from various sources | Array of objects (struct) |
| `acquiree_name` | Acquired company name | String |
| `announced_date` | Date when the acquisition was announced in `YYYY-MM-DD` format | String (date) |
| `price` | Acquisition price | Integer |
| `currency` | Acquisition price currency | String |
**See a snippet of the dataset for reference:**
{% code title="Acquisitions" %}
```json
"num_acquisitions_source_1": 2,
"acquisition_list_source_1": [
{
"acquiree_name": "First Acquiree",
"announced_date": "2019-12-10",
"price": 350000000,
"currency": "USD"
},
{
"acquiree_name": "Second Acquiree",
"announced_date": "2020-01-27",
"price": 350000000,
"currency": "USD"
}
],
"num_acquisitions_source_2": 2,
"acquisition_list_source_2": [
{
"acquiree_name": "First Acquiree",
"announced_date": "2020-01-27",
"price": 350000000,
"currency": "USD"
},
{
"acquiree_name": "Second Acquiree",
"announced_date": "2019-12-10",
"price": 350000000,
"currency": "USD"
}
],
```
{% endcode %}
## News features
| Data field | Description | Data type |
| ------------------- | --------------------------------------------------------- | -------------------------- |
| `num_news_articles` | Number of news articles that mention the record company | Integer |
| `news_articles` | Details about the news articles featuring company updates | Array of objects (structs) |
| `headline` | News article headline | String |
| `published_date` | Date the article was published in `YYYY-MM-DD` format | String (date) |
| `summary` | News article summary | String |
| `article_url` | Full news article URL | String |
| `source` | Source of the news | String |
**See a snippet of the dataset for reference:**
{% code title="Media mentions" %}
```json
"num_news_articles": 1,
"news_articles": [
{
"headline": "Example Company Layoffs Hit Channel and Sales Team",
"published_date": "2024-01-08",
"summary": "Professional networking sites such as have seen the influx of Example Company employees posting about getting layoff notices in the past week. The cuts have come following the acquisition by contact-center-as-a-service (CCaaS) giant NICE.",
"article_url": "https://www.channelfutures.com/unified-communications/voicestream-technologies-inc-layoffs-hit-channel-and-sales-team",
"source": "News source example"
}
],
```
{% endcode %}
## Technographics
| Data field | Description | Data type |
| ----------------------- | ------------------------------------------------------ | ---------------- |
| `num_technologies_used` | Number of technologies used by the company | Integer |
| `technologies_used` | List of technologies used by the company | Array of strings |
| `technology` | Technology name | String |
| `first_verified_at` | Date this technology was first assigned to the company | String (date) |
| `last_verified_at` | Date this technology was last assigned to the company | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Company tech stack " %}
```json
"num_technologies_used": 40,
"technologies_used": [
{
"technology": "React",
"first_verified_at": "2022-03-15",
"last_verified_at": "2024-10-15"
}
]
```
{% endcode %}
## Company websites and social media
| Data field | Description | Data type |
| ----------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `website` | Website URL | String |
| `website_domain` | Normalized domain output (e.g., `example-company.com`) | String |
| `website_alias` |
All possible company website variations (collected from our firmographic sources)
| String |
| `professional_network_url` | Professional network URL where the company was first discovered. It can be outdated if the company has changed its profile | String |
| `professional_network_shorthand_name` | Shorthand name of the company's Professional network URL | String |
| `canonical_professional_network_url` | The current official Professional network URL for the company, reflecting the most recent updates | String |
| `canonical_professional_network_shorthand_name` | The current shorthand name of the company's Professional network URL | String |
| `twitter_url` | Twitter profile URL | Array of strings |
| `discord_url` | Discord server URL | Array of strings |
| `facebook_url` | Facebook page URL | Array of strings |
| `instagram_url` | Instagram profile URL | Array of strings |
| `pinterest_url` | Pinterest profile URL | Array of strings |
| `tiktok_url` | TikTok profile URL | Array of strings |
| `youtube_url` | YouTube channel URL | Array of strings |
| `github_url` | GitHub profile URL | Array of strings |
| `reddit_url` | Reddit profile URL | Array of strings |
| `financial_website_url` | Financial network profile URL | String |
**See a snippet of the dataset for reference:**
{% code title="Company websites and social media" %}
```json
"website": "https://www.example-company.com",
"website_domain": "example-company.com",
"website_alias": [
"https://www.example-website.org"
]
"professional_network_url": "https://www.professional-network.com/company/example-company",
"professional_network_shorthand_name": "example-company",
"canonical_professional_network_url": "https://www.professional-network.com/company/example-company",
"canonical_professional_network_shorthand_name": "example-company",
"twitter_url": [
"https://twitter.com/example-company"
],
"discord_url": [
"https://discord.gg/example-company"
],
"facebook_url": [
"https://www.facebook.com/example-company"
],
"instagram_url": [
"https://www.instagram.com/example-company"
],
"pinterest_url": [
"https://www.pinterest.com/example-company"
],
"tiktok_url": [
"https://www.tiktok.com/@example-company"
],
"youtube_url": [
"https://www.youtube.com/example-company"
],
"github_url": [
"https://github.com/example-company"
],
"reddit_url": [
"https://www.reddit.com/user/example-company"
],
"financial_website_url": "https://www.financial-website.com/organization/example-company",
```
{% endcode %}
## Website traffic
### Web traffic and topics
| Data field | Description | Data type |
| -------------------------------- | ----------------------------------------------------------------- | ---------------- |
| `total_website_visits_monthly` | Monthly website visits | Integer (long) |
| `visits_change_monthly` | Monthly change in website visits, shown in percentage | Float (double) |
| `rank_global` | Global rank of the website | Integer |
| `rank_country` | Country-specific rank of the website | Integer |
| `rank_category` | Category-specific rank of the website | Integer |
| `bounce_rate` | Percentage of visitors who leave the site after visiting one page | Float (double) |
| `pages_per_visit` | Average number of pages viewed per visit | Float (double) |
| `average_visit_duration_seconds` | Average duration of a visit in seconds | Float (double) |
| `similarly_ranked_websites` | Similarly ranked websites | Array of strings |
| `top_topics` | List of top topics associated with the website | Array of strings |
**See a snippet of the dataset for reference:**
{% code title="Web traffic and topics" %}
```json
"total_website_visits_monthly": 72600,
"visits_change_monthly": 14.12,
"rank_global": 573826,
"rank_country": 119057,
"rank_category": 2160,
"bounce_rate": 41.26,
"pages_per_visit": 5.5,
"average_visit_duration_seconds": 287.0,
"similarly_ranked_websites": [
"datainsighttool.com",
"endsanalytics.io"],
"top_topics": [
"google",
"social network",
"social",
"social media",
"google apps"
],
```
{% endcode %}
| Data field | Description | Data type |
| ----------------------------- | -------------------------------------------------------------------- | --------------- |
| `total_website_visits_change` | Changes in the total number of website visits over different periods | Object (struct) |
| `current` | Current number of total website visits | Integer (long) |
| `change_monthly` | Monthly change in total website visits | Integer (long) |
| `change_monthly_percentage` | Monthly percentage change in total website visits | Float (double) |
| `change_quarterly` | Quarterly change in total website visits | Integer (long) |
| `change_quarterly_percentage` | Quarterly percentage change in total website visits | Float (double) |
| `change_yearly` | Yearly change in total website visits | Integer (long) |
| `change_yearly_percentage` | Yearly percentage change in total website visits | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Web traffic and topics" %}
```json
"total_website_visits_change": {
"current": 15432,
"change_monthly": 89,
"change_monthly_percentage": 0.576321854392679,
"change_quarterly": 1043,
"change_quarterly_percentage": 6.781293846102947,
"change_yearly": 1983,
"change_yearly_percentage": 13.425986213489573
}
```
{% endcode %}
| Data field | Description | Data type |
| ------------------------------- | ----------------------- | -------------------------- |
| `total_website_visits_by_month` | Website visits by month | Array of objects (structs) |
| `total_website_visits` | Website visits | Float (double) |
| `date` | Record date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Website visits" %}
```json
"total_website_visits_by_month": [
{
"total_website_visits": 60,
"date": "2019-11-01"
},
{
"total_website_visits": 75,
"date": "2021-01-01"
}
],
```
{% endcode %}
### Visits by country
| Data field | Description | Data type |
| ----------------------------- | ------------------------------------------------------- | -------------------------- |
| `visits_breakdown_by_country` | Breakdown of website visits by country | Array of objects (structs) |
| `country` | Visitor's country | String |
| `percentage` | Percentage of visits from one country | Float (double) |
| `percentage_monthly_change` | Monthly change in percentage of visits from one country | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Visits by country" %}
```json
"visits_breakdown_by_country": [
{
"country": "United States",
"percentage": 74.9,
"percentage_monthly_change": 31.74
}
],
```
{% endcode %}
### Visits by gender
| Data field | Description | Data type |
| ---------------------------- | ------------------------------------- | --------------- |
| `visits_breakdown_by_gender` | Breakdown of website visits by gender | Object (struct) |
| `male_percentage` | Percentage of visits by males | Float (double) |
| `female_percentage` | Percentage of visits by females | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Visits by gender" %}
```json
"visits_breakdown_by_gender": {
"male_percentage": 64.04,
"female_percentage": 35.96
},
```
{% endcode %}
### Visits by age
| Data field | Description | Data type |
| ------------------------- | ----------------------------------------------- | --------------- |
| `visits_breakdown_by_age` | Breakdown of website visits by age group | Object (struct) |
| `age_18_24_percentage` | Percentage of visits by users aged 18-24 | Float |
| `age_25_34_percentage` | Percentage of visits by users aged 25-34 | Float |
| `age_35_44_percentage` | Percentage of visits by users aged 35-44 | Float |
| `age_45_54_percentage` | Percentage of visits by users aged 45-54 | Float |
| `age_55_64_percentage` | Percentage of visits by users aged 55-64 | Float |
| `age_65_plus_percentage` | Percentage of visits by users aged 65 and above | Float |
**See a snippet of the dataset for reference:**
{% code title="Visits by age" %}
```json
"visits_breakdown_by_age": {
"age_18_24_percentage": 22.92,
"age_25_34_percentage": 32.22,
"age_35_44_percentage": 15.47,
"age_45_54_percentage": 13.31,
"age_55_64_percentage": 10.3,
"age_65_plus_percentage": 5.78
},
```
{% endcode %}
## Employee review scores & changes
### Review count
| Data field | Description | Data type |
| ------------------------------------------ | --------------------------------- | -------------- |
| `company_employee_reviews_count` | Total number of employee reviews | Integer (long) |
| `company_employee_reviews_aggregate_score` | Average score of employee reviews | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Review count" %}
```json
"company_employee_reviews_count": 145,
"company_employee_reviews_aggregate_score": 4.1,
```
{% endcode %}
### Review score breakdown
| Data field | Description | Data type |
| ---------------------------------- | ------------------------------------------------ | --------------- |
| `employee_reviews_score_breakdown` | Breakdown of employee review ratings by category | Object (struct) |
| `business_outlook` | Business outlook rating | Float (double) |
| `career_opportunities` | Career opportunities rating | Float (double) |
| `ceo_approval` | CEO approval rating | Float (double) |
| `compensation_benefits` | Compensation and benefits rating | Float (double) |
| `culture_values` | Culture and values rating | Float (double) |
| `diversity_inclusion` | Diversity and inclusion rating | Float (double) |
| `recommend` | Recommendation rating | Float (double) |
| `senior_management` | Senior management rating | Float (double) |
| `work_life_balance` | Work-life balance rating | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Review score breakdown" %}
```json
"employee_reviews_score_breakdown": {
"business_outlook": 0.55,
"career_opportunities": 3.6,
"ceo_approval": 0.57,
"compensation_benefits": 4.2,
"culture_values": 4.1,
"diversity_inclusion": 3.7,
"recommend": 0.76,
"senior_management": 3.4,
"work_life_balance": 4.2
},
```
{% endcode %}
### Review score distribution
| Data field | Description | Data type |
| ------------------------------------- | ------------------------------------------- | --------------- |
| `employee_reviews_score_distribution` | Distribution of star ratings in the reviews | Object (struct) |
| `score_1` | Number of 1-star reviews | Integer (long) |
| `score_2` | Number of 2-star reviews | Integer (long) |
| `score_3` | Number of 3-star reviews | Integer (long) |
| `score_4` | Number of 4-star reviews | Integer (long) |
| `score_5` | Number of 5-star reviews | Integer (long) |
**See a snippet of the dataset for reference:**
{% code title="Review rating distribution" %}
```json
"employee_reviews_score_distribution": {
"score_1": 2,
"score_2": 7,
"score_3": 9,
"score_4": 14,
"score_5": 28
},
```
{% endcode %}
### Total rating change
| Data field | Description | Data type |
| ------------------------------------------ | --------------------------------------------------------------------------------- | --------------- |
| `employee_reviews_score_aggregated_change` | Changes in the aggregated rating score of employee reviews over different periods | Object (struct) |
| `current` | Current aggregated score of employee reviews | Float (double) |
| `change_monthly` | Monthly change in the aggregated score | Float (double) |
| `change_quarterly` | Quarterly change in the aggregated score | Float (double) |
| `change_yearly` | Yearly change in the aggregated score | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Total rating change" %}
```json
"employee_reviews_score_aggregated_change": {
"current": 4.3,
"change_monthly": 0.05,
"change_quarterly": 0.1,
"change_yearly": -0.2
}
```
{% endcode %}
| Data field | Description | Data type |
| -------------------------------------------- | -------------------------------- | -------------------------- |
| `employee_reviews_score_aggregated_by_month` | Aggregated review score by month | Array of objects (structs) |
| `aggregated_score` | Aggregated score | Float (double) |
| `date` | Record date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Aggregated reviews by month" %}
```json
"employee_reviews_score_aggregated_by_month": [
{
"aggregated_score": 3.4,
"date": "2023-03-01"
},
{
"aggregated_score": 3.7,
"date": "2024-07-01"
}
],
```
{% endcode %}
### Rating change in the business outlook category
| Data field | Description | Data type |
| ------------------------------------------------ | ---------------------------------------------- | --------------- |
| `employee_reviews_score_business_outlook_change` | Changes in the business outlook rating score | Object (struct) |
| `current` | Current business outlook score | Float (double) |
| `change_monthly` | Monthly change in the business outlook score | Float (double) |
| `change_quarterly` | Quarterly change in the business outlook score | Float (double) |
| `change_yearly` | Yearly change in the business outlook score | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Rating change in the business outlook category" %}
```json
"employee_reviews_score_business_outlook_change": {
"current": 0.45,
"change_monthly": 0.02,
"change_quarterly": -0.05,
"change_yearly": 0.3
}
```
{% endcode %}
| Data field | Description | Data type |
| -------------------------------------------------- | ------------------------------- | -------------------------- |
| `employee_reviews_score_business_outlook_by_month` | Business outlook score by month | Array of objects (structs) |
| `business_outlook_score` | Business outlook score | Float (double) |
| `date` | Record date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Business outlook score by month" %}
```json
"employee_reviews_score_business_outlook_by_month": [
{
"business_outlook_score": 49.0,
"date": "2023-01-01"
},
{
"business_outlook_score": 49.0,
"date": "2022-09-01"
}
],
```
{% endcode %}
### Rating change in the career opportunities category
| Data field | Description | Data type |
| ---------------------------------------------------- | ---------------------------------------------------------------------- | --------------- |
| `employee_reviews_score_career_opportunities_change` | Changes in the career opportunities rating score from employee reviews | Object (struct) |
| `current` | Current career opportunities score | Float (double) |
| `change_monthly` | Monthly change in the career opportunities score | Float (double) |
| `change_quarterly` | Quarterly change in the career opportunities score | Float (double) |
| `change_yearly` | Yearly change in the career opportunities score | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Rating change in the career opportunities category" %}
```json
"employee_reviews_score_career_opportunities_change": {
"current": 3.8,
"change_monthly": -0.1,
"change_quarterly": -0.15,
"change_yearly": -0.3
}
```
{% endcode %}
| Data field | Description | Data type |
| ------------------------------------------------------ | ----------------------------------- | -------------------------- |
| `employee_reviews_score_career_opportunities_by_month` | Career opportunities score by month | Array of objects (structs) |
| `career_opportunities_score` | Business outlook score | Float (double) |
| `date` | Record date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Career opportunities score by month" %}
```json
"employee_reviews_score_career_opportunities_by_month": [
{
"career_opportunities_score": 3.6,
"date": "2022-10-01"
},
{
"career_opportunities_score": 3.6,
"date": "2022-06-01"
}
],
```
{% endcode %}
### Rating change in the CEO approval category
| Data field | Description | Data type |
| -------------------------------------------- | -------------------------------------------------------------- | --------------- |
| `employee_reviews_score_ceo_approval_change` | Changes in the CEO approval rating score from employee reviews | Object (struct) |
| `current` | Current approval score of the CEO | Float (double) |
| `change_monthly` | Monthly change in the CEO approval score | Float (double) |
| `change_quarterly` | Quarterly change in the CEO approval score | Float (double) |
| `change_yearly` | Yearly change in the CEO approval score | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Rating change in the CEO approval category" %}
```json
"employee_reviews_score_ceo_approval_change": {
"current": 0.58,
"change_monthly": -0.03,
"change_quarterly": -0.05,
"change_yearly": -45.12
}
```
{% endcode %}
| Data field | Description | Data type |
| ---------------------------------------------- | --------------------------- | -------------------------- |
| `employee_reviews_score_ceo_approval_by_month` | CEO approval score by month | Array of objects (structs) |
| `ceo_approval_score` | CEO approval score | Float (double) |
| `date` | Record date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="CEO approval score by month" %}
```json
"employee_reviews_score_ceo_approval_by_month": [
{
"ceo_approval_score": 4.0,
"date": "2023-03-01"
},
{
"ceo_approval_score": 3.0,
"date": "2022-08-01"
}
],
```
{% endcode %}
### Rating change in the compensation and benefits category
| Data field | Description | Data type |
| ----------------------------------------------------- | --------------------------------------------------------------------------- | --------------- |
| `employee_reviews_score_compensation_benefits_change` | Changes in the compensation and benefits rating score from employee reviews | Object (struct) |
| `current` | Current compensation and benefits score | Float (double) |
| `change_monthly` | Monthly change in the compensation and benefits score | Float (double) |
| `change_quarterly` | Quarterly change in the compensation and benefits score | Float (double) |
| `change_yearly` | Yearly change in the compensation and benefits score | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Rating change in the compensation and benefits category" %}
```json
"employee_reviews_score_compensation_benefits_change": {
"current": 4.3,
"change_monthly": 0.05,
"change_quarterly": 0.07,
"change_yearly": -0.08
}
```
{% endcode %}
| Data field | Description | Data type |
| ------------------------------------------------------- | ---------------------------------------- | -------------------------- |
| `employee_reviews_score_compensation_benefits_by_month` | Compensation and benefits score by month | Array of objects (structs) |
| `compensation_benefits_score` | Compensation and benefits score | Float (double) |
| `date` | Record date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Comensation benefits by month" %}
```json
"employee_reviews_score_compensation_benefits_by_month": [
{
"compensation_benefits_score": 3.6,
"date": "2023-02-01"
},
{
"compensation_benefits_score": 3.6,
"date": "2022-12-01"
}
],
```
{% endcode %}
### Rating change in the culture and values category
| Data field | Description | Data type |
| ---------------------------------------------- | -------------------------------------------------------------------- | --------------- |
| `employee_reviews_score_culture_values_change` | Changes in the culture and values rating score from employee reviews | Object (struct) |
| `current` | Current culture and values score | Float (double) |
| `change_monthly` | Monthly change in the culture and values score | Float (double) |
| `change_quarterly` | Quarterly change in the culture and values score | Float (double) |
| `change_yearly` | Yearly change in the culture and values score | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Rating change in culture and values category" %}
```json
"employee_reviews_score_culture_values_change": {
"current": 4.1,
"change_monthly": 0.1,
"change_quarterly": 0.2,
"change_yearly": -0.3
}
```
{% endcode %}
| Data field | Description | Data type |
| ------------------------------------------------ | --------------------------------- | -------------------------- |
| `employee_reviews_score_culture_values_by_month` | Culture and values score by month | Array of objects (structs) |
| `culture_values_score` | Culture and values score | Float (double) |
| `date` | Record date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Culture and values score by month" %}
```json
"employee_reviews_score_culture_values_by_month": [
{
"culture_values_score": 3.9,
"date": "2023-03-01"
},
{
"culture_values_score": 3.9,
"date": "2022-11-01"
}
],
```
{% endcode %}
### Rating change in the culture and values category
| Data field | Description | Data type |
| ---------------------------------------------- | -------------------------------------------------------------------- | --------------- |
| `employee_reviews_score_culture_values_change` | Changes in the culture and values rating score from employee reviews | Object (struct) |
| `current` | Current culture and values score | Float (double) |
| `change_monthly` | Monthly change in the culture and values score | Float (double) |
| `change_quarterly` | Quarterly change in the culture and values score | Float (double) |
| `change_yearly` | Yearly change in the culture and values score | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Rating change in the culture and values category" %}
```json
"employee_reviews_score_culture_values_change": {
"current": 4.1,
"change_monthly": 0.1,
"change_quarterly": 0.2,
"change_yearly": -0.3
}
```
{% endcode %}
### Rating change in the diversity and inclusion category
| Data field | Description | Data type |
| --------------------------------------------------- | ------------------------------------------------------------------------- | --------------- |
| `employee_reviews_score_diversity_inclusion_change` | Changes in the diversity and inclusion rating score from employee reviews | Object (struct) |
| `current` | Current diversity and inclusion score | Float (double) |
| `change_monthly` | Monthly change in the diversity and inclusion score | Float (double) |
| `change_quarterly` | Quarterly change in the diversity and inclusion score | Float (double) |
| `change_yearly` | Yearly change in the diversity and inclusion score | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Rating change in the diversity and inclusion category" %}
```json
"employee_reviews_score_diversity_inclusion_change": {
"current": 3.7,
"change_monthly": -0.1,
"change_quarterly": -0.2,
"change_yearly": -0.3
}
```
{% endcode %}
| Data field | Description | Data type |
| ----------------------------------------------------- | -------------------------------------- | -------------------------- |
| `employee_reviews_score_diversity_inclusion_by_month` | Diversity and inclusion score by month | Array of objects (structs) |
| `diversity_inclusion_score` | Diversity and inclusion score | Float (double) |
| `date` | Record date | String (date) |
{% code title="Diversity and inclusion score by month" %}
```json
"employee_reviews_score_diversity_inclusion_by_month": [
{
"diversity_inclusion_score": 4.5,
"date": "2023-03-01"
},
{
"diversity_inclusion_score": 4.5,
"date": "2022-11-01"
}
],
```
{% endcode %}
### Rating change in the recommendations category
| Data field | Description | Data type |
| ----------------------------------------- | ---------------------------------------------------------------- | --------------- |
| `employee_reviews_score_recommend_change` | Changes in the recommendation rating score from employee reviews | Object (struct) |
| `current` | Current recommendation score | Float (double) |
| `change_monthly` | Monthly change in the recommendation score | Float (double) |
| `change_quarterly` | Quarterly change in the recommendation score | Float (double) |
| `change_yearly` | Yearly change in the recommendation score | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Rating change in the recommendations category" %}
```json
"employee_reviews_score_recommend_change": {
"current": 0.76,
"change_monthly": -0.12,
"change_quarterly": -0.12,
"change_yearly": -0.24
}
```
{% endcode %}
| Data field | Description | Data type |
| ------------------------------------------- | -------------------------------------- | -------------------------- |
| `employee_reviews_score_recommend_by_month` | Likelihood to recommend score by month | Array of objects (structs) |
| `recommend_score` | Likelihood to recommend score | Float (double) |
| `date` | Record date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Recommendation score by month" %}
```json
"employee_reviews_score_recommend_by_month": [
{
"recommend_score": 0.54,
"date": "2024-07-01"
},
{
"recommend_score": 0.54,
"date": "2024-06-01"
}
],
```
{% endcode %}
### Rating change in the senior management category
| Data field | Description | Data type |
| ------------------------------------------------- | ------------------------------------------------------------------- | --------------- |
| `employee_reviews_score_senior_management_change` | Changes in the senior management rating score from employee reviews | Object (struct) |
| `current` | Current senior management score | Float (double) |
| `change_monthly` | Monthly change in the senior management score | Float (double) |
| `change_quarterly` | Quarterly change in the senior management score | Float (double) |
| `change_yearly` | Yearly change in the senior management score | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Rating change in the senior management category" %}
```json
"employee_reviews_score_senior_management_change": {
"current": 3.4,
"change_monthly": -0.1,
"change_quarterly": -0.1,
"change_yearly": -0.3
}
```
{% endcode %}
| Data field | Description | Data type |
| --------------------------------------------------- | -------------------------------- | -------------------------- |
| `employee_reviews_score_senior_management_by_month` | Senior management score by month | Array of objects (structs) |
| `senior_management_score` | Senior management score | Float (double) |
| `date` | Record date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Senior management score by month" %}
```json
"employee_reviews_score_senior_management_by_month": [
{
"senior_management_score": 3.9,
"date": "2023-03-01"
},
{
"senior_management_score": 3.9,
"date": "2022-11-01"
}
],
```
{% endcode %}
### Rating change in the work and life balance category
| Data field | Description | Data type |
| ------------------------------------------------- | ------------------------------------------------------------------- | --------------- |
| `employee_reviews_score_work_life_balance_change` | Changes in the work-life balance rating score from employee reviews | Object (struct) |
| `current` | Current work-life balance score | Float (double) |
| `change_monthly` | Monthly change in the work-life balance score | Float (double) |
| `change_quarterly` | Quarterly change in the work-life balance score | Float (double) |
| `change_yearly` | Yearly change in the work-life balance score | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Rating change in the work and life balance category" %}
```json
"employee_reviews_score_work_life_balance_change": {
"current": 4.2,
"change_monthly": 0.0,
"change_quarterly": 0.0,
"change_yearly": -0.1
}
```
{% endcode %}
| Data field | Description | Data type |
| --------------------------------------------------- | -------------------------------- | -------------------------- |
| `employee_reviews_score_work_life_balance_by_month` | Work-life balance score by month | Array of objects (structs) |
| `work_life_balance_score` | Work-life balance score | Float (double) |
| `date` | Record date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Work-life balance score by month" %}
```json
"employee_reviews_score_work_life_balance_by_month": [
{
"work_life_balance_score": 3.8,
"date": "2023-01-01"
},
{
"work_life_balance_score": 3.8,
"date": "2022-09-01"
}
],
```
{% endcode %}
## Workforce trends
### Key executives
| Data field | Description | Data type |
| ----------------------- | ---------------------- | -------------------------- |
| `key_executives` | List of key executives | Array of objects (structs) |
| `parent_id` | Executive's identifier | String |
| `member_full_name` | Executive's name | String |
| `member_position_title` | Executive's job title | String |
**See a snippet of the dataset for reference:**
{% code title="Key executives" %}
```json
"key_executives": [
{
"parent_id": 86953887,
"member_full_name": "John Doe",
"member_position_title": "Partner"
}
],
```
{% endcode %}
### Key employee change events
| Data field | Description | Data type |
| ---------------------------- | ---------------------------------------------------------------- | -------------------------- |
| `key_employee_change_events` | List of key employee change events and corresponding information | Array of objects (structs) |
| `employee_change_event_name` | Employee change event | String |
| `employee_change_event_date` | Employee change event date in `YYYY-MM-DD` format | String (date) |
| `employee_change_event_url` | Event article URL | String |
**See a snippet of the dataset for reference:**
{% code title="Key employee change events" %}
```json
"key_employee_change_events": [
{
"employee_change_event_name": "Example Company Appoints John Doe as Chief Investment Officer",
"employee_change_event_date": "2024-01-18",
"employee_change_event_url" : "https://www.vcaonline.com/news/2024011822/voicestream-technologies-appoints-john-doe-as-chief-investment-officer/"
}
],
```
{% endcode %}
### Key executive arrivals
| Data field | Description | Data type |
| ------------------------ | --------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `key_executive_arrivals` |
List of new executives in the company. Executives are considered members that have a decision\_maker = true flag
| Array of strings |
| `parent_id` | Executive's identifier | Integer (long) |
| `member_full_name` | Full name | String |
| `member_position_title` | Position title | String |
| `arrival_date` | Employment start date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Key executives" %}
```json
"key_executive_arrivals": [
{
"parent_id": 423235614,
"member_full_name": "John Doe",
"member_position_title": "Partner",
"arrival_date": "Apr 2024"
},
{
"parent_id": 2241368,
"member_full_name": "Marry Moe",
"member_position_title": "Partner",
"arrival_date": "May 2024"
}
],
```
{% endcode %}
### Key executive departures
| Data field | Description | Data type |
| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ---------------- |
| `key_executive_departures` |
List of former executives in the company. Executives are considered members that have a decision\_maker = true flag
| Array of strings |
| `parent_id` | Executive's identifier | Integer (long) |
| `member_full_name` | Full name | String |
| `member_position_title` | Position title | String |
| `departure_date` | Employment end date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Key employee departures" %}
```json
"key_executive_departures": [
{
"parent_id": 692515608,
"member_full_name": "John Doe",
"member_position_title": "Partner",
"departure_date": "May 2024"
},
{
"parent_id": 83299323,
"member_full_name": "Danny Doe",
"member_position_title": "Partner",
"departure_date": "Aug 2024"
}
],
```
{% endcode %}
### Top companies
| Data field | Description | Data type |
| ------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `top_previous_companies` | Top ten companies that likely were former workplaces for the current workforce. `Likely to switch` counts are based on `member_experience` data | Array of objects |
| `company_id` | Company identification key | Integer (long) |
| `company_name` | Company name | String |
| `count` | Count to identify the number of transitions | Integer (long) |
| `top_next_companies` |
Top ten companies, people will likely switch after their current job. Likely to switch counts are based on member\_experience data
| Array of objects |
| `company_id` | Company identification key | Integer (long) |
| `company_name` | Company name | String |
| `count` | Count to identify the number of transitions | Integer (long) |
**See a snippet of the dataset for reference:**
{% code title="Top companies" %}
```json
"top_previous_companies": [
{
"company_id": 110,
"company_name": "Example Company",
"count": 5
}
],
"top_next_companies": [
{
"company_id": 110,
"company_name": "Example Sister Company",
"count": 3
}
]
```
{% endcode %}
### Employee count by department
| Data field | Description | Data type |
| ----------------------------------------- | -------------------------------------------------------- | --------------- |
| `employees_count_breakdown_by_department` | Breakdown of employee count by department | Object (struct) |
| `employees_count_medical` | Number of employees in the medical department | Integer (long) |
| `employees_count_sales` | Number of employees in the sales department | Integer (long) |
| `employees_count_hr` | Number of employees in the HR department | Integer (long) |
| `employees_count_legal` | Number of employees in the legal department | Integer (long) |
| `employees_count_marketing` | Number of employees in the marketing department | Integer (long) |
| `employees_count_finance` | Number of employees in the finance department | Integer (long) |
| `employees_count_techical` | Number of employees in the tech department | Integer (long) |
| `employees_count_consulting` | Number of employees in the consulting department | Integer (long) |
| `employees_count_operations` | Number of employees in the operations department | Integer (long) |
| `employees_count_general_management` | Number of employees in the general management department | Integer (long) |
| `employees_count_administrative` | Number of employees in the administrative department | Integer (long) |
| `employees_count_customer_service` | Number of employees in the customer service department | Integer (long) |
| `employees_count_project_management` | Number of employees in the project management department | Integer (long) |
| `employees_count_design` | Number of employees in the design department | Integer (long) |
| `employees_count_research` | Number of employees in the research department | Integer (long) |
| `employees_count_trades` | Number of employees in the trades department | Integer (long) |
| `employees_count_real_estate` | Number of employees in the real estate department | Integer (long) |
| `employees_count_education` | Number of employees in the education department | Integer (long) |
| `employees_count_other_department` | Number of employees in other departments | Integer (long) |
| `employees_count_product` | Number of employees in the product department | Integer (long) |
**See a snippet of the dataset for reference:**
{% code title="Employee count by department" %}
```json
"employees_count_breakdown_by_department": {
"employees_count_medical": 0,
"employees_count_sales": 24,
"employees_count_hr": 8,
"employees_count_legal": 2,
"employees_count_marketing": 5,
"employees_count_finance": 7,
"employees_count_tech": 63,
"employees_count_consulting": 2,
"employees_count_operations": 2,
"employees_count_other_department": 99,
"employees_count_product": 12
},
```
{% endcode %}
| Data field | Description | Data type |
| -------------------------------------------------- | ----------------------------------------------------------------- | -------------- |
| `employees_count_breakdown_by_department_by_month` | Employee count changes by month and department | Object |
| `employees_count_medical` | Employee count in the medical department | Integer (long) |
| `employees_count_sales` | Employee count in the sales department | Integer (long) |
| `employees_count_hr` | Employee count in the HR department | Integer (long) |
| `employees_count_legal` | Employee count in the legal department | Integer (long) |
| `employees_count_marketing` | Employee count in the marketing department | Integer (long) |
| `employees_count_finance` | Employee count in the finance department | Integer (long) |
| `employees_count_technical` | Employee count in the technical department | Integer (long) |
| `employees_count_consulting` | Employee count in the consulting department | Integer (long) |
| `employees_count_operations` | Employee count in the operations department | Integer (long) |
| `employees_count_product` | Employee count in the product department | Integer (long) |
| `employees_count_general_management` | Employee count in the general management department | Integer (long) |
| `employees_count_administrative` | Employee count in the administrative department | Integer (long) |
| `employees_count_customer_service` | Employee count in the customer service department | Integer (long) |
| `employees_count_project_management` | Employee count in the project management department | Integer (long) |
| `employees_count_design` | Employee count in the design department | Integer (long) |
| `employees_count_research` | Employee count in the research department | Integer (long) |
| `employees_count_trades` | Employee count in the trades department | Integer (long) |
| `employees_count_real_estate` | Employee count in the real estate department | Integer (long) |
| `employees_count_education` | Employee count in the education department | Integer (long) |
| `employees_count_other_department` | Employee count in the other departments | Integer (long) |
| `date` |
Record date. Counts available from 2020.01
| String (date) |
**See a snippet of the dataset for reference:**
{% code title="Employee count by department" %}
```json
"employees_count_breakdown_by_department_by_month": [
{
"employees_count_breakdown_by_department": {
"employees_count_sales": 0,
"employees_count_hr": 0,
"employees_count_legal": 45,
"employees_count_marketing": 4,
"employees_count_finance": 0,
"employees_count_technical": 0,
"employees_count_consulting": 0,
"employees_count_operations": 55,
"employees_count_product": 0,
"employees_count_general_management": 0,
"employees_count_administrative": 0,
"employees_count_customer_service": 0,
"employees_count_project_management": 34,
"employees_count_design": 0,
"employees_count_research": 0,
"employees_count_trades": 56,
"employees_count_real_estate": 0,
"employees_count_education": 0,
"employees_count_other_department": 0,
"employees_count_other_department": 5
},
"date": "2021-01"
}
]
```
{% endcode %}
| Data field | Description | Data type |
| ---------------------------- | ------------------------- | ---------------- |
| `employees_count_by_country` | Employee count by country | Array of objects |
| `country` | Country | String |
| `employee_count` | Employee count | Integer |
**See a snippet of the dataset for reference:**
{% code title="Employee count by country" %}
```json
"employees_count_by_country": [
{
"country": "Germany",
"employee_count": 1
},
{
"country": "United Kingdom",
"employee_count": 1
}
],
```
{% endcode %}
| Data field | Description | Data type |
| ------------------------------------- | ----------------------------------------------------------------- | -------------------------- |
| `employees_count_by_country_by_month` | Employee count by country and month | Array of objects (structs) |
| `employees_count_by_country` | Employee count by country | Object (struct) |
| `country` | Country | String |
| `employee_count` | Employee count | Integer (long) |
| `date` |
Record date. Counts available from 2020.01
| String (date) |
**See a snippet of the dataset for reference:**
{% code title="Employee count by country" %}
```json
"employees_count_by_country_by_month": [
{
"employees_count_by_country": [
{
"country": "United States",
"employee_count": 43
}
],
"date": "2021-01"
}
]
```
{% endcode %}
| Data field | Description | Data type |
| --------------------------------------- | ------------------------------------------- | --------- |
| `employees_count_breakdown_by_region` | Employee count breakdown by region | Object |
| `employees_count_eastern_europe` | Employee count in Eastern Europe | Integer |
| `employees_count_latin_america` | Employee count in Latin America | Integer |
| `employees_count_southern_europe` | Employee count in Southern Europe | Integer |
| `employees_count_sub_saharan_africa` | Employee count in Sub-Saharan Africa | Integer |
| `employees_count_central_asia` | Employee count in Central Asia | Integer |
| `employees_count_northern_america` | Employee count in Northern America | Integer |
| `employees_count_australia_new_zealand` | Employee count in Australia and New Zealand | Integer |
| `employees_count_northern_europe` | Employee count in Northern Europe | Integer |
| `employees_count_south_eastern_asia` | Employee count in Southeast Asia | Integer |
| `employees_count_polynesia` | Employee count in Polynesia | Integer |
| `employees_count_southern_asia` | Employee count in Southern Asia | Integer |
| `employees_count_northern_africa` | Employee count in Northern Africa | Integer |
| `employees_count_melanesia` | Employee count in Melanesia | Integer |
| `employees_count_western_europe` | Employee count in Western Europe | Integer |
| `employees_count_western_asia` | Employee count in Western Asia | Integer |
| `employees_count_eastern_asia` | Employee count in Eastern Asia | Integer |
| `employees_count_micronesia` | Employee count in Micronesia | Integer |
| `employees_count_unknown` | Employee count in unassigned region | Integer |
**See a snippet of the dataset for reference:**
{% code title="Employee count by department" %}
```json
"employees_count_breakdown_by_region": {
"employees_count_eastern_europe" : 4,
"employees_count_latin_america": 5,
"employees_count_southern_europe": 6,
"employees_count_sub_saharan_africa": 0,
"employees_count_central_asia": 15,
"employees_count_northern_america": 0,
"employees_count_australia_new_zealand": 0,
"employees_count_northern_europe": 0,
"employees_count_south_eastern_asia": 0,
"employees_count_polynesia": 2,
"employees_count_southern_asia": 0,
"employees_count_northern_africa": 3,
"employees_count_melanesia": 0,
"employees_count_western_europe": 0,
"employees_count_western_asia": 0,
"employees_count_eastern_asia": 9,
"employees_count_micronesia": 0,
"employees_count_unknown": 0
}
```
{% endcode %}
| Data field | Description | Data type |
| ---------------------------------------------- | ----------------------------------------------------------------- | ---------------- |
| `employees_count_breakdown_by_region_by_month` | Employee count breakdown by region and date | Array of objects |
| `employees_count_breakdown_by_region` | Employee count breakdown by region | Object (struct) |
| `employees_count_eastern_europe` | Employee count in Eastern Europe | Integer |
| `employees_count_latin_america` | Employee count in Latin America | Integer |
| `employees_count_southern_europe` | Employee count in Southern Europe | Integer |
| `employees_count_sub_saharan_africa` | Employee count in Sub-Saharan Africa | Integer |
| `employees_count_central_asia` | Employee count in Central Asia | Integer |
| `employees_count_northern_america` | Employee count in Northern America | Integer |
| `employees_count_australia_new_zealand` | Employee count in Australia and New Zealand | Integer |
| `employees_count_northern_europe` | Employee count in Northern Europe | Integer |
| `employees_count_south_eastern_asia` | Employee count in Southeast Asia | Integer |
| `employees_count_polynesia` | Employee count in Polynesia | Integer |
| `employees_count_southern_asia` | Employee count in Southern Asia | Integer |
| `employees_count_northern_africa` | Employee count in Northern Africa | Integer |
| `employees_count_melanesia` | Employee count in Melanesia | Integer |
| `employees_count_western_europe` | Employee count in Western Europe | Integer |
| `employees_count_western_asia` | Employee count in Western Asia | Integer |
| `employees_count_eastern_asia` | Employee count in Eastern Asia | Integer |
| `employees_count_micronesia` | Employee count in Micronesia | Integer |
| `employees_count_unknown` | Employee count in unassigned region | Integer |
| `date` |
Record date. Counts available from 2020.01
| String (date) |
**See a snippet of the dataset for reference:**
{% code title="Employee count by department" %}
```json
"employees_count_breakdown_by_region_by_month": [
{
"employees_count_breakdown_by_region": {
"employees_count_eastern_europe": 3,
"employees_count_latin_america": 0,
"employees_count_southern_europe": 0,
"employees_count_sub_saharan_africa": 0,
"employees_count_central_asia": 0,
"employees_count_northern_america": 5,
"employees_count_australia_new_zealand": 0,
"employees_count_northern_europe": 7,
"employees_count_south_eastern_asia": 0,
"employees_count_polynesia": 0,
"employees_count_southern_asia": 0,
"employees_count_northern_africa": 0,
"employees_count_melanesia": 5,
"employees_count_western_europe": 0,
"employees_count_western_asia": 6,
"employees_count_eastern_asia": 0,
"employees_count_micronesia": 0,
"employees_count_unknown": 0
},
"date": "2021-01"
}
]
```
{% endcode %}
### Employee count by seniority
| Data field | Description | Data type |
| ---------------------------------------- | ------------------------------------------------------- | --------------- |
| `employees_count_breakdown_by_seniority` | Breakdown of employee count by seniority level | Object (struct) |
| `employees_count_owner` | Number of employees with the `Owner` job title | Integer (long) |
| `employees_count_founder` | Number of employees with the `Founder` job title | Integer (long) |
| `employees_count_clevel` | Number of C-level employees | Integer (long) |
| `employees_count_partner` | Number of employees with the `Partner` job title | Integer (long) |
| `employees_count_vp` | Number of employees with the `Vice President` job title | Integer (long) |
| `employees_count_head` | Number of employees with the `Head` job title | Integer (long) |
| `employees_count_director` | Number of employees with the `Director` job title | Integer (long) |
| `employees_count_manager` | Number of employees with the `Manager` job title | Integer (long) |
| `employees_count_senior` | Number of senior-level employees | Integer (long) |
| `employees_count_mid` | Number of mid-level employees | Integer (long) |
| `employees_count_junior` | Number of junior-level employees | Integer (long) |
| `employees_count_intern` | Number of interns | Integer (long) |
| `employees_count_specialist` | Number of specialists | Integer (long) |
| `employees_count_other_management` | Number of employees in other management roles | Integer (long) |
**See a snippet of the dataset for reference:**
{% code title="Employee count by seniority" %}
```json
"employees_count_breakdown_by_seniority": {
"employees_count_owner": 0,
"employees_count_founder": 0,
"employees_count_clevel": 3,
"employees_count_partner": 1,
"employees_count_vp": 8,
"employees_count_head": 1,
"employees_count_director": 10,
"employees_count_manager": 31,
"employees_count_senior": 73,
"employees_count_mid": 8,
"employees_count_junior": 1,
"employees_count_intern": 0,
"employees_count_specialist": 0,
"employees_count_other_management": 88
},
```
{% endcode %}
| Data field | Description | Data type |
| ------------------------------------------------- | ----------------------------------------------------------------- | ---------------- |
| `employees_count_breakdown_by_seniority_by_month` | Employee count breakdown seniority and date | Array of objects |
| `employees_count_breakdown_by_seniority` | Employee count breakdown by seniority | Object (struct) |
| `employees_count_owner` | Number of owners in the company | Integer |
| `employees_count_founder` | Number of founders in the company | Integer |
| `employees_count_clevel` | Number of C-level employees in the company | Integer |
| `employees_count_partner` | Number of partners in the company | Integer |
| `employees_count_vp` | Number of vice presidents in the company | Integer |
| `employees_count_head` | Number of \*head-\*level employees in the company | Integer |
| `employees_count_director` | Number of directors in the company | Integer |
| `employees_count_manager` | Number of managers in the company | Integer |
| `employees_count_senior` | Number of seniors in the company | Integer |
| `employees_count_intern` | Number of interns in the company | Integer |
| `employees_count_specialist` | Number of specialists in the company | Integer |
| `employees_count_other_management` | Number of other management employees in the company | Integer |
| `date` |
Record date. Counts available from 2020.01
| Integer |
**See a snippet of the dataset for reference:**
{% code title="Employee count by seniority" %}
```json
"employees_count_breakdown_by_seniority_by_month": [
{
"employees_count_breakdown_by_seniority": {
"employees_count_owner": 5,
"employees_count_founder": 3,
"employees_count_clevel": 7,
"employees_count_partner": 2,
"employees_count_vp": 6,
"employees_count_head": 4,
"employees_count_director": 10,
"employees_count_manager": 15,
"employees_count_senior": 20,
"employees_count_intern": 8,
"employees_count_specialist": 12,
"employees_count_other_management": 5
},
"date": "2023-09"
}
]
```
{% endcode %}
### Employee count changes
| Data field | Description | Data type |
| ----------------------------- | -------------------------------------------------------------- | --------------- |
| `employees_count_change` | Changes in the number of employees over different time periods | Object (struct) |
| `current` | Current number of employees | Integer (long) |
| `change_monthly` | Monthly change in employee count | Integer (long) |
| `change_monthly_percentage` | Monthly percentage change in employee count | Float (double) |
| `change_quarterly` | Quarterly change in employee count | Integer (long) |
| `change_quarterly_percentage` | Quarterly percentage change in employee count | Float (double) |
| `change_yearly` | Yearly change in employee count | Integer (long) |
| `change_yearly_percentage` | Yearly percentage change in employee count | Float (double) |
**See a snippet of the dataset for reference:**
{% code title="Employee count change" %}
```json
"employees_count_change": {
"current": 324,
"change_monthly": -26,
"change_monthly_percentage": -7.428571428571429,
"change_quarterly": -213,
"change_quarterly_percentage": -39.66480446927375,
"change_yearly": -244,
"change_yearly_percentage": -42.95774647887324
},
```
{% endcode %}
| Data field | Description | Data type |
| -------------------------- | ------------------------------- | -------------------------- |
| `employees_count_by_month` | Employee count changes by month | Array of objects (structs) |
| `employees_count` | Number of employees | Integer (long) |
| `date` | Record date | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Employees count by month" %}
```json
"employees_count_by_month": [
{
"employees_count": 0,
"date": "2019-11-01"
},
{
"employees_count": 0,
"date": "2021-01-01"
}
],
```
{% endcode %}
### Active job postings
| Data field | Description | Data type |
| --------------------------- | ---------------------------------------------------------------------------------------------------- | ---------------- |
| `active_job_postings_count` | Number of active job postings associated with the company | Integer (long) |
| `active_job_postings` | Active job postings | Array of structs |
| `job_posting_id` | Professional network Job ID | Long |
| `job_posting_title` | Job title posted by the recruiter. Semantic search is applied with `/synonym_search/es_dsl` endpoint | String |
**See a snippet of the dataset for reference:**
{% code title="Active jobs" %}
```json
"active_job_postings_count": 2,
"active_job_postings": [
{
"job_posting_id": 123456789123456,
"job_posting_title": "Product Manager"
}
]
```
{% endcode %}
| Data field | Description | Data type |
| ------------------------------------ | ----------------------------------------------------------------- | --------------- |
| `active_job_postings_count_by_month` | Active job postings by month | Object (struct) |
| `active_job_postings_count` | Job posting count | Integer (long) |
| `date` |
***
## Using pagination in cURL requests
{% hint style="info" %}
This tutorial requires prior knowledge of how to compile and execute POST requests in Multi-source Company API.
{% endhint %}
Use parameter `x-next-page-after` to retrieve a second page of IDs.
1. Navigate to the **Headers** section and click it:
2. Find the following information:\
– `x-next-page-after`\
– `x-total-pages`\
– `x-total-results`
3. Add parameter `?after={x-next-page-after}` to the POST request to see the next results page.
4. Execute the request, and you will see the next page in the **Body** section:
```json
[
1000,
1001,
3000,
4004
]
```
### Pagination using sorting
Pagination using **ID** sorting has similar `x-next-page-after` format, but the **last updated** date is excluded.
#### **Pagination usage example (cURL request in Postman)**
1. Add parameter `?after={x-next-page-after}` to the POST request:\
Refer to the example below for the exact parameter placement:
{% code title="Pagination (sorted by id)" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/company_multi_source/search/es_dsl?after=3771705' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"query": {
"bool": {
"must": [
{
"query_string": {
"query": "(3D printing) OR (3D printing service) OR (Lead generation)",
"default_field": "description",
"default_operator": "and"
}
}
]
}
},
"sort": [
"id"
]
}'
```
{% endcode %}
**Send** the request, and you will see the next page in the **(Response) Body**.
***
Pagination using **score** sorting has a different ID format. The format difference is seen by the `x-next-page-after` parameter, showing the **score**, the **last updated** date, and the **last ID** on the page.
**Pagination usage example (cURL request in Postman)**
Add parameter `?after={x-next-page-after}` to the POST request to see the next results page. Refer to the example below for the exact parameter placement:
{% code title="Pagination (sorted by score)" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/company_multi_source/search/es_dsl?after=26.806067,"2025-02-25",6428995' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"query": {
"bool": {
"must": [
{
"query_string": {
"query": "(3D printing) OR (3D printing service) OR (Lead generation)",
"default_field": "description",
"default_operator": "and"
}
}
]
}
},
"sort": [
"_score"
]
}'
```
{% endcode %}
**Send** the request, and you will see the next page in the **(Response) Body**.
## Limiting search results per page
Query parameter `?items_per_page={int}` allows you to specify the number of results retrieved per Search results page. The current limit is 1,000. Thus, this parameter lets you set a smaller limit value for the results page.
{% code title="Items per page" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/company_multi_source/search/es_dsl?items_per_page=10' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"query": {
"bool": {
"must": [
{
"query_string": {
"query": "(3D printing) OR (3D printing service) OR (Lead generation)",
"default_field": "description",
"default_operator": "and"
}
}
]
}
}
}'
```
{% endcode %}
# Search Preview: Multi-source Company API
{% columns %}
{% column width="16.666666666666664%" %}
Data type:
Query type:
URL:
{% endcolumn %}
{% column %}
Multi-source Company
Elasticsearch DSL
{% endcolumn %}
{% endcolumns %}
***
## Overview
Retrieve a limited set of fields from top-matching records in real time, and search suggestion features. Here, Multi-source Company API search `/v2/company_multi_source/search/es_dsl/preview` endpoint's usage is reviewed.
Learn how to obtain Multi-source Company data using profile URLs or shorthand names
| Used key | Collect endpoints | Function |
| -------------- | ------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Company ID | */v2/company\_multi\_source/collect/{company\_id}* | Retrieved using Company search endpoints. |
| Shorthand name | */v2/company\_multi\_source/collect/{profile\_url/shorthand\_name}* | Use profile URLs or shorthand names, taken directly from the URL (e.g., *example-company* from [www.professional-network.com/company/example-company](http://www.professional-network.com/company/example-company)). |
***
## Collection using IDs
Examples in this article are prepared using Postman.
However, you can use the most convenient tool for you: terminal, Postman, or any API-compatible application.
### cURL (Postman)
Paste in the numeric company ID instead of `{company_id}` and API Key instead of `{API Key}` in the request template:
{% tabs %}
{% tab title="Full collect" %}
{% code title="cURL request template" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/company_multi_source/collect/{company_id}' \
-H "accept: application/json"
-H "apikey: {API Key}"
```
{% endcode %}
{% endtab %}
{% tab title="Field selection" %}
{% code title="cURL request example with several fields" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/company_multi_source/collect/{company_id}?fields=id&fields=company_name' \
-H "accept: application/json"
-H "apikey: {API Key}"
```
{% endcode %}
{% endtab %}
{% endtabs %}
## Collection using shorthand names
Examples in this article are prepared using Postman.
However, you can use the most convenient tool for you: terminal, Postman, or any API-compatible application.
### cURL (Postman)
Use the provided request template below. Enter a valid `profile_url` or `shorthand_name` value and your `API Key`.
{% tabs %}
{% tab title="Full collect" %}
{% code title="cURL request template" %}
```json
curl -X GET "https://api.coresignal.com/cdapi/v2/company_multi_source/collect/{profile_url/shorthand_name}"
-H "accept: application/json"
-H "apikey: {API Key}"
```
{% endcode %}
{% endtab %}
{% tab title="Field selection" %}
{% code title="cURL request example with several fields" %}
```json
curl -X GET "https://api.coresignal.com/cdapi/v2/company_multi_source/collect/{profile_url/shorthand_name}?fields=id&fields=company_name"
-H "accept: application/json"
-H "apikey: {API Key}"
```
{% endcode %}
{% endtab %}
{% endtabs %}
# Bulk Collect: Multi-source Company API
## Overview
Discover the Bulk Collect (Bulk API) capabilities and explore potential uses for efficiently retrieving company data in batches. Find all Bulk Collect related information in the following topic:
## Endpoints
| Request type | Endpoint |
| ------------ | ----------------------------------------------------------- |
| POST | */v2/data\_requests/company\_multi\_source/ids* |
| POST | */v2/data\_requests/company\_multi\_source/es\_dsl* |
| GET | */v2/data\_requests/{data\_request\_id}/files* |
| GET | */v2/data\_requests/{data\_request\_id}/files/{file\_name}* |
### Limiting returned record count
Include the parameter `"limit": int` to control the number of records returned by your queries in `/v2/data_requests/company_multi_source/es_dsl` endpoint.
**Request example to retrieve five records**
{% code title="Elasticsearch DSL example" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/data_requests/company_multi_source/es_dsl' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"webhook_url": "{optional_webhook_url}",
"limit": 5,
"es_dsl_query": {
"query": {
"bool": {
"must": [
{
"query_string": {
"query": "(3D printing) OR (3D printing service) OR (Lead generation)",
"default_field": "description",
"default_operator": "and"
}
}
]
}
}
}'
```
{% endcode %}
## Credits
Your credits for Multi-source Company API will also apply to Bulk Collect data collection requests.
Learn about the credits in Bulk Collect usage in the [general Bulk Collect](/api-introduction/requests/bulk-collect) topic.
## Rate limits
Bulk Collect endpoints have a limited number of requests allowed per second. Learn about [rate limits](/api-introduction/rate-limits) for Bulk Collect requests.
## Webhooks
POST endpoints allow you to add webhooks and get notified when your data request is ready.
{% hint style="info" %}
Keep in mind that `webhook_url` is **optional.**
{% endhint %}
{% tabs %}
{% tab title="Elasticsearch DSL template" %}
{% code title="Elasticsearch DSL template" %}
```json
{
"webhook_url": "{optional_webhook_url}",
"es_dsl_query": {}
}
```
{% endcode %}
{% endtab %}
{% tab title="IDs cURL template" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/data_requests/company_multi_source/ids' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: multipart/form-data' \
-F 'webhook_url={optional_webhook_url}'\
-F 'ids={list of ids}'
```
{% endtab %}
{% endtabs %}
# POST Requests: Multi-source Company API
{% columns %}
{% column width="16.666666666666664%" %}
Data type:
URLs:
{% endcolumn %}
{% column %}
Multi-source Company
{% endcolumn %}
{% endcolumns %}
***
## Overview
Bulk Collect features three POST endpoints, making collecting company data records in bulk easier.
{% hint style="danger" %}
Before you proceed with your Bulk Collect requests, test them in the Multi-source Company API first to avoid any unexpected costs.
{% endhint %}
Find step-by-step guides for making Bulk Collect POST requests in the following topic:
## Elasticsearch DSL requests
Use the endpoint `/v2/data_requests/company_multi_source/es_dsl` to request company data in bulk using our Elasticsearch DSL schema.
Learn how to obtain company data using website URLs.
| Used key | Collect endpoints |
| ----------- | ------------------------------------------------- |
| Website URL | */v2/company\_multi\_source/enrich?website={URL}* |
***
## Collection using websites
Examples in this article are prepared using Postman.
However, you can use the most convenient tool for you: terminal, Postman, or any API-compatible application.
### cURL (Postman)
Paste in the website URL instead of `{URL}` and API Key instead of `{API Key}` in the request template:
{% code title="Template" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/company_multi_source/enrich?website={URL}' \
-H 'accept: application/json' \
-H 'apikey: {API Key}'
```
{% endcode %}
Here is an example:
{% tabs %}
{% tab title="Full collect" %}
{% code title="Request example" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/company_multi_source/enrich?website=thermofisher.com' \
-H 'accept: application/json' \
-H 'apikey: {API Key}'
```
{% endcode %}
{% endtab %}
{% tab title="Fields selection" %}
{% code title="Request example" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/company_multi_source/enrich?website=thermofisher.com&fields=id&fields=company_name' \
-H 'accept: application/json' \
-H 'apikey: {API Key}'
```
{% endcode %}
{% endtab %}
{% endtabs %}
Collect company data from *Body*:
# Clean Company API
Clean Company API overview: endpoints, rate limits, and credits for accessing fresh, enriched company data via Search, Collect, and Bulk Collect.
## Overview
This section covers basic information on the Clean Company API.
To learn more about the API and its endpoints, follow the links below:
## Clean Company API endpoints
{% hint style="info" %}
Our API is a data retrieval tool. The endpoints do not support analytic features.
{% endhint %}
Clean Company API features **two search** endpoints and **three collect** endpoints to retrieve clean company data. Use the endpoints with any API-compatible application.
{% hint style="warning" %}
All Clean Company API requests must be made over HTTPS. Requests made over HTTP will fail or be redirected to HTTPS.
{% endhint %}
Clean Company API supports two types of requests:
* **Search** endpoints support POST requests only.
* **Collect** endpoints support GET requests only.
Collect data using website URLs and enrichment endpoint
10
\*📌 Full profile URL example: [www.professional-network.com/company/\*\*example-company\*\*.\\](http://www.professional-network.com/company/**example-company**.\\)
Shorthand name example: example-company.
### Bulk Collect
Bulk Collect (Bulk API) expands upon Clean Company API's functionality, featuring **four POST** and **two GET** endpoints. Bulk Collect allows you to search and collect company data in bulk using company IDs, Elasticsearch DSL queries and shorthand names or URLs.
| Request type | Endpoint |
| ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| POST | [*/v2/data\_requests/company\_clean/ids*](/company-api/clean-company-api/endpoints/bulk-collect/post-requests#ids-requests) |
| POST | [*/v2/data\_requests/company\_clean/es\_dsl*](/company-api/clean-company-api/endpoints/bulk-collect/post-requests#elasticsearch-dsl-requests) |
| POST | [*/v2/data\_requests/company\_clean/shorthand\_names*](/company-api/clean-company-api/endpoints/bulk-collect/post-requests#shorthand-names-and-urls-requests) |
| POST | [*/v2/data\_requests/company\_clean/urls*](/company-api/clean-company-api/endpoints/bulk-collect/post-requests#shorthand-names-and-urls-requests) |
| GET | */v2/data\_requests/{data\_request\_id}/files* |
| GET | */v2/data\_requests/{data\_request\_id}/files/{file\_name}* |
Read more about Bulk Collect in the following article:
# Data Dictionary: Clean Company API
Data dictionary for Clean Company API – field-level reference with cleaning details across fresh firmographics, funding, technologies, and updates.
Data dictionary for data retrieved using Clean Company API endpoints.
This data dictionary shows all available data fields, explains their values, and provides data samples from the Clean Company API data.
{% tabs %}
{% tab title="Data fields per category" %}
1. [Metadata](#metadata)
2. [Identifiers](#identifiers)
3. [Firmographics](#firmographics)
4. [Product and services overview](#product-and-services-overview)
5. [Contact information](#contact-information)
6. [Social media and websites](#social-media-and-websites)
7. [Location](#location)
8. [Funding information](#funding-information)
9. [Technologies](#technologies)
10. [Supporting fields](#supporting-fields)
11. [Company updates](#company-updates)
{% endtab %}
{% endtabs %}
{% hint style="info" %}
The data provided in the samples is strictly intended for illustrative purposes, allowing you to visualize its appearance and format.
{% endhint %}
## Metadata
| Data field | Processing | Description | Data type |
| -------------------------------- | ---------- | ---------------------------------------------------------- | ------------- |
| `last_updated` | Cleaned | Record update date | String (date) |
| `professional_network_source_id` | Raw | Record identification key assigned by Professional Network | String |
| `created_at` | Cleaned | Time and date when we created the company record | String (date) |
{% code title="Meta data" %}
```json
"created_at": "2019-04-07",
"last_updated": "2023-12-06",
"professional_network_source_id": "60191",
```
{% endcode %}
Cleaning actions
| Data field | Cleaning action |
| -------------- | ---------------------------------------------- |
| `last_updated` | Value is converted to the *yyyy-mm-dd* format. |
| `created_at` | Value is converted to the *yyyy-mm-dd* format. |
***
## Identifiers
| Data field | Processing | Description | Data type |
| ---------- | ---------- | ----------------------------------------------- | ---------------- |
| `id` | Raw | Company ID in our database | Number (integer) |
| `name` | Cleaned | Company name | String |
| `logo` | Cleaned | BASE64 encoded JPEG image of the company's logo | String |
| `ticker` | Cleaned | Company's stock ticker | String |
| `exchange` | Cleaned | Company's stock exchange | String |
{% code title="Identifiers" %}
```json
"id": 8039488,
"name": "Example Company",
"logo": "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",
"ticker": "DHCC",
"exchange": "NYSE",
```
{% endcode %}
Cleaning and enriching actions
| Data field | Cleaning/enriching action |
| ------------------- | -------------------------------------------------------------------------------------------------------------------- |
| `name` | Values *\["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"]* are replaced with value `None`. |
| `company_logo` | Image is resized to 50x50px. |
| `ticker`/`exchange` | Values *\["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"]* are replaced with value `None`. |
***
## Firmographics
| Data field | Processing | Description | Data type |
| ------------------------------- | ---------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `industry` | Cleaned | Industry the company operates in | String |
| `type` | Cleaned | Company type | String |
| `founded` | Cleaned | Company founding year | String |
| `size_range` | Cleaned | Company size range | String |
| `size_employees_count` | Enriched | The number of employees working in the company | Number (integer) |
| `size_employees_count_inferred` | Enriched | Estimated number of employees, calculated based on inferred employee data | Number (integer) |
| `followers` | Cleaned | The number of company followers | Number (integer) |
| `description` | Cleaned | Company description | String |
| `specialities` | Raw | Company specialties | Array of strings |
| `metadata_title` | Enriched | Company title parsed from additional sources | String |
| `metadata_description` | Enriched | Company description parsed from additional sources | String |
| `enriched_summary` | Enriched | LLM enriched company summary | String |
| `enriched_category` | Enriched | Company category assigned with LLM | String |
| `enriched_keywords` | Enriched | LLM enriched company keywords | Array of strings |
| `enriched_b2b` | Enriched |
Marks if the company offers B2B products/services enriched with the help of LLM 1 – B2B company 0 – not B2B company
| Number (double) |
{% code title="Firmographics" %}
```json
"type": "Privately Held",
"founded": "2011",
"followers": 1234,
"size_range": "11-50 employees",
"size_employees_count": 5,
"size_employees_count_inferred": 5,
"industry": "Unique industry",
"description": "Digital Example Company offers very important services.",
"specialities": [
"Example_1"
],
"enriched_summary": "Digital Example Company offers services.",
"enriched_keywords": [
"keyword_1",
"keyword_2"
],
"enriched_b2b": 0.0,
"enriched_category": "Example_2",
"metadata_title": "A great company for you",
"metadata_description": null,
```
{% endcode %}
Cleaning and enriching actions
| Data field | Cleaning/enriching action |
| ---------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `industry` | Values *\["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"]* are replaced with value `None`. |
| `type` | Values *\["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"]* are replaced with value `None`. |
| `founded` |
Values \["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None;
Values are replaced with None if the year is not between 500 and the current year.
|
| `followers` |
Values \["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value 0;
Every value is converted to an integer.
|
| `size_range` |
Some inconsistencies are fixed with overlapping values:
"1 employee" – "Myself Only";
"2-10 employees" – "1-10 employees";
"501-1,000 employees" – "501-1000 employees";
"1,001-5,000 employees" – "1001-5000 employees".
|
| `size_employees_count` | When `size_employees_count` is `0`, we check if we have any scraped profiles of employees working at this company. If yes, then we count how many employees are associated with it and change the value to that number. This can occur in cases when the public profile does not show some of the employees. |
| `industry` | Values *\["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"]* are replaced with value `None`. |
| `description` |
Values \["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None;
Value is replaced to None if the description is shorter than 3 characters;
Text styling tags removed;
Multiple spaces are replaced with single ones.
|
***
## Product and services overview
| Data field | Processing | Description | Data type |
| ---------------------- | ---------- | ---------------------------------------------------------- | --------- |
| `pricing_available` | Enriched | Marks if the company service pricing is available online | Boolean |
| `free_trial_available` | Enriched | Marks if the company offers a free trial of their services | Boolean |
| `demo_available` | Enriched | Marks if the company offers a demo | Boolean |
| `is_downloadable` | Enriched | Marks if the company offers a downloadable file/service | Boolean |
| `mobile_apps_exist` | Enriched | Marks if the company has mobile apps for their service | Boolean |
| `online_reviews_exist` | Enriched | Marks if the company has any online reviews | Boolean |
| `api_docs_exist` | Enriched | Marks if the company has API docs published | Boolean |
{% code title="Product and services overview" %}
```json
"pricing_available": false,
"free_trial_available": false,
"demo_available": false,
"is_downloadable": false,
"mobile_apps_exist": false,
"online_reviews_exist": false,
"api_docs_exist": false,
```
{% endcode %}
Enriching actions
| Data field | Enriching action |
| ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------- |
|
| Information is taken from the official company website. |
***
## Contact information
| Data field | Processing | Description | Data type |
| --------------- | ---------- | ---------------------------------------- | ---------------- |
| `phone_numbers` | Enriched | Publicly available company phone number | Array of strings |
| `emails` | Enriched | Publicly available company email address | Array of strings |
{% code title="Contact information" %}
```json
"phone_numbers": [
"0000 111 222"
],
"emails": [
"service@example-company.com"
],
```
{% endcode %}
Enriching actions
| Data field | Enriching action |
| ---------------------------------------------------------- | ---------------------------------------------------- |
|
phone\_numbers, emails
| Information taken from the official company website. |
***
## Social media and websites
| Data field | Processing | Description | Data type |
| ----------------------------------------- | ---------- | -------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `websites_main_original` | Raw | Company website URL | String |
| `websites_main` | Cleaned | Cleaned and resolved company website URL | String |
| `websites_resolved` | Enriched | Resolved company website URL | String |
| `websites_facebook` | Enriched | Company Facebook URL | String |
| `websites_twitter` | Enriched | Company Twitter URL | String |
| `websites_professional_network` | Raw | Professional network URL where the company was first discovered. It can be outdated if the company has changed its profile | String |
| `websites_professional_network_canonical` | Raw | The current official Professional network URL for the company, reflecting the most recent updates | String |
| `social_discord_urls` | Enriched | Company discord profile/channel | Array of strings |
| `social_facebook_urls` | Enriched | Company Facebook page | Array of strings |
| `social_instagram_urls` | Enriched | Company Instagram page | Array of strings |
| `social_professional_network_urls` | Enriched | Company professional network profile | Array of strings |
| `social_pinterest_urls` | Enriched | Company Pinterest page | Array of strings |
| `social_tiktok_urls` | Enriched | Company TikTok profile | Array of strings |
| `social_twitter_urls` | Enriched | Company Twitter profile | Array of strings |
| `social_x_urls` | Enriched | Company X profile | Array of strings |
| `social_youtube_urls` | Enriched | Company YouTube channel/profile | Array of strings |
| `social_github_urls` | Enriched | Company Github page/profile | Array of strings |
| `social_reddit_urls` | Enriched | Company Reddit profile | Array of strings |
{% tabs %}
{% tab title="Social media and websites" %}
{% code title="Social media and websites" %}
```json
"websites_main_original": "http://www.example-company.com.",
"websites_main": "https://example-company.com.",
"websites_facebook": "https://www.facebook.com/example-company",
"websites_twitter": "https://www.twitter.com/example-company",
"websites_professional_network": "https://www.professional-network.com/company/example-company",
"websites_professional_network_canonical": "https://www.professional-network.com/company/example-company",
```
{% endcode %}
{% endtab %}
{% tab title="Company social links" %}
{% code title="Company social links" %}
```json
"social_discord_urls": [
"https://discord.gg/example-company"
],
"social_facebook_urls": [
"https://www.facebook.com/example-company"
],
"social_instagram_urls": [
"https://www.instagram.com/example_company"
],
"social_professional_network_urls": [
"https://www.professional-network.com/company/example-company"
],
"social_pinterest_urls": [
"https://www.pinterest.com/example_company"
],
"social_tiktok_urls": [
"https://www.tiktok.com/@example_company"
],
"social_twitter_urls": [
"https://twitter.com/example_company"
],
"social_x_urls": [
"https://www.example-company-x.com"
],
"social_youtube_urls": [
"https://www.youtube.com/c/example-company"
],
"social_github_urls": [
"https://github.com/example-company"
],
"social_reddit_urls": [
"https://www.reddit.com/user/example_company"
]
```
{% endcode %}
{% endtab %}
{% endtabs %}
Cleaning and enriching actions
| Data field | Cleaning/enriching action |
| --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `websites_main` |
Every website\_main\_original URL is resolved;
Each URL we collect is parsed, parameters are removed and added to the websites\_main column.
Only one company can have a unique \.\/\.
Expired domains are removed.
|
| `websites_twitter` | If <*domain*> (from `websites_main`) == `twitter`, we move the URL value to `websites_twitter`. |
| `websites_facebook` | If <*domain*> (from `websites_main`) == `facebook`, we move the URL value to `websites_facebook`. |
| `websites_professional_network` | If <*domain*> (from `websites_main`) == `professional_network`, we move the URL value to `websites_professional_network`. |
|
| URLs taken from the official company website. |
***
## Location
| Data field | Processing | Description | Data type |
| --------------------------- | ---------- | ------------------------------------------------------------------------------------------------------- | ---------------- |
| `location_hq_raw_address` | Cleaned | Detailed company location | String |
| `location_hq_country` | Cleaned | Headquarters country | String |
| `location_hq_country_iso_2` | Enriched | ISO 2-letter country code for company HQ location | String |
| `location_hq_country_iso_3` | Enriched | ISO 3-letter country code for company HQ location | String |
| `location_hq_state` | Enriched | Company HQ state | String |
| `location_hq_city` | Enriched | Company HQ city | String |
| `location_hq_regions` | Enriched | Geographical region(s) the company is associated with based on the `company_location_hq_country` value. | String |
| **`locations_full`** | Raw | Full company location information | Array of objects |
| `location_address` | Raw | Company location address | String |
| `is_primary` | Raw | Marks if the listed location is the primary | Boolean |
| `city` | Enriched | Location city | String |
| `state` | Enriched | Location state | String |
| `country_code` | Enriched | Country code | String |
| `country` | Enriched | Country | String |
| `country_iso_2` | Enriched | ISO 2-letter code of the location country | String |
| `country_iso_3` | Enriched | ISO 3-letter code of the location country | String |
| **`regions`** | Enriched | Regions list | Struct |
| `region` | Enriched | Region | String |
{% code title="Location" %}
```json
"location_hq_raw_address": "Exampleville, CA, United States",
"location_hq_country": "United States",
"location_hq_country_iso_2": "US",
"location_hq_country_iso_3": "USA",
"location_hq_state": "CA",
"location_hq_city": "Exampleville",
"location_hq_regions": "[Northern America, Northern America, AMER]",
"locations_full": [
{
"location_address": "Sample St; Exampleville, CA, USA",
"is_primary": true,
"city": "Exampleville",
"state": "CA",
"country": "United States",
"country_iso_2": "US",
"country_iso_3": "USA",
"regions": [
{
"region": "Northern America"
}
}
],
```
{% endcode %}
Cleaning actions
| Data field | Cleaning action |
| ------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `location_hq_country` | Values *\["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"]* are replaced with value `None`. |
| `location_hq_raw_address` |
Values \["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None;
Special trailing characters trimmed;
Value location\_hq\_country added to the end of the string (separated by a comma).
|
***
## Funding information
| Data field | Processing | Description | Data type |
| ---------------------------- | ---------- | ------------------------------------------------------------------- | ---------------- |
| `funding_rounds` | | Information on company funding (rounds) | Array of objects |
| `last_round_investors_count` | Cleaned | The number of investors that participated in the last funding round | Number (integer) |
| `total_rounds_count` | Cleaned | Total number of funding rounds | Number (integer) |
| `last_round_type` | Cleaned | Last funding round type | String |
| `last_round_date` | Cleaned | Last funding round date | String |
| `last_round_money_raised` | Cleaned | Amount of money raised during the last funding round | Number (integer) |
| `financial_website_url` | Raw | Last funding round financial website URL | String |
{% code title="Funding information" %}
```json
"funding_rounds": [
{
"last_round_investors_count": 10,
"total_rounds_count": 5,
"last_round_type": "Series A",
"last_round_date": "2020-12-09",
"last_round_money_raised": 1230000,
"financial_website_url": "https://www.financial_website.com/funding_round/example"
}
]
}
```
{% endcode %}
Cleaning actions
| Data field | Cleaning action |
| ---------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `funding_rounds` |
Duplicate data fields filtered out;
Removed empty/irrelevant data fields.
|
| `last_round_investors_count` |
Values \["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value 0;
Every value is converted to an integer.
|
| `total_rounds_count` |
Values \["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value 0;
Every value is converted to an integer.
|
| `last_round_type` | Values *\["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"]* are replaced with value `0`. |
| `last_round_date` | Value is converted to the *yyyy-mm-dd* format. |
| `last_round_money_raised` |
Values \["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value 0;
Every value is converted to an integer (integer value is parsed from the text value).
|
***
## Technologies
| Data field | Processing | Description | Data type |
| ------------------- | ---------- | --------------------------------------------------------------- | ---------------- |
| `technologies` | - | Data type changed from `array of strings` to `array of structs` | Array of structs |
| `technology` | Enriched | Technology name | String |
| `first_verified_at` | Cleaned | Date this technology was first assigned to the company | String (date) |
| `last_verified_at` | Cleaned | Date this technology was last assigned to the company | String (date) |
{% code title="Technologies" %}
```json
"technologies_used": [
{
"technology": "React",
"first_verified_at": "2022-03-15",
"last_verified_at": "2024-10-15"
}
]
```
{% endcode %}
Enriching and cleaning actions
| Data field | Enriching action |
| -------------------------------------------------------------------------- | ----------------------------------------------- |
| `company_technologies` | Enriched by our ML model from multiple sources. |
|
first\_verified\_at last\_verified\_at
| Value is converted to the *yyyy-mm-dd* format. |
***
## Supporting fields
| Data field | Processing | Description | Data type |
| ---------------- | ---------- | ---------------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `expired_domain` | Enriched | Marks if the `company_websites_main_original` URL redirects to a domain dealer | Number (integer) |
| `unique_domain` | Enriched | Marks if only this company has the right to have this unique domain, e.g., `company_websites_main:` `https://ibm.com` | Number (integer) |
| `unique_website` | Enriched | Marks if only this company has a unique website but not necessarily a unique domain, e.g., `company_websites_main: https://ibm.com/generation` | Number (integer) |
{% code title="Supporting fields" %}
```json
"expired_domain": 0,
"unique_domain": 0,
"unique_website": 0,
```
{% endcode %}
***
## Company updates
| Data field | Processing | Description | Data type |
| ------------------------------- | ---------- | --------------------------------------------------------------------------------- | ---------------- |
| `updates` | | Company posts and related details | Array of objects |
| `urn` | Raw | String-based identifier | String |
| `followers` | Raw | Number of followers | String |
| `date` | Raw |
Post publish date (e.g., 1 month ago)
| String |
| `description` | Raw |
Published text
Note: may contain control characters
| String |
| `reactions_count` | Raw | Number of reactions on the post | Integer |
| `comments_count` | Raw | Number of comments on the post | Integer |
| `reshared_post_author` | Raw | Reshared post author | String |
| `reshared_post_author_url` | Raw | Author's profile URL | String |
| `reshared_post_author_headline` | Raw | Author's headline | String |
| `reshared_post_description` | Raw | Reshared post text | String |
| `reshared_post_followers` | Raw | The number of followers of the reshared post author | Integer |
| `reshared_post_date` | Raw | Date the reshared post was published (e.g., 1 month ago) | String |
{% code title="Company updates" %}
```json
"updates": [
{
"urn": "urn:pn:activity:0000000000000000000"
"followers": 1371,
"date": "1mo",
"description": "Example description",
"reactions_count": 22,
"comments_count": 2,
"reshared_post_author": "John Doe",
"reshared_post_author_url": "https://www.professional_network.com/john-doe",
"reshared_post_author_headline": "Co-Founder at Example Company, TEDx & Keynote Speaker",
"reshared_post_description": "Example description",
"reshared_post_followers": 45,
"reshared_post_date": "1mo"
}
]
```
{% endcode %}
# Sample: Clean Company API Data
Review a Clean Company API data sample – enriched firmographics, funding rounds, tech stack, social URLs, and regularly refreshed company metadata.
{% hint style="info" %}
All personal/company information mentioned within this context is entirely fictional and is solely intended for illustrative purposes.
{% endhint %}
## Clean Company API sample
{% code title="Clean Company API sample" %}
```json
{
"id": 1234567,
"name": "Company 123",
"type": "Private",
"founded": "2019",
"followers": 121000,
"websites_main_original": "http://www.company123.com",
"websites_main": "http://wwww.company123.com",
"websites_resolved": "https://www.company123.com",
"websites_facebook": "https://www.facebook.com/company123",
"websites_twitter": "https://www.twitter.com/company123",
"websites_professional_network": "https://www.professional-network.com/company/company123",
"websites_professional_network_canonical": "https://www.professional-network.com/company/company123",
"size_range": "51-200 employees",
"size_employees_count": 150,
"size_employees_count_inferred": 150,
"industry": "Software",
"description": "Company 123 provides innovative software solutions for businesses.",
"location_hq_raw_address": "Cityville, CA, USA",
"location_hq_country": "United States",
"location_hq_country_iso_2": "US",
"location_hq_country_iso_3": "USA",
"location_hq_state": "CA",
"location_hq_city": "Cityville",
"location_hq_regions": "[North America, AMER, Northern America]",
"created_at": "2005-06-15",
"last_updated": "2024-03-30",
"logo": "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",
"specialities": "Machine learning, Cloud Computing, AI, SaaS",
"ticker": "CMP123",
"exchange": "NASDAQ",
"expired_domain": 0,
"unique_domain": 1,
"unique_subdomain": 0,
"unique_website": 1,
"enriched_summary": "Company 123 specializes in AI-powered business tools.",
"enriched_keywords": [
"AI",
"Cloud",
"SaaS",
"Business Tools"
],
"enriched_b2b": 1.0,
"enriched_category": "Technology",
"metadata_title": "Company 123 - Leading AI Solutions",
"metadata_description": "Discover Company 123's innovative AI-powered business solutions.",
"phone_numbers":[
"+1-800-123-4567"
],
"emails": [
"contact@company123.com"
],
"social_discord_urls": ["https://discord.gg/company123"],
"social_facebook_urls": ["https://facebook.com/company123"],
"social_instagram_urls": ["https://instagram.com/company123"],
"social_professional_network_urls": ["https://www.professional-network.com/company/company123"],
"social_pinterest_urls": ["https://pinterest.com/company123"],
"social_tiktok_urls": ["https://tiktok.com/@company123"],
"social_twitter_urls": ["https://twitter.com/company123"],
"social_x_urls": ["https://x.com/company123"],
"social_youtube_urls": ["https://youtube.com/company123"],
"social_github_urls": ["https://github.com/company123"],
"social_reddit_urls": ["https://reddit.com/r/company123"],
"pricing_available": true,
"free_trial_available": true,
"demo_available": true,
"is_downloadable": false,
"mobile_apps_exist": true,
"online_reviews_exist": true,
"api_docs_exist": true,
"professional_network_source_id": "12345",
"funding_rounds": [
{
"last_round_investors_count": 3,
"total_rounds_count": 4,
"last_round_type": "Series B",
"last_round_date": "2022-08-10",
"last_round_money_raised": 50000000,
"financial_website_url": "https://www.financial_website.com/company123"
}
],
"locations_full": [
{
"location_address": "Sample St; Exampleville, CA, USA",
"is_primary": true,
"city": "Exampleville",
"state": "CA",
"country": "United States",
"country_iso_2": "US",
"country_iso_3": "USA",
"regions": [
{
"region": "Northern America"
}
}
],
"technologies": [
{
"technology": "AWS",
"first_verified_at": "2024-02-20",
"last_verified_at": "2025-03-30"
}
],
"updates": [
{
"urn": "urn:pn:activity:111133522751201281",
"followers": 120000,
"date": "2025-03-28",
"description": "Company 123 launches a new AI-powered tool",
"reactions_count": 503,
"comments_count": 102,
"reshared_post_author": "Jane Doe",
"reshared_post_author_url": "https://www.professional-network.com/janedoe",
"reshared_post_author_headline": "Tech Analyst",
"reshared_post_description": "Excited about Company 123's latest AI tool!",
"reshared_post_date": "1mo",
"reshared_post_followers": 124
}
]
}
```
{% endcode %}
# Endpoints: Clean Company API
Search and collect the relevant Clean Company Data using Clean Company API endpoints. The data was filtered, cleaned, and additionally enriched with the help of LLM, so it looks different from our regular Base Company API.
Information about enrichment endpoint usage and request examples.
# Elasticsearch DSL: Clean Company API
{% columns %}
{% column width="16.666666666666664%" %}
Data type:
Query type:
URL:
{% endcolumn %}
{% column %}
Clean Company
Elasticsearch DSL
{% endcolumn %}
{% endcolumns %}
***
## Overview
Use the `/v2/company_clean/search/es_dsl` endpoint to find company data matching your specifications.
General Elasticsearch DSL information and usage tips
Use the company collection endpoints to collect the company profile data using the company IDs, profile URLs or shorthand names (e.g., tesla from Professional network profile URL *[www.professional-network.com/company/tesla](http://www.professional-network.com/company/tesla)*).
Learn how to obtain Clean Company API data using profile URLs or shorthand names
### Company collection endpoints
| Used key | Collect endpoints | Function |
| -------------- | ----------------------------------------------------------- | --------------------------------------------------------------------------------------- |
| Company ID | */v2/company\_clean/collect/{company\_id}* | Collect Clean Company data using company IDs (retrieved using company search endpoints) |
| Shorthand name | */v2/company\_clean/collect/{profile\_url/shorthand\_name}* | Collect Clean Company data using profile URLs or shorthand names taken from the URLs |
## Collection using company IDs
Examples in this article are prepared using Postman.
However, you can use the most convenient tool for you: terminal, Postman, or any API-compatible application.
### cURL (Postman)
Paste in the numeric company ID instead of `{company_id}` and API Key instead of `{API Key}` in the request template:
{% tabs %}
{% tab title="Full collect" %}
{% code title="cURL request" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/company_clean/collect/{company_id}' \
-H 'accept: application/json' \
-H 'apikey: {API Key}'
```
{% endcode %}
{% endtab %}
{% tab title="Field selection" %}
{% code title="cURL request example with several fields" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/company_clean/collect/{company_id}?fields=id&fields=name' \
-H 'accept: application/json' \
-H 'apikey: {API Key}'
```
{% endcode %}
{% endtab %}
{% endtabs %}
## Collection using profile URLs or shorthand names
Examples in this article are prepared using Postman.
### cURL (Postman)
Use the provided request template below. Enter a valid `profile_url` or `shorthand_name` value and your `API Key`.
{% tabs %}
{% tab title="Full collect" %}
{% code title="cURL request" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/company_clean/collect/{profile_url/shorthand_name}' \
-H 'accept: application/json' \
-H 'apikey: {API Key}'
```
{% endcode %}
{% endtab %}
{% tab title="Field selection" %}
{% code title="cURL request example with several fields" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/company_clean/collect/{profile_url/shorthand_name}?fields=id&fields=name' \
-H 'accept: application/json' \
-H 'apikey: {API Key}'
```
{% endcode %}
{% endtab %}
{% endtabs %}
# Bulk Collect: Clean Company API
## Overview
Discover the Bulk Collect (Bulk API) capabilities and explore potential uses for efficiently retrieving company data in batches. Find all Bulk Collect related information in the following topic:
## Endpoints
Bulk Collect (Bulk API) is an extension of Clean Company API and includes **four POST** and **two GET** endpoints.
Collect company data in bulk using an company ID list, or Elasticsearch DSL schema that is already used in the Clean Comapny API.
| Request type | Endpoint |
| ------------ | ----------------------------------------------------------- |
| POST | */v2/data\_requests/company\_clean/ids* |
| POST | */v2/data\_requests/company\_clean/es\_dsl* |
| POST | */v2/data\_requests/company\_clean/shorthand\_names* |
| POST | */v2/data\_requests/company\_clean/urls* |
| GET | */v2/data\_requests/{data\_request\_id}/files* |
| GET | */v2/data\_requests/{data\_request\_id}/files/{file\_name}* |
### Limiting returned record count
Include the parameter `"limit": int` to control the number of records returned by your queries in `/v2/data_requests/company_clean/es_dsl` endpoints.
**Request example to retrieve five records**
{% tabs %}
{% tab title="Elasticsearch DSL example" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/data_requests/company_clean/es_dsl' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"webhook_url": "{optional_webhook_url}",
"limit": 5,
"es_dsl_query": '{
"query":{
"bool":{
"must":[
{
"query_string":{
"query":"2023",
"default_field":"founded",
"default_operator":"and"
}
}
]
}
}
}'
```
{% endtab %}
{% endtabs %}
## Credits
Your credits for Clean Company API will also apply to Bulk Collect data collection requests.
Learn about the credits in Bulk Collect usage in the [general Bulk Collect](/api-introduction/requests/bulk-collect#credits) topic.
## Rate limits
Bulk Collect endpoints have a limited number of requests allowed per second. Learn about [rate limits](/api-introduction/rate-limits) for Bulk Collect requests.
## Webhooks
POST endpoints allow you to add webhooks and get notified when your data request is ready.
{% hint style="info" %}
Keep in mind that `webhook_url` is **optional.**
{% endhint %}
{% tabs %}
{% tab title="Elasticsearch DSL template" %}
{% code title="Elasticsearch DSL template" %}
```json
{
"webhook_url": "{optional_webhook_url}",
"es_dsl_query": {}
}
```
{% endcode %}
{% endtab %}
{% tab title="IDs template" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/data_requests/company_clean/ids' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: multipart/form-data' \
-F 'webhook_url={optional_webhook_url}' \
-F 'ids={list of ids}'
```
{% endtab %}
{% endtabs %}
# POST Requests: Clean Company API
{% columns %}
{% column width="16.666666666666664%" %}
Data type:
URLs:
{% endcolumn %}
{% column %}
Clean Company
{% endcolumn %}
{% endcolumns %}
***
## Overview
Bulk Collect features three POST endpoints, making collecting company data records in bulk easier.
{% hint style="danger" %}
Before you proceed with your Bulk Collect requests, test them in the Clean Company API first to avoid any unexpected costs.
{% endhint %}
Find step-by-step guides for making Bulk Collect POST requests in the following topic:
## Elasticsearch DSL requests
Use the endpoint `/v2/data_requests/company_clean/es_dsl` to request company data in bulk using our Elasticsearch DSL schema:
Learn how to obtain company data using website URLs.
| Used key | Collect endpoints |
| ----------- | ----------------------------------------- |
| Website URL | */v2/company\_clean/enrich?website={URL}* |
***
## Collection using websites
Examples in this article are prepared using Postman.
However, you can use the most convenient tool for you: terminal, Postman, or any API-compatible application.
### cURL (Postman)
Paste in the website URL instead of `{URL}` and API Key instead of `{API Key}` in the request template:
{% code title="Template" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/company_clean/enrich?website={URL}' \
-H 'accept: application/json' \
-H 'apikey: {API Key}'
```
{% endcode %}
Here is an example:
{% tabs %}
{% tab title="Full collect" %}
{% code title="Request example" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/company_clean/enrich?website=thermofisher.com' \
-H 'accept: application/json' \
-H 'apikey: {API Key}'
```
{% endcode %}
{% endtab %}
{% tab title="Field selection" %}
{% code title="Request example with several fields" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/company_clean/enrich?website=thermofisher.com&fields=name&fields=created_at' \
-H 'accept: application/json' \
-H 'apikey: {API Key}'
```
{% endcode %}
{% endtab %}
{% endtabs %}
Collect company data from *Body*:
# Base Company API
Overview of the Base Company API: includes endpoints, rate limits, and accessing company data via Search, Collect, and Bulk Collect.
## Overview
This section covers general details about Base Company API.\
Follow the links inside the topics to learn more about the API and its endpoints.
## Base Company API endpoints
{% hint style="info" %}
Our API is a data retrieval tool. The endpoints do not support analytic features.
{% endhint %}
Base Company API features **four search** and **two collect** endpoints. Use the endpoints with any API-compatible application to retrieve Base Company data.
{% hint style="warning" %}
All Base Company API requests must be made over HTTPS. Requests made over HTTP will fail or be redirected to HTTPS.
{% endhint %}
Base Company API supports two types of requests:
* **Search** endpoints support POST requests only.
* **Collect** endpoints support the GET requests only.
Collect company data using profile URLs or shorthand names*
10
\*📌 Full profile URL example: \_[www.professional-network.com/company/example-company\_.\\](http://www.professional-network.com/company/example-company_.\\)
Shorthand name example: *example-company*.
### Bulk Collect
Bulk Collect (Bulk API) expands upon Base Company API's functionality, featuring **three POST** and **two GET** endpoints. Bulk Collect allows you to search and collect company data in bulk using company IDs, search filters, or Elasticsearch DSL queries.
| Request type | Endpoints |
| ------------ | ------------------------------------------------------------------------------------------------------------------------------------------- |
| POST | [*/v2/data\_requests/company\_base/id\_file*](/company-api/base-company-api/endpoints/bulk-collect/post-requests#id-file-requests) |
| POST | [*/v2/data\_requests/company\_base/filter*](/company-api/base-company-api/endpoints/bulk-collect/post-requests#search-filter-post-requests) |
| POST | [*/v2/data\_requests/company\_base/es\_dsl*](/company-api/base-company-api/endpoints/bulk-collect/post-requests#elasticsearch-dsl-requests) |
| GET | */v2/data\_requests/{data\_request\_id}/files* |
| GET | */v2/data\_requests/{data\_request\_id}/files/{file\_name}* |
{% hint style="success" %}
You can try out the Base Company API playground on Coresignal's Self-service
Try Self-service
{% endhint %}
Read more about Bulk Collect in the following article:
# Data Dictionary: Base Company API
Data dictionary for Base Company API – all fields explained across company info, funding rounds, stock data, locations, and fresh company updates.
## Overview
Data dictionary for data retrieved using Base Company API endpoints.
This data dictionary shows all available data fields, explains their values, and provides data samples from the Base Companies dataset. Full sample can be found [here](/company-api/base-company-api/sample-base-company-api-data).
{% tabs %}
{% tab title="Data fields per category" %}
1. [Company information](#company-information)
2. [Affiliated](#affiliated)
3. [Financial website info](#financial-website-info)
4. [Featured employees](#featured-employees)
5. [Investors](#investors)
6. [Funding rounds](#funding-rounds)
7. [Locations](#locations)
8. [Similar companies](#similar-companies)
9. [Specialties](#specialties)
10. [Stock info](#stock-info)
11. [Company updates](#company-updates)
{% endtab %}
{% tab title="Legacy data fields per category" %}
This dataset contains legacy tables that are either no longer filled or have low fill rates
1. [Also viewed](#also-viewed)
{% endtab %}
{% endtabs %}
{% hint style="info" %}
All personal/company information mentioned within this context is entirely fictional and is solely intended for illustrative purposes.
{% endhint %}
{% hint style="success" %}
A **null** value means that the information was not listed on the company's profile.
{% endhint %}
## Data fields
### Company information
Repeated fields
| Data field | Description | Data type |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------- |
| `id` |
Identification key for a company profile record. Each record in the dataset has its ID value. ID in the company table (root) becomes company\_id in the other tables of the dataset
| Number (integer) |
| `company_id` | Company record ID assigned in the database | Number (integer) |
| `created` | Timestamp of record creation | String |
| `last_updated` | Timestamp of record last update | String |
| `deleted` |
Marks if the record is available publicly. Two possible values: 1 – the last time we scraped the company page, the Page not found was returned; 0 – we successfully scraped the company profile
| Boolean/integer |
| Data field | Description | Data type |
| ------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `url` | Professional network URL where the company was first discovered. It can be outdated if the company has changed its profile | String |
| `hash` | Profile URL processed by the MD5 algorithm | String |
| `name` | Name | String |
| `website` | Website | String |
| `size` | Size category | String |
| `industry` | Associated industry | String |
| `description` | Description | String |
| `followers` | Profile follower count | Number (integer) |
| `founded` | Founding year | Number (integer) |
| `headquarters_city` |
Headquarters location (city)
Note: legacy field that is rarely filled
| String |
| `headquarters_country` |
Headquarters location (country)
Note: legacy field that is rarely filled
| String |
| `headquarters_state` |
Headquarters location (state)
Note: legacy field that is rarely filled
| String |
| `headquarters_street1` |
Headquarters location (street address)
Note: legacy field that is rarely filled
| String |
| `headquarters_street2` |
Headquarters location (street address)
Note: legacy field that is rarely filled
| String |
| `headquarters_zip` |
Headquarters location (zip code)
Note: legacy field that is rarely filled
| String |
| `logo_url` | Logo URL | String |
| `last_response_code` | Response code to the last scraping request | Integer |
| `type` | Company type | String |
| `headquarters_new_address` | Newly exposed company headquarters address | String |
| `employees_count` | Number of employees on the professional network who associated their experience with the company | Number (integer) |
| `headquarters_country_restored` |
Country the company is based in (from our restored database)
Note: legacy field that is rarely filled
| String |
| `headquarters_country_parsed` | Country the company is based in (as parsed by our in-house country parser) | String |
| `company_shorthand_name` | Dynamic part of the URL used to update the profile and identify companies | String |
| `company_shorthand_name_hash` | Shorthand name of the company processed by the MD5 algorithm | String |
| `canonical_url` | The current official Professional network URL for the company, reflecting the most recent updates | String |
| `canonical_hash` | `Canonical_url` processed by the MD5 algorithm | String |
| `canonical_shorthand_name` |
Dynamic part of a canonical\_url, used to identify companies (the part of the professional network URL that has the company name)
| String |
| `canonical_shorthand_name_hash` | Canonical shorthand name processed by the MD5 algorithm | String |
| `last_updated_ux` | Date and time when the record was last updated (Unix timestamp) | Number (integer) |
| `source_id` | In-house company ID (static) on the professional network | Number (integer) |
**Refer to the table example from the data:**
{% code title="Main company data" %}
```json
{
"id": 111371,
"url": "https://www.professional-network.com/company/luxury-store-brand",
"hash": "83627f1b6ccb868ce2450d63124a1562",
"name": "Luxury store brand",
"website": "http://www.luxury-store-brand.com",
"size": "1,001-5,000 employees",
"industry": "Retail Apparel and Fashion",
"description": "Luxury store brand is the go-to destination for chic, contemporary fashion. The brand evokes a mindset - an attitude, not an age. It's a true original, always defining fashion's next stride forward. Designed for the confident, sexy, modern woman, Luxury store brand is a global label that embodies a sensual, sophisticated lifestyle.\n\nChairman and Founder John Doe opened the first Luxury store brand boutique in 1980 in San Francisco. Recognizing a demographic that was neither junior nor bridge, Manny created the first contemporary fashion brand. Over 35 years later, Luxury store brand has established itself as one of the world's top fashion retailers.\n\nLuxury store brand STORES\n\nWithin its stores and on luxurystorebrand.com, Luxury store brand seeks to create an upscale, visually stimulating boutique environment while providing top-notch service to ensure an exceptional shopping experience. Stylists are knowledgeable, passionate about fashion and skilled at addressing clients' styling needs. Luxury store brand also offers a line of merchandise branded with the distinctive Luxury store brand logo for those who love to wear the Luxury store brand name.\n\nLuxury store brand markets its merchandise under the Luxury store brand and Luxury store brand outlet names through more than 200 retail stores, more than 100 international-licensee operated stores and luxury-store-brand.com. \n\nluxury-store-brand.com\nLaunched in 1998, luxury-store-brand.com continues the Luxury store brand store experience and provides a complete assortment of Luxury store brand merchandise to clients in the United States and internationally.",
"followers": 23288,
"founded": 1976,
"headquarters_city": null,
"headquarters_country": null,
"headquarters_state": null,
"headquarters_street1": null,
"headquarters_street2": null,
"headquarters_zip": null,
"logo_url": "https://media.prof-ntwk.com/dms/image/C560BAQFmmRv6bZ4u_A/company-logo_200_200/0/1519856046980?e=2147483647&v=beta&t=PihtlecPzQHcPSUJFPYlpCI_KxUnwqOvzJqWgkr7StY",
"created": "2016-06-17 20:08:21",
"last_updated": "2023-09-09 18:08:29",
"last_response_code": 200,
"type": "Public Company",
"headquarters_new_address": "Los Angeles, California",
"employees_count": 1246,
"headquarters_country_restored": "United States",
"headquarters_country_parsed": "United States",
"company_shorthand_name": "luxury-store-brand",
"company_shorthand_name_hash": "44584e20693d123abce7729e4a2b9acb",
"canonical_url": "https://www.professional-network.com/company/luxury-store-brand",
"canonical_hash": "83627f1b6ccb868ce2450d63124a1562",
"canonical_shorthand_name": "luxury-store-brand",
"canonical_shorthand_name_hash": "44584e20693d123abce7729e4a2b9acb",
"deleted": 0,
"last_updated_ux": 1694282909,
"source_id": 16753,
```
{% endcode %}
### Affiliated
{% hint style="info" %}
This category also includes data fields from [Repeated fields](#repeated-fields).
{% endhint %}
| Data field | Description | Data type |
| ------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `company_affiliated_collection` | Profiles of affiliated companies | Array of objects |
| `affiliated_company_url` | Affiliate company profile URL | String |
| `affiliated_company_id` |
Identification key relating to the company table for the affiliate company Note: legacy field
| Number (integer) |
**Refer to the table example from the data:**
{% code title="Afiiliated table" %}
```json
"company_affiliated_collection": [
{
"id": 735765,
"company_id": 7410562,
"affiliated_company_url": "https://www.professional-network.com/company/rental-company",
"affiliated_company_id": 7125453,
"created": "2020-02-04 08:32:23",
"last_updated": "2022-09-21 08:16:07",
"deleted": 1
}
],
```
{% endcode %}
### Also viewed
{% hint style="info" %}
This category also includes data fields from [Repeated fields](#repeated-fields).
{% endhint %}
{% hint style="warning" %}
A legacy table that is no longer filled.
{% endhint %}
| Data field | Description | Data type |
| -------------------------------- | --------------------------------------------------------------------------- | ---------------- |
| `company_also_viewed_collection` | Company profiles that were viewed in tandem with the record company profile | Array of objects |
| `viewed_company_url` | Company profile URL | String |
| `viewed_company_id` | Identification key relating to the `company` table | Number (integer) |
**Refer to the table example from the data:**
{% code title="Also viewed table" %}
```json
"company_also_viewed_collection": [
{
"id": 448146,
"company_id": 111371,
"viewed_company_url": "https://www.professional-network.com/company/fashion-brand",
"viewed_company_id": null,
"created": "2016-06-17 20:08:21",
"last_updated": "2019-11-14 01:07:39",
"deleted": 1
}
],
```
{% endcode %}
### Financial website info
{% hint style="info" %}
This category also includes data fields from [Repeated fields](#repeated-fields).
{% endhint %}
| Data field | Description | Data type |
| ------------------------------------------- | ---------------------------------------- | ---------------- |
| `company_financial_website_info_collection` | Company profile on the financial website | Array of objects |
| `financial_website_url` | Profile URL on the financial website | String |
**Refer to the table example from the data:**
{% code title="Financial website info" %}
```json
"company_financial_website_url_info_collection": [
{
"id": 258123,
"company_id": 111371,
"financial_website_url": "https://www.financial-website.com/organization/luxury-store-brand",
"created": "2023-02-06 14:06:55",
"last_updated": "2023-09-09 18:08:29",
"deleted": 0
}
],
```
{% endcode %}
### Featured employees
{% hint style="info" %}
This category also includes data fields from [Repeated fields](#repeated-fields).
{% endhint %}
| Data field | Description | Data type |
| --------------------------------------- | ---------------------------- | ---------------- |
| `company_featured_employees_collection` | List of associated employees | Array of objects |
| `url` | Employee profile URL | String |
**Refer to the table example from the data:**
{% code title="Featured employees table " %}
```json
"company_featured_employees_collection": [
{
"id": 26392978,
"company_id": 111371,
"url": "https://www.professional-network.com/john-doe",
"created": "2019-11-14 01:07:39",
"last_updated": "2020-02-22 12:17:32",
"deleted": 1
}
],
```
{% endcode %}
### Investors
{% hint style="info" %}
This category also includes data fields from [Repeated fields](#repeated-fields).
{% endhint %}
| Data field | Description | Data type |
| --------------------------------------- | --------------------------------------------------------------------------------------- | ---------------- |
| `company_featured_investors_collection` |
Consists of record metadata and company\_investors\_list objects
| Array of objects |
| `investor_id` | Identification key relating to the `company_investors_list` table | Number (integer) |
| `round_id` | Identification key relating to the `company_funding_rounds` table | Number (integer) |
| `company_investors_list` | List of company investors | Object |
| `name` | Investor's name | String |
| `hash` | Investor's name processed by the MD5 algorithm | String |
| `financial_website_url` | Investor's profile URL on the financial website | String |
**Refer to the table example from the data:**
{% code title="Featured investors table" %}
```json
"company_featured_investors_collection": [
{
"id": 812666,
"company_id": 111371,
"investor_id": 5311,
"round_id": 1822807,
"created": "2021-11-29 16:02:04",
"last_updated": "2021-12-28 09:06:11",
"deleted": 1,
"company_investors_list": {
"id": 5311,
"name": "The Investor Company",
"hash": "7b1fcd743ad2e20a8f72819749131678",
"financial_website_url": "https://www.financial-website.com/organization/the-investor-company",
"created": "2020-09-10 00:02:42",
"last_updated": "2022-02-07 10:32:06"
}
}
],
```
{% endcode %}
### Funding rounds
{% hint style="info" %}
This category also includes data fields from [Repeated fields](#repeated-fields).
{% endhint %}
| Data field | Description | Data type |
| ----------------------------------- | -------------------------------------------------------------- | ---------------- |
| `company_funding_rounds_collection` | Last funding round details | Array of objects |
| `last_round_investors_count` | Number of investors who have participated in the funding round | Number (integer) |
| `total_rounds_count` | Total number of completed funding rounds | Number (integer) |
| `last_round_type` | Last funding round type | String |
| `last_round_date` | Last funding round date | String |
| `last_round_money_raised` | Amount of money raised in the last funding round | String |
| `financial_website_url` | Funding round record URL on financial website | String |
**Refer to the table example from the data:**
{% code title="Funding rounds table" %}
```json
"company_funding_rounds_collection": [
{
"id": 2,
"last_round_investors_count": 0,
"total_rounds_count": 4,
"last_round_type": "Seed",
"last_round_date": "2016-06-01 00:00:00",
"last_round_money_raised": "US$ 224.2K",
"financial_website_url": "https://www.financial-website.com/funding_round/software-company-seed--92e35c7f",
"created": "2020-09-07 13:01:57",
"last_updated": "2020-09-26 16:29:54",
"deleted": 1
}
],
```
{% endcode %}
### Locations
{% hint style="info" %}
This category also includes data fields from [Repeated fields](#repeated-fields).
{% endhint %}
| Data field | Description | Data type |
| ------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------- |
| `company_locations_collection` | Company locations | Array of objects |
| `location_address` | Company address | String |
| `is_primary` |
Denotes if the location is the company's primary location 0 – not a primary location 1 – primary location
| Boolean |
**Refer to the table example from the data:**
{% code title="Locations table" %}
```json
"company_locations_collection": [
{
"id": 11612582,
"company_id": 111371,
"location_address": "Los Angeles, California, US",
"is_primary": 1,
"created": "2019-11-14 01:07:39",
"last_updated": "2023-09-09 18:08:29",
"deleted": 0
},
{
"id": 11612583,
"company_id": 111371,
"location_address": "New York, US",
"is_primary": 0,
"created": "2019-11-14 01:07:39",
"last_updated": "2023-09-09 18:08:29",
"deleted": 0
}
],
```
{% endcode %}
### Similar companies
{% hint style="info" %}
This category also includes data fields from [Repeated fields](#repeated-fields).
{% endhint %}
| Data field | Description | Data type |
| ---------------------------- | --------------------------------------- | ---------------- |
| `company_similar_collection` | Companies similar to the record company | Array of objects |
| `url` | Similar company profile URL | String |
**Refer to the table example from the data:**
{% code title="Similar table" %}
```json
"company_similar_collection": [
{
"id": 45310284,
"company_id": 111371,
"url": "https://www.professional-network.com/company/luxury-clothing-brand",
"created": "2019-11-14 01:07:39",
"last_updated": "2021-06-22 02:08:26",
"deleted": 0
}
],
```
{% endcode %}
### Specialties
{% hint style="info" %}
This category also includes data fields from [Repeated fields](#repeated-fields).
{% endhint %}
| Data field | Description | Data type |
| -------------------------------- | --------------------------- | ---------------- |
| `company_specialties_collection` | List of company specialties | Array of objects |
| `specialty` | Specialty | String |
**Refer to the table example from the data:**
{% code title="Specialties table" %}
```json
"company_specialties_collection": [
{
"id": 160305,
"company_id": 111371,
"specialty": "Luxury store brand stores",
"created": "2016-06-17 20:08:21",
"last_updated": "2022-07-25 04:42:17",
"deleted": 1
}
],
```
{% endcode %}
### Stock info
{% hint style="info" %}
This category also includes data fields from [Repeated fields](#repeated-fields).
{% endhint %}
| Data field | Filter name | Data type |
| ------------------------------- | -------------- | ---------------- |
| `company_stock_info_collection` | Stock details | Array of objects |
| `ticker` | Stock ticker | String |
| `exchange` | Stock exchange | String |
**Refer to the table example from the data:**
{% code title="Stock info table" %}
```json
"company_stock_info_collection": [
{
"id": 2789,
"company_id": 111371,
"ticker": "LSB",
"exchange": "OTCM",
"created": "2020-05-07 14:38:14",
"last_updated": "2020-07-19 09:39:38",
"deleted": 1
}
],
```
{% endcode %}
### Company updates
{% hint style="info" %}
This category also includes data fields from [Repeated fields](#repeated-fields).
{% endhint %}
| Data field | Description | Data type |
| ------------------------------- | --------------------------------------------------------------------------------- | ---------------- |
| `company_updates_collection` | Company's posts/updates on professional network | Array of objects |
| `urn` | String-based identifier | String |
| `followers` | Number of followers | Number (integer) |
| `date` |
Publish date (e.g., 1 month ago)
| String |
| `description` |
Published text
Note: may contain control characters
| String |
| `reactions_count` | Number of reactions on the post | Number (integer) |
| `comments_count` | Number of comments on the post | Number (integer) |
| `reshared_post_author` | Reshared post author | String |
| `reshared_post_author_url` | Profile URL of the reshared post author | String |
| `reshared_post_author_headline` | Headline of the reshared post author | String |
| `reshared_post_description` | Reshared post text | String |
| `reshared_post_followers` | The number of followers of the reshared post author | Number (integer) |
| `reshared_post_date` |
Date the reshared post was published (e.g., 1 month ago)
| String |
**Refer to the table example from the data:**
{% code title="Company updates table" %}
```json
"company_updates_collection": [
{
"id": 193,
"company_id": 12695930,
"urn": "urn:li:activity:6991335602751201281",
"followers": 1371,
"date": "1mo",
"description": "We are delighted to share with you our success at the Expo 2022. Prime Minister spent the highest time in the expo at our Virtual Reality Aircraft Simulator, took an entire sortie on the virtual ALH MK III, experienced it to the fullest. \n\nKudos to the entire team of the Tech Company for this remarkable achievement ! Hands down by far the best show we've ever had !",
"reactions_count": 22,
"comments_count": 2,
"reshared_post_author": "John Doe",
"reshared_post_author_url": "https://www.professional-network.com/john-doe",
"reshared_post_author_headline": "Co-Founder at Tech Company, VR/AR Training Facilitator, TEDx & Keynote Speaker",
"reshared_post_description": "We are delighted to share with you our success at the Expo 2022. Prime Minister spent the highest time in the expo at our Virtual Reality Aircraft Simulator, took an entire sortie on the virtual ALH MK III, experienced it to the fullest. \n\nKudos to the entire team of the Tech Company for this remarkable achievement ! Hands down by far the best show we've ever had !",
"reactions_count": 22,
"reshared_post_followers": 30,
"reshared_post_date": "1mo",
"last_updated": "2023-01-04 10:09:34",
"deleted": 1
}
],
```
{% endcode %}
# Sample: Base Company API Data
Explore a Base Company API data sample – firmographics, funding rounds, locations, specialties, and regularly refreshed company profile fields.
{% hint style="info" %}
All personal/company information mentioned within this context is entirely fictional and is solely intended for illustrative purposes.
{% endhint %}
```json
{
"id": 1001,
"industry": "Software",
"last_response_code": 200,
"last_updated": "2024-02-01 08:00:00",
"last_updated_ux": 1706774400,
"logo_url": "https://example.com/logo.png",
"name": "Example Company",
"size": "51-200",
"source_id": 999,
"type": "Private",
"url": "https://www.professional-network.com/company/example-company",
"website": "https://www.example.com",
"created": "2022-10-01 08:00:00",
"deleted": 0,
"description": "Example Company provides AI solutions for enterprise customers.",
"employees_count": 150,
"followers": 5000,
"founded": 2020,
"hash": "companyhash123abcdefg123456",
"headquarters_city": "Tech City",
"headquarters_country": "USA",
"headquarters_country_parsed": "United States",
"headquarters_country_restored": "United States",
"headquarters_new_address": "123 Innovation Drive",
"headquarters_state": "TX",
"headquarters_street1": "123 Innovation Drive",
"headquarters_street2": "Suite 200",
"headquarters_zip": "75001",
"company_shorthand_name": "exampleco",
"company_shorthand_name_hash": "hash789xyz789xyz789abc789def",
"canonical_hash": "abc123hash789xyz789abc789def",
"canonical_shorthand_name": "exampleco",
"canonical_shorthand_name_hash": "hash789xyz789abc789def",
"canonical_url": "https://www.example.com",
"company_affiliated_collection": [
{
"affiliated_company_id": 2001,
"affiliated_company_url": "https://www.professional-network.com/company/affiliate-example",
"company_id": 1001,
"created": "2023-01-01 10:00:00",
"deleted": 0,
"id": 1,
"last_updated": "2024-01-01 10:00:00"
}
],
"company_also_viewed_collection": [
{
"company_id": 1001,
"created": "2023-05-01 12:00:00",
"deleted": 0,
"id": 2,
"last_updated": "2024-01-02 15:30:00",
"viewed_company_id": "3001",
"viewed_company_url": "https://www.professional-network.com/company/viewedco-example"
}
],
"company_financial_website_url_info_collection": [
{
"financial_website_url": "https://www.financial-website.com/organization/example-company",
"company_id": 1001,
"created": "2023-02-01 08:30:00",
"deleted": 0,
"id": 3,
"last_updated": "2024-01-05 11:00:00"
}
],
"company_featured_employees_collection": [
{
"company_id": 1001,
"created": "2023-03-10 09:00:00",
"deleted": 0,
"id": 4,
"last_updated": "2024-01-10 14:00:00",
"url": "https://www.professional-network.com/john-doe"
}
],
"company_featured_investors_collection": [
{
"company_id": 1001,
"company_investors_list": {
"financial_website_url": "https://www.financial-website.com/organization/fake-investor",
"created": "2023-06-15 10:00:00",
"hash": "investorhash001",
"id": 10,
"last_updated": "2024-01-15 16:00:00",
"name": "Fake Investor Capital"
},
"created": "2023-06-15 10:00:00",
"deleted": 0,
"id": 5,
"investor_id": 5001,
"last_updated": "2024-01-15 16:00:00",
"round_id": 6001
}
],
"company_funding_rounds_collection": [
{
"financial_website_url": "https://www.financial-website.com/funding_round/example-seed",
"company_id": 1001,
"created": "2022-11-20 13:45:00",
"deleted": 0,
"id": 6,
"last_round_date": "2023-10-10",
"last_round_investors_count": 3,
"last_round_money_raised": "$2,000,000",
"last_round_type": "Seed",
"last_updated": "2024-01-20 10:00:00",
"total_rounds_count": 2
}
],
"company_locations_collection": [
{
"company_id": 1001,
"created": "2022-12-01 10:00:00",
"deleted": 0,
"id": 7,
"is_primary": 1,
"last_updated": "2024-01-22 12:00:00",
"location_address": "123 Innovation Drive, Tech City, TX 75001"
}
],
"company_similar_collection": [
{
"company_id": 1001,
"created": "2023-07-01 10:00:00",
"deleted": 0,
"id": 8,
"last_updated": "2024-01-25 10:00:00",
"url": "https://www.professional-network.com/company/similar-example"
}
],
"company_specialties_collection": [
{
"company_id": 1001,
"created": "2023-08-01 09:00:00",
"deleted": 0,
"id": 9,
"last_updated": "2024-01-30 11:00:00",
"specialty": "Artificial Intelligence"
}
],
"company_stock_info_collection": [
{
"id": 2789,
"company_id": 111371,
"ticker": "LSB",
"exchange": "OTCM",
"created": "2020-05-07 14:38:14",
"last_updated": "2020-07-19 09:39:38",
"deleted": 1
}
],
"company_updates_collection": [
{
"comments_count": 15,
"company_id": 1001,
"date": "2mo",
"deleted": 0,
"description": "Example Company launched its new AI platform.",
"followers": 1200,
"id": 10,
"last_updated": "2024-02-01 14:00:00",
"reactions_count": 300,
"reshared_post_author": "John Doe",
"reshared_post_author_headline": "Tech Influencer",
"reshared_post_author_url": "https://www.professsional-betwork.com/johndoe",
"reshared_post_date": "2mo",
"reshared_post_description": "Exciting innovation from Example Company!",
"reshared_post_followers": 1000,
"urn": "urn:li:activity:example"
}
]
}
```
# Endpoints: Base Company API
You can find more information on the specific endpoints in the dedicated articles:
Information about Bulk Collect endpoints usage and request examples.
# Search Filters: Base Company API
{% columns %}
{% column width="16.666666666666664%" %}
Data type:
Query type:
URL:
{% endcolumn %}
{% column %}
Base Company
Coresignal's custom filters
{% endcolumn %}
{% endcolumns %}
***
## Overview
Use the endpoint to discover company IDs. It offers a less complex user experience than the `/v2/company_base/search/es_dsl` endpoint.
Explore the available filters, their potential applications, and helpful tips.
If you prefer uncomplicated queries, opt for this endpoint. Below are details about filter explanations and how to use them.
## Endpoint structure
{% code title="Full structure" %}
```json
{
"name": "string",
"website": "string",
"exact_website": "string",
"size": "string",
"industry": "string",
"country": "string",
"location": "string",
"created_at_gte": "string",
"created_at_lte": "string",
"last_updated_gte": "string",
"last_updated_lte": "string",
"deleted": true,
"employees_count_gte": 0,
"employees_count_lte": 0,
"source_id": 0,
"founded_year_gte": 0,
"founded_year_lte": 0,
"funding_total_rounds_count_gte": 0,
"funding_total_rounds_count_lte": 0,
"funding_last_round_type": "string",
"funding_last_round_date_gte": "string",
"funding_last_round_date_lte": "string"
}
```
{% endcode %}
## Filter list
{% hint style="info" %}
All personal/company information mentioned within this context is entirely fictional and is solely intended for illustrative purposes.
***
Example outputs are redacted and may not contain all the fields you would receive using the endpoint.
{% endhint %}
name
| Filter name | Data input type | Description | Usage |
| ----------- | --------------- | ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `name` | String | Company name |
Find company records using the company name or parts of it. Available operators:
AND (both keywords need to be present)
OR (one or the other keyword is present).
|
{% hint style="success" %}
You can search for company records using a part of the name.
For instance, entering `IT` will return results with companies such as `Backbone IT Consulting Inc.` and `Brothers IT Limited`.
{% endhint %}
**Example input:**
{% code title="name" %}
```json
{
"name": "IT"
}
```
{% endcode %}
{% code title="OR operator" %}
```json
{
"name": "(IT Consulting) OR (IT Security)"
}
```
{% endcode %}
{% code title="AND operator" %}
```json
{
"name": "(IT Consulting) AND (IT Security)"
}
```
{% endcode %}
{% hint style="success" %}
Use `"(first phrase) OR (second phrase)"` if you are searching for words in a phrase and want them to be interpreted together.
{% endhint %}
**Example output:**
{% code title="First example" %}
```json
{
"id": 482,
"name": "IT Consulting Inc.",
}
```
{% endcode %}
website
| Filter name | Data input type | Description | Usage |
| ----------- | --------------- | ----------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `website` | String | Company's website |
Search for company records using the company's website. Possible URL formats include: microsoft.com
Search for company records using the (exact) company website. Possible URL formats: microsoft.com
[www.microsoft.com](http://www.microsoft.com)
|
**Example input:**
{% code title="exact\_website" %}
```json
{
"name": "IT Company",
"exact_website": "www.it-company.com"
}
```
{% endcode %}
**Example output:**
{% code title="Example" %}
```json
"name": "IT Company",
"website": "http://www.it-company.com",
```
{% endcode %}
size
| Filter name | Data input type | Description | Usage |
| ----------- | --------------- | --------------------------------- | ------------------------------------------ |
| `size` | String | Company size (based on headcount) | Find company profiles based on their size. |
Possible input values can be found in general [Search filters](/api-introduction/requests/search-filters#possible-input-values) topic.
industry
| Filter name | Data input type | Description | Usage |
| ----------- | --------------- | ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `industry` | String | Associated industry |
Use the associated industry values to find company profiles. Search based on the available industry list. Available operators:
AND (both keywords need to be present)
OR (one or the other keyword is present in the data).
|
{% hint style="success" %}
Use `"(first phrase) OR (second phrase)"` if you are searching for words in a phrase and want them to be interpreted together.
{% endhint %}
{% code title="Phrases" %}
```json
{
"industry": "(Information technology) OR Internet"
}
```
{% endcode %}
Possible input values can be found in general [Search filters](/api-introduction/requests/search-filters#possible-input-values) topic.
country
| Filter name | Data input type | Description | Usage |
| ----------- | --------------- | ---------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
| `country` | String | Country where the company is based |
Use any of the possible country values to find company records. Available operators: \* OR (one of the two input values)
|
{% hint style="info" %}
Use `"(first phrase) OR (second phrase)"` if you are searching for words in a phrase and want them to be interpreted together.
{% endhint %}
{% code title="Phrases" %}
```json
{
"country": "(United Kingdom) OR Germany"
}
```
{% endcode %}
Possible input values can be found in general [Search filters](/api-introduction/requests/search-filters#possible-input-values) topic.
location
| Filter name | Data input type | Description | Usage |
| ----------- | --------------- | ---------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `location` | String | Company location |
Use the following values to search for company records:
continent (e.g., North America)
country (e.g., United States)
state (e.g., Florida)
city (e.g., Jacksonville)
village (e.g., Palmetto bay).
Available operators:
AND – both keywords need to be present;
OR – one or the other keyword is present.
|
**Example input:**
{% code title="location" %}
```json
{
"location": "United States, Florida"
}
```
{% endcode %}
**Example output:**
{% code title="Output" %}
```json
"headquarters_new_address": "Palmetto Bay, Miami, Florida, United States",
```
{% endcode %}
{% hint style="info" %}
Use `"\"{keyword}\""` format to find exact matches.
{% endhint %}
{% code title="Exact match" %}
```json
{
"location": "\"United States\" OR Germany"
}
```
{% endcode %}
created_at_gte
| Filter name | Data input type | Description | Usage |
| ---------------- | --------------- | --------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `created_at_gte` | String | Date and time when the record was created in our database |
Find company records based on the creation date. Use the YYYY-MM-DD hh:mm:ss date format. The output value will be greater than or equal to the input value.
|
**Example input:**
{% code title="created\_at\_gte" %}
```json
{
"created_at_gte": "2021-06-26 12:21:01"
}
```
{% endcode %}
**Example output:**
{% code title="Example" %}
```json
"created": "2021-06-26 12:21:06",
```
{% endcode %}
created_at_lte
| Filter name | Data input type | Description | Usage |
| ---------------- | --------------- | --------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `created_at_lte` | String | Date and time when the record was created in our database |
Find company records based on the creation date. Use the YYYY-MM-DD hh:mm:ss date format. The output data will be less than or equal to the input value.
|
**Example input:**
{% code title="created\_at\_lte" %}
```json
{
"created_at_lte": "2021-06-26 12:21:01"
}
```
{% endcode %}
**Example output:**
{% code title="Example" %}
```json
"created": "2016-06-17 14:59:01",
```
{% endcode %}
last_updated_gte
| Filter name | Data input type | Description | Usage |
| ------------------ | --------------- | ---------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `last_updated_gte` | String | Date and time when the record was last updated |
Find company records based on the last update date. Use the YYYY-MM-DD hh:mm:ss date format. The output value will be greater than or equal to the input value.
|
**Example input:**
{% code title="last\_updated\_gte" %}
```json
{
"last_updated_gte": "2021-02-26 12:41:01"
}
```
{% endcode %}
**Example output:**
{% code title="Example" %}
```json
"last_updated": "2023-05-21 17:58:40",
"last_response_code": 200,
```
{% endcode %}
last_updated_lte
| Filter name | Data input type | Description | Usage |
| ------------------ | --------------- | ---------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `last_updated_lte` | String | Date and time when the record was last updated |
Find company records based on the last update date. Use the YYYY-MM-DD hh:mm:ss date format. The output value will be less than or equal to the input value.
Use it to see company records that are deleted or private. Use the value true to include deleted/private profiles or false to exclude such profiles from your search.
Tip: Combine with other filters using relevant keywords.
Find company records based on the funding rounds in the company's financial history. The output value will be greater than or equal to the input value.
Find company records based on the company's last funding round. Use any of the possible input values.
|
Possible input values can be found in general [Search filters](/api-introduction/requests/search-filters#possible-input-values) topic.
funding_last_round_date_gte
| Filter name | Data input type | Description | Usage |
| ----------------------------- | --------------- | ----------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `funding_last_round_date_gte` | String | Last funding round date |
Use the last funding round date to find relevant company records. Use the yyyy-mm-dd date format. The output value will be greater than or equal to the input value.
Use the last funding round date to find relevant company records. Use the yyyy-mm-dd date format. The output value will be less than or equal to the input value.
## Using pagination in cURL requests
{% hint style="info" %}
This tutorial requires prior knowledge of how to compile and execute POST requests in Base Company API.
{% endhint %}
Use parameter `x-next-page-after` to retrieve a second page of IDs.
1. Navigate to the **Headers** section and click it:
2. Find the following information:\
– `x-next-page-after` \
– `x-total-pages`\
\
\
– `x-total-results`
3. Add parameter `?after={x-next-page-after}` to the POST request to see the next results page:
{% code title="Pagination example" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/company_base/search/filter?after="2025-03-17 13:23:06",4680650' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"industry": "Software Development",
"country": "United States",
"founded_year_lte": 2022
}'
```
{% endcode %}
4. Execute the request, and you will see the next page in the **Body** section:
```json
[
1000,
1001,
3000,
4004
]
```
## Limiting search results per page
Query parameter `?items_per_page={int}` allows you to specify the number of results retrieved per Search results page. The current limit is 1,000. Thus, this parameter lets you set a smaller limit value for the results page.
{% code title="Items per page" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/company_base/search/filter?items_per_page=20' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"industry": "Analyst",
"country": "Germany",
"founded_year_lte": 2023
}'
```
{% endcode %}
# Elasticsearch DSL: Base Company API
Elasticsearch DSL endpoint for Base Company API. Includes the full schema, sample queries, and sorting options for retrieving fresh company data.
{% columns %}
{% column width="16.666666666666664%" %}
Data type:
Query type:
URL:
{% endcolumn %}
{% column %}
Base Company
Elasticsearch DSL
{% endcolumn %}
{% endcolumns %}
***
## Overview
Use the `/v2/company_base/search/es_dsl` endpoint for more sophisticated queries and retrieve company data.
General Elasticsearch DSL information and usage tips
## Using pagination in cURL requests
{% hint style="info" %}
This tutorial requires prior knowledge of how to compile and execute POST requests in Base Company API.
{% endhint %}
Use parameter `x-next-page-after` to retrieve a second page of IDs.
1. Navigate to the **Headers** section and click it:
2. Find the following information:\
– `x-next-page-after`\
\
\
\
\
– `x-total-pages`\
\
– `x-total-results`
3. Add parameter `?after={x-next-page-after}` to the POST request to see the next results page
4. Execute the request, and you will see the next page in the **Body** section:
```json
[
1000,
1001,
3000,
4004
]
```
### Pagination using sorting
**Pagination usage example (cURL request in Postman)**
1. Add parameter `?after={x-next-page-after}` to the POST request:\
Refer to the example below for the exact parameter placement:
{% code title="Pagination (sorted by id)" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/company_base/search/es_dsl?after=33468751' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"query": {
"bool": {
"must": [
{
"query_string": {
"query": "(3D printing) OR (3D printing service) OR (Lead generation)",
"default_field": "description",
"default_operator": "and"
}
}
]
}
},
"sort": [
"id"
]
}'
```
{% endcode %}
**Send** the request, and you will see the next page in the **(Response) Body**.
***
Pagination using **score** sorting has a different ID format. The format difference is seen by the `x-next-page-after` parameter, showing the **score**, the **last updated** date, and the **last ID** on the page.
**Pagination usage example (cURL request in Postman)**
Add parameter `?after={x-next-page-after}` to the POST request to see the next results page. Refer to the example below for the exact parameter placement:
{% code title="Pagination (sorted by score)" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/company_base/search/es_dsl?after=28.772884,"2025-03-09 21:49:52",89757124' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"query": {
"bool": {
"must": [
{
"query_string": {
"query": "(3D printing) OR (3D printing service) OR (Lead generation)",
"default_field": "description",
"default_operator": "and"
}
}
]
}
},
"sort": [
"_score"
]
}'
```
{% endcode %}
**Send** the request, and you will see the next page in the **(Response) Body**.
## Limiting search results per page
Query parameter `?items_per_page={int}` allows you to specify the number of results retrieved per Search results page. The current limit is 1,000. Thus, this parameter lets you set a smaller limit value for the results page.
{% code title="Items per page" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/company_base/search/es_dsl?items_per_page=100' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"query": {
"bool": {
"must": [
{
"query_string": {
"query": "(3D printing) OR (3D printing service) OR (Lead generation)",
"default_field": "description",
"default_operator": "and"
}
}
]
}
}
}'
```
{% endcode %}
# Search Preview: Base Company API
{% columns %}
{% column width="16.666666666666664%" %}
Data type:
Query type:
URLs:
{% endcolumn %}
{% column %}
Base Company
Coresignal's custom filters and Elasticsearch DSL
{% endcolumn %}
{% endcolumns %}
***
## Overview
Retrieve a limited set of fields from top-matching records in real time, and search suggestion features. Here, Base Company API search `/v2/company_base/search/filter/preview` and `/v2/company_base/search/es_dsl/preview` endpoints' usage is reviewed.
Learn how to obtain Base Company data using profile URLs or shorthand names
| Key | Collect endpoint | Function |
| -------------- | ---------------------------------------------------------- | -------------------------------------------------------------------------------------- |
| Company ID | */v2/company\_base/collect/{company\_id}* | Collect Base Company data using company IDs (retrieved using company search endpoints) |
| Shorthand name | */v2/company\_base/collect/{profile\_url/shorthand\_name}* | Collect Base Company data using profile URLs or shorthand names taken from the URLs |
## Collection using IDs
Examples in this article are prepared using Postman.
However, you can use the most convenient tool: terminal, Postman, or any API-compatible application.
### cURL (Postman)
Use the provided request template below. Enter a valid `company_id` value and your `API Key`.
{% tabs %}
{% tab title="Full collect" %}
{% code title="cURL request" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/company_base/collect/{company_id}' \
-H 'accept: application/json' \
-H 'apikey: {API Key}'
```
{% endcode %}
{% endtab %}
{% tab title="Field selection" %}
{% code title="cURL request example with several fields" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/company_base/collect/{company_id}?fields=id&fields=name&fields=created' \
-H 'accept: application/json' \
-H 'apikey: {API Key}'
```
{% endcode %}
{% endtab %}
{% endtabs %}
## Collection using profile URLs or shorthand names
Examples in this article are prepared using Postman.
However, you can use the tool that is most convenient for you: terminal, Postman, or any API-compatible application.
### cURL (Postman)
Use the provided request template below. Enter a valid `profile_url` or `shorthand_name` value and your `API Key`.
{% tabs %}
{% tab title="Full collect" %}
{% code title="cURL request example" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/company_base/collect/{profile_url/shorthand_name}' \
-H 'accept: application/json' \
-H 'apikey: {API Key}'
```
{% endcode %}
{% endtab %}
{% tab title="Field selection" %}
{% code title="cURL request example with several fields" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/company_base/collect/{profile_url/shorthand_name}?fields=id&fields=name&fields=created' \
-H 'accept: application/json' \
-H 'apikey: {API Key}'
```
{% endcode %}
{% endtab %}
{% endtabs %}
# Bulk Collect: Base Company API
## Overview
Discover the Bulk Collect (Bulk API) capabilities and explore potential uses for efficiently retrieving company data in batches. Find all Bulk Collect related information in the following topic:
## Bulk collect endpoints
Bulk Collect is an extension of Base Company API and includes **three POST** and **two GET** endpoints.
Collect company data in bulk using an ID list, search filters, or Elasticsearch DSL schema that is already used in the Base Company API.
| Request type | Endpoint |
| ------------ | ----------------------------------------------------------- |
| POST | */v2/data\_requests/company\_base/id\_file* |
| POST | */v2/data\_requests/company\_base/filter* |
| POST | */v2/data\_requests/company\_base/es\_dsl* |
| GET | */v2/data\_requests/{data\_request\_id}/files* |
| GET | */v2/data\_requests/{data\_request\_id}/files/{file\_name}* |
### Limiting the returned record count
Include the parameter `"limit": int` to control the number of records returned by your queries in `/v2/data_requests/company_base/filter` and `/v2/data_requests/company_base/es_dsl` endpoints.
**Request example to retrieve five records**
{% tabs %}
{% tab title="Search filters example" %}
{% code title="Search filters example" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/data_requests/company_base/filter' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"webhook_url": {optional_webhook_url}",
"limit": 5,
"filters": {
"industry": "Information technology",
"created_at_gte": "2018-01-01 00:00:01",
"last_updated_gte": "2022-05-26 00:00:01"
}
}'
```
{% endcode %}
{% endtab %}
{% tab title="Elasticsearch DSL example" %}
{% code title="Elasticsearch DSL example" %}
```json
curl -X 'POST' \
'https: //api.coresignal.com/cdapi/v2/data_requests/company_base/es_dsl' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"webhook_url": "{optional_webhook_url}",
"limit": 5,
"es_dsl_query": {
"query": {
"bool": {
"must": [
{
"query_string": {
"query": "(3D printing) OR (3D printing service) OR (Lead generation)",
"default_field": "description",
"default_operator": "and"
}
}
]
}
}
}
}'
```
{% endcode %}
{% endtab %}
{% endtabs %}
## Credits
Your credits for Base Company API will also apply to Bulk Collect data collection requests.
Learn about the credits in Bulk Collect usage in the [general Bulk Collect](/company-api/base-company-api#credits) topic.
## Rate limits
Bulk Collect endpoints have a limited number of requests allowed per second. Learn about [rate limits](/api-introduction/rate-limits) for Bulk Collect requests.
## Webhooks
POST endpoints allow you to add webhooks and get notified when your data request is ready.
{% hint style="info" %}
Keep in mind that the `webhook_url` parameter is optional
{% endhint %}
{% tabs %}
{% tab title="ID file cURL template" %}
{% code title="ID file cURL template" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/data_requests/company_base/id_file' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: multipart/form-data' \
-F 'ids_file=@id_list_example.csv;type=text/csv' \
-F 'webhook_url={optional_webhook_url}'
```
{% endcode %}
{% endtab %}
{% tab title="Search Filter template" %}
{% code title="Search Filter template" %}
```json
{
"webhook_url": "{optional_webhook_url}",
"filters": {}
}
```
{% endcode %}
{% endtab %}
{% tab title="Elasticsearch DSL template" %}
{% code title="Elasticsearch DSL template" %}
```json
{
"webhook_url": "{optional_webhook_url}",
"es_dsl_query": {}
}
```
{% endcode %}
{% endtab %}
{% endtabs %}
# POST Requests: Base Company API
{% columns %}
{% column width="16.666666666666664%" %}
Data type:
URLs:
{% endcolumn %}
{% column %}
Base Company
{% endcolumn %}
{% endcolumns %}
***
## Overview
Bulk Collect features three POST endpoints, making collecting company data records in bulk easier.
{% hint style="danger" %}
Before you proceed with your Bulk Collect requests, test them in the Base Company API first to avoid any unexpected costs.
{% endhint %}
This step is important because these requests can potentially be quite expensive.
By doing this, you can see how many records your query will return and understand the credits the Bulk Collect query will require.
Find step-by-step guides of making Bulk Collect POST requests in the following topic:
### Endpoint usage example
{% code title="Example request" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/data_requests/company_base/es_dsl' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"data_format": "json",
"webhook_url": "{optional_webhook_url}",
"limit": {optional_integer}
"es_dsl_query": {
"query": {
"bool": {
"must": [
{
"query_string": {
"query": "(3D printing) OR (3D printing service) OR (Lead generation)",
"default_field": "description",
"default_operator": "and"
}
}
]
}
}
}
}'
```
{% endcode %}
Retrieve the `request_id` from the response body:
{% code title="Request ID" %}
```json
{
"request_id": "433869ec-0a98-4dcd-9b13-db4df58260f5"
}
```
{% endcode %}
* `Location` response header provides a URL where the results can be retrieved.
> Location: /v2/data\_requests/e000b0ec-0f00-0b00-0a0a-0b00fa0000d0/files
***
## ID File requests
Use the endpoint `/v2/data_requests/company_base/id_file` to submit a list of IDs in a .csv or .txt file to request company data in bulk. The following topic explains how to make ID File request:
# Company Posts API
Company Posts API overview: search and collect fresh company-published posts via Search Filters or Elasticsearch DSL, with Collect endpoint support.
## Overview
This section covers basic information on the Company Posts API.
To learn more about the API and its endpoints, follow the links below:
## Company Posts API endpoints
{% hint style="info" %}
Our API is a data retrieval tool. The endpoints do not support analytic features.
{% endhint %}
Company Posts API features two search endpoints and one collect endpoints to retrieve company posts data.\
Use the endpoints with any API-compatible application.
{% hint style="warning" %}
All Company Posts API requests must be made over HTTPS. Requests made over HTTP will fail or be redirected to HTTPS.
{% endhint %}
Company Posts API supports two types of requests:
* **Search** endpoints support POST requests only.
* **Collect** endpoints support GET requests only.
# Data Dictionary: Company Posts API
Data dictionary for Company Posts API – fields explained across author info, fresh post content, engagement metrics, and comment data.
## Overview
Data dictionary for data retrieved using Company Posts API endpoints.
This data dictionary shows all available data fields, explains their values, and provides data samples from the API Company Posts dataset.
{% tabs %}
{% tab title="Data fields per category" %}
1. [Author](#author)
2. [Metadata](#metadata)
3. [Post content](#post-content)
4. [Engagement](#engagement)
5. [Reshared post](#reshared-post)
{% endtab %}
{% endtabs %}
{% hint style="info" %}
All personal/company information mentioned within this context is entirely fictional and is solely intended for illustrative purposes.
{% endhint %}
## Author
| Data field | Description | Data type |
| ------------------ | ----------------------------------------------- | --------- |
| `company_name` | Company name | String |
| `company_url` | Company profile URL | String |
| `company_headline` | Headline or title of the company (if available) | String |
**Refer to the table example from the data:**
{% code title="Author" %}
```json
{
"company_name": "Example Company",
"company_url": "https://www.professional-network.com/company/example-company",
"company_headline": "Global Leader in Enterprise Software | Empowering 10,000+ Businesses Worldwide",
}
```
{% endcode %}
## Metadata
| Data field | Description | Data type |
| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ | --------- |
| `company_id` | Company record identification key in our database | Long |
| `company_source_id` | Identifier assigned by Professional network | Long |
| `id` | Post's ID | String |
| `url` | Post's URL | String |
| `date_published` | Post publication date | String |
| `deleted` |
Record deletion status: 1 – the profile returned "Page not found" or was deleted; 0 – the record exists
| Integer |
| `created_at` | Record creation timestamp in `ISO 8601` format | Timestamp |
| `updated_at` | Record update timestamp in `ISO 8601` format | Timestamp |
**Refer to the table example from the data:**
{% code title="Metadata" %}
```json
{
"company_id": 9871234560,
"company_source_id": 4412398,
"id": "7192837465019283746",
"url": "https://www.professional-network.com/posts/example-company_ai-innovation-futureofwork-activity",
"date_published": "2026-05-14",
"deleted": 0,
"created_at": "2026-05-14 08:15:22.104837",
"updated_at": "2026-05-17 10:42:55.837291"
}
```
{% endcode %}
## Post content
| Data field | Description | Data type |
| ---------------------- | --------------------------------------------------- | ---------------- |
| `article_body` | Content of the post | String |
| `image_url` | URL of an image attached to the post (if available) | String |
| `hashtags` | Hashtags used in the post | Array of strings |
| `mentions` | Mentioned companies or people | Array of structs |
| `mentions[].full_name` | Name mentioned in the post | String |
| `mentions[].url` | Mentioned entity URL | String |
**Refer to the table example from the data:**
{% code title="Post content" expandable="true" %}
```json
{
"article_body": "Excited to share our latest insights on the future of AI in enterprise. The past year has been transformative for our team, and we're just getting started. Drop your thoughts in the comments below 👇 #AI #Innovation",
"image_url": "https://media.licdn.com/dms/image/D4E22AQF3k9mXpT7vYw/feedshare-shrink_800/0/1700000000000",
"hashtags": [
"AI",
"Innovation"
],
"mentions": [
{
"full_name": "John Doe",
"url": "https://www.professional-network.com/john-doe-12345"
},
{
"full_name": "Jane Smith",
"url": "https://www.professional-network.com/jane-smith-67890"
}
]
}
```
{% endcode %}
## Engagement
| Data field | Description | Data type |
| --------------------------- | ------------------------------------------------------------------------------------------------------------ | ---------------- |
| `reaction_count` | Number of reactions on the post | Integer |
| `comment_count` | Number of comments on the post | Integer |
| `comments` | List of comments on the post | Array of objects |
| `comments[].full_name` | Name of the person who wrote the comment | String |
| `comments[].headline` | Headline or title of the commenter (if available) | String |
| `comments[].profile_url` | URL of the commenter’s profile | String |
| `comments[].body` | Content of the comment | String |
| `comments[].date_published` | Time when comment was published | String |
| `comments[].reaction_count` | Reactions number on the comment | Integer |
| `comments[].deleted` |
| Integer |
| `comments[].created_at` | Comment creation timestamp | Timestamp |
| `comments[].updated_at` | Comment update timestamp | Timestamp |
**Refer to the table example from the data:**
{% code title="Engagement" expandable="true" %}
```json
{
"reaction_count": 847,
"comment_count": 1,
"comments": [
{
"full_name": "Alice Johnson",
"headline": "Head of Product @ Sample Corp",
"profile_url": "https://www.professional-network.com/alice-johnson-48291",
"body": "This is such a timely post. We've been seeing the exact same trends on our end — enterprises are finally moving past the pilot phase and into real production deployments. Would love to connect and exchange notes!",
"reaction_count": 34,
"date_published": "2026-05-15",
"deleted": 0,
"created_at": "2026-05-15 09:23:14.582341",
"updated_at": "2026-05-15 09:23:14.582341"
}
]
}
```
{% endcode %}
## Reshared post
| Data field | Description | Data type |
| ---------------------------------- | ------------------------------------------------------------------------------------ | ---------------- |
| `reshared_post` | Reshared posts information | Struct |
| `reshared_post.id` | Unique identifier of the reshared post. Used to distinguish it from other posts | String |
| `reshared_post.url` | Direct URL to the reshared post on Professional network | String |
| `reshared_post.author_name` | Name of the original author of the reshared post | String |
| `reshared_post.author_profile_url` | Professional network profile URL of the original post’s author | String |
| `reshared_post.author_headline` | Headline or summary text shown under the author’s name, often follower count or role | String |
| `reshared_post.company_id` | The `company_id` of the reshared post in case the post belongs to a company | Long |
| `reshared_post.date_published` | Time since the post was reshared | String |
| `reshared_post.article_body` | Main text content of the reshared post | String |
| `reshared_post.image_url` | Original post image URL | Array of strings |
| `reshared_post.hashtags` | List of hashtags included in the reshared post text | Array of strings |
**Refer to the table example from the data:**
{% code title="Reshared post" expandable="true" %}
```json
{
"reshared_post": {
"id": "7185647382910274651",
"url": "https://www.professional-network.com/posts/partner-company_digitaltransformation-enterpriseai-activity-7185647382910274651-mP4q",
"author_name": "Partner Company",
"author_profile_url": "https://www.professional-network.com/company/partner-company",
"author_headline": "Enterprise AI Solutions | Trusted by Fortune 500 Companies",
"company_id": 5523109,
"date_published": "2026-05-17",
"article_body": "The next wave of enterprise transformation is here. We've just published our 2024 State of AI report, covering adoption trends, ROI benchmarks, and the strategies top-performing companies are using to stay ahead. Download the full report below 👇 #DigitalTransformation #EnterpriseAI",
"image_url": [
"https://media.licdn.com/dms/image/D4E22AQF9kLmPx3vZw/feedshare-shrink_800/reshare_thumbnail.jpg"
],
"hashtags": [
"DigitalTransformation",
"EnterpriseAI"
]
}
```
{% endcode %}
# Sample: Company Posts API
Explore Company Posts API data sample with fresh post content, author profile details, engagement counts, and comment-level data in JSON format.
Review Coresignal's Company Posts API sample below, or [contact sales](https://coresignal.com/contact-us/?utm_source=web\&utm_medium=public-docs\&utm_campaign=data-consultation) for more information.
{% hint style="info" %}
All personal/company information mentioned within this context is entirely fictional and is solely intended for illustrative purposes.
{% endhint %}
{% code title="Data sample" %}
```json
{
"company_id": 9871234560,
"company_source_id": 4412398,
"id": "7192837465019283746",
"url": "https://www.professional-network.com/posts/example-company_ai-innovation-futureofwork-activity",
"company_name": "Example Company",
"company_url": "https://www.professional-network.com/company/example-company",
"company_headline": "Global Leader in Enterprise Software | Empowering 10,000+ Businesses Worldwide",
"date_published": "2026-05-14",
"article_body": "Excited to share our latest insights on the future of AI in enterprise. The past year has been transformative for our team, and we're just getting started. Drop your thoughts in the comments below 👇 #AI #Innovation",
"image_url": "https://media.licdn.com/dms/image/D4E22AQF3k9mXpT7vYw/feedshare-shrink_800/0/1700000000000",
"hashtags": [
"AI",
"Innovation"
],
"mentions": [
{
"full_name": "John Doe",
"url": "https://www.professional-network.com/john-doe-12345"
},
{
"full_name": "Jane Smith",
"url": "https://www.professional-network.com/jane-smith-67890"
}
],
"reaction_count": 847,
"comment_count": 1,
"comments": [
{
"full_name": "Alice Johnson",
"headline": "Head of Product @ Sample Corp",
"profile_url": "https://www.professional-network.com/alice-johnson-48291",
"body": "This is such a timely post. We've been seeing the exact same trends on our end — enterprises are finally moving past the pilot phase and into real production deployments. Would love to connect and exchange notes!",
"reaction_count": 34,
"date_published": "2026-05-15",
"deleted": 0,
"created_at": "2026-05-15 09:23:14.582341",
"updated_at": "2026-05-15 09:23:14.582341"
}
],
"reshared_post": {
"id": "7185647382910274651",
"url": "https://www.professional-network.com/posts/partner-company_digitaltransformation-enterpriseai-activity-7185647382910274651-mP4q",
"author_name": "Partner Company",
"author_profile_url": "https://www.professional-network.com/company/partner-company",
"author_headline": "Enterprise AI Solutions | Trusted by Fortune 500 Companies",
"company_id": 5523109,
"date_published": "2026-05-17",
"article_body": "The next wave of enterprise transformation is here. We've just published our 2024 State of AI report, covering adoption trends, ROI benchmarks, and the strategies top-performing companies are using to stay ahead. Download the full report below 👇 #DigitalTransformation #EnterpriseAI",
"image_url": [
"https://media.licdn.com/dms/image/D4E22AQF9kLmPx3vZw/feedshare-shrink_800/reshare_thumbnail.jpg"
],
"hashtags": [
"DigitalTransformation",
"EnterpriseAI"
]
},
"deleted": 0,
"created_at": "2026-05-14 08:15:22.104837",
"updated_at": "2026-05-17 10:42:55.837291"
}
```
{% endcode %}
# Search Filters: Company Posts API
{% columns %}
{% column width="16.666666666666664%" %}
Data type:
Query type:
URL:
{% endcolumn %}
{% column %}
Company Posts
Coresignal's custom filters
{% endcolumn %}
{% endcolumns %}
***
## Overview
Explore the available filters, their explanations, potential applications, and helpful tips for the endpoint `/v2/company_post/search/filter` usage.
Use this endpoint to collect company posts IDs with relevant keywords.
Opt for this endpoint if you prefer uncomplicated queries. Details about filter explanations and how to use them are provided below.
## Endpoint structure
{% code title="Full structure" %}
```json
{
"company_url": "string",
"article_body": "string",
"comments_profile_url": "string",
"comments_body": "string",
"date_published_gte": "string",
"date_published_lte": "string"
}
```
{% endcode %}
## Filter list
{% hint style="info" %}
Keep in mind that example outputs are redacted and may not contain all the fields you would receive using the endpoint.
{% endhint %}
company_url
| Filter name | Data input type | Description | Usage |
| ------------- | --------------- | --------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `company_url` | String | Company's profile URL |
Use it to find records based on the profile's url.
Available operators:
AND (both keywords need to be present)
OR (one of the two input values).
|
{% hint style="success" %}
Use `"(first phrase) OR (second phrase)"` if you are searching for words in a phrase and want them to be interpreted together.
{% endhint %}
**Example input:**
{% code title="author\_profile\_url" %}
```json
{
"company_url": "https://www.professional-network.com/company1"
}
```
{% endcode %}
article_body
| Filter name | Data input type | Description | Usage |
| -------------- | --------------- | ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `article_body` | String | Content of the post |
Use it to find records based on content.
Available operators:
AND (both keywords need to be present)
OR (one of the two input values).
|
{% hint style="success" %}
Use `"(first phrase) OR (second phrase)"` if you are searching for words in a phrase and want them to be interpreted together.
{% endhint %}
**Example input:**
{% code title="article\_body" %}
```json
{
"article_body": "promoted to Senior Data Analyst"
}
```
{% endcode %}
comments_profile_url
| Filter name | Data input type | Description | Usage |
| ---------------------- | --------------- | ------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `comments_profile_url` | String | Profile URL of a posted comment |
Use to find post records based on the posted comment profile URL.
Available operators:
AND (both keywords need to be present)
OR (one of the two input values).
|
{% hint style="success" %}
Use `"(first phrase) OR (second phrase)"` if you are searching for words in a phrase and want them to be interpreted together.
{% endhint %}
**Example input:**
{% code title="comments\_profile\_url" %}
```json
{
"comments_profile_url": "https://www.professional-network.com/john-doe"
}
```
{% endcode %}
comments_body
| Filter name | Data input type | Description | Usage |
| --------------- | --------------- | ---------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `comments_body` | String | Content of the comment |
Use it to find post records based on the comments.
Available operators:
AND (both keywords need to be present)
OR (one of the two input values).
|
{% hint style="success" %}
Use `"(first phrase) OR (second phrase)"` if you are searching for words in a phrase and want them to be interpreted together.
{% endhint %}
**Example input:**
{% code title="comments\_body" %}
```json
{
"comments_body": "This post is written by AI"
}
```
{% endcode %}
date_published_gte
| Filter name | Data input type | Description | Usage |
| -------------------- | --------------- | --------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------- |
| `date_published_gte` | String | Date of company post publishing. The output value will be greater than or equal to the input value. |
Find company post records based on publication date.
Use the YYYY-MM-DD date format.
|
**Example input:**
{% code title="date\_published\_gte" %}
```json
{
"date_published_gte": "2026-07-01"
}
```
{% endcode %}
date_published_lte
| Filter name | Data input type | Description | Usage |
| -------------------- | --------------- | ------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------- |
| `date_published_lte` | String | Date of company post publishing. The output value will be less than or equal to the input value. |
Find company post records based on publication date.
Use the YYYY-MM-DD date format.
|
**Example input:**
{% code title="date\_published\_lte" %}
```json
{
"date_published_gte": "2026-07-01"
}
```
{% endcode %}
## Sample request
{% code title="Request example" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/company_post/search/filter' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"article_body": "newest invention",
"date_published_gte": "2026-07-01"
}'
```
{% endcode %}
# Search Filters Pagination: Company Posts API
## Overview
General information about the pagination is listed in [Results Pagination](/api-introduction/requests/elasticsearch-dsl/results-pagination) topic.
Here you can find:
## Using pagination in cURL requests
{% hint style="info" %}
This tutorial requires prior knowledge of how to compile and execute POST requests in Company Posts data API.
{% endhint %}
Use parameter `x-next-page-after` to retrieve a second page of IDs.
1. Navigate to the **Headers** section and click it:
2. Find the following information:\
– `x-next-page-after`\
– `x-total-pages`\
– `x-total-results`
3. Add parameter `?after={x-next-page-after}` to the POST request to see the next results page:
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/company_post/search/filter?after="2026-07-01",7350452046057107456' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"article_body": "newest invention",
"date_published_gte": "2026-07-01"
}'
```
4. Execute the request, and you will see the next page in the **Body** section:
```json
[
1000,
1001,
3000,
4004
]
```
## Limiting search results per page
Query parameter `?items_per_page={int}` allows you to specify the number of results retrieved per Search results page. The current limit is 1,000. Thus, this parameter lets you set a smaller limit value for the results page.
{% code title="Items per page" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/company_post/search/filter?items_per_page=250' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"article_body": "newest invention",
"date_published_gte": "2026-07-01"
}'
```
{% endcode %}
# Elasticsearch DSL: Company Posts API
{% columns %}
{% column width="16.666666666666664%" %}
Data type:
Query type:
URL:
{% endcolumn %}
{% column %}
Company Posts
Elasticsearch DSL
{% endcolumn %}
{% endcolumns %}
***
## Overview
Use the `/v2/company_post/search/es_dsl` endpoint for more sophisticated queries.
Additionally, the endpoint allows you to operate filters that mimic our company posts data, enabling you to write more sophisticated queries than using the `/v2/company_post/search/filter` endpoint.
General Elasticsearch DSL information and usage tips
## Using pagination in cURL requests
{% hint style="info" %}
This tutorial requires prior knowledge of how to compile and execute POST requests in Company Posts data API.
{% endhint %}
Use parameter `x-next-page-after` to retrieve a second page of IDs.
1. Navigate to the **Headers** section and click it:
2. Find the following information:\
– `x-next-page-after`\
– `x-total-pages`\
– `x-total-results`
3. Add parameter `?after={x-next-page-after}` to the POST request to see the next results page:
4. Execute the request, and you will see the next page in the **Body** section:
```json
[
1000,
1001,
3000,
4004
]
```
### Pagination using sorting
#### **Pagination usage example (cURL request in Postman)**
1. Add parameter `?after={x-next-page-after}` to the POST request:\
Refer to the example below for the exact parameter placement:
{% code title="Pagination (sorted by id)" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/company_post/search/es_dsl?after=6553076922996396032' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"query":{
"match":{
"company_name":{
"query":"Tesla",
"operator":"and"
}
}
},
"sort": [
"id"
]
}'
```
{% endcode %}
**Send** the request, and you will see the next page in the **(Response) Body**.
***
Pagination using **score** sorting has a different ID format.
The format difference is seen by the `x-next-page-after` parameter, showing the **score**, the **last updated** date, and the **last ID** on the page.
#### **Pagination usage example (cURL request in Postman)**
Add parameter `?after={x-next-page-after}` to the POST request to see the next results page. Refer to the example below for the exact parameter placement:
{% code title="Pagination (sorted by score)" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/company_post/search/es_dsl?after=11.875278,"2024-09-03",7236756071086710784' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"query":{
"match":{
"company_name":{
"query":"Tesla",
"operator":"and"
}
}
},
"sort": [
"_score"
]
}'
```
{% endcode %}
**Send** the request, and you will see the next page in the **(Response) Body**.
## Limiting search results per page
Query parameter `?items_per_page={int}` allows you to specify the number of results retrieved per Search results page. The current limit is 1,000. Thus, this parameter lets you set a smaller limit value for the results page.
{% code title="Items per page" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/company_post/search/es_dsl?items_per_page=150' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"query":{
"match":{
"company_name":{
"query":"Tesla",
"operator":"and"
}
}
}
}'
```
{% endcode %}
# Collect: Company Posts API
{% columns %}
{% column width="16.666666666666664%" %}
Data type:
URL:
{% endcolumn %}
{% column %}
Company Posts
{% endcolumn %}
{% endcolumns %}
***
## Overview
Find instructions for collection endpoint usage and data collection.
| Key | Collect endpoint | Function |
| ---------------- | -------------------------------------- | --------------------------------- |
| Company Posts ID | */v2/company\_post/collect/{post\_id}* | Retrieved using search endpoints. |
## Collection using posts IDs
Examples in this article are prepared using Postman.\
However, you can use the most convenient tool for you: terminal, Postman, or any API-compatible application.
### cURL (Postman)
Use the provided request template below. Enter a valid `post_id` value and your `API Key`.
{% tabs %}
{% tab title="Full collect" %}
{% code title="cURL request example" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/company_post/collect/{post_id}' \
-H 'accept: application/json' \
-H 'apikey: {API Key}'
```
{% endcode %}
{% endtab %}
{% tab title="Field selection" %}
{% code title="cURL request example with several fields" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/company_post/collect/{post_id}?fields=company_name&fields=date_published' \
-H 'accept: application/json' \
-H 'apikey: {API Key}'
```
{% endcode %}
{% endtab %}
{% endtabs %}
# Historical Headcount API
This section covers general details about Historical Headcount API.
{% hint style="info" %}
#### Find out more
Contact our sales team to get access to more information about this API:
Contact sales
{% endhint %}
## Historical headcount API endpoints
Historical headcount data enables you to see how companies’ social media following and headcount numbers have changed over time.
Retrieve the Historical headcount data using **two collection** endpoints. Collect the data using either `company ID` or `company shorthand name`.
{% hint style="warning" %}
All Historical headcount API requests must be made over HTTPS. Requests made over HTTP will fail or be redirected to HTTPS.
{% endhint %}
Historical headcount API supports GET requests only.
Endpoint
Function
Credits
GET /v2/historical_headcount/collect/{company_id}
Collect Historical Headcount data using company ID
10
GET /v2/historical_headcount/collect/{shorthand_name}
Collect Historical Headcount data using company shorthand name
10
\*📌 Full profile URL example: \_[www.professional-network.com/example-company\_.\\](http://www.professional-network.com/example-company_.\\)
Shorthand name example: *example-company*.
## FAQ
The data you receive from the Historical headcount API differs from company data.
How often is the data updated?
Historical headcount data is updated once a month (at the beginning of the month).
## Dictionary
| Data field | Description | Data type |
| -------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- | --------- |
| `created` | Record creation timestamp | String |
| `company_id` | Company record ID assigned in the database | Integer |
| `source_id` | Professional network in-house company ID (static) | Integer |
| `professional_network_url` |
Professional network profile URL Note: seen as canonical\_url in our company data
| String |
| `shorthand_name` |
Dynamic part of the URL used to identify companies Note: seen as company\_shorthand\_name in our company data
| String |
| `canonical_shorthand_name` | Most recent version of the `shorthand_name` | String |
| `name` | Name | String |
| `website` | Website | String |
| `size` | Employee count | String |
| `industry` | Associated industry | String |
| `type` | Type | String |
| `location` | Location | String |
| `country` | Location (country) | String |
| `headcount` | Headcount | Integer |
| `follower_count` | Profile follower count | Integer |
### Response sample
{% hint style="info" %}
All personal/company information mentioned within this context is entirely fictional and is solely intended for illustrative purposes.
{% endhint %}
{% code title="Response example" expandable="true" %}
```json
[
{
"created": "2023-12-19 08:42:33",
"company_id": 8579547,
"source_id": 1586,
"professional_network_url": "https://www.professional_network.com/company/example-company",
"shorthand_name": "example-company",
"canonical_shorthand_name": "example-company",
"name": "Amazon",
"website": "https://www.example-company.com/",
"size": "10,001+ employees",
"industry": "Software Development",
"type": "Private Company",
"location": "San Francisco, CA",
"country": "United States",
"headcount": 119730,
"follower_count": 229985793
},
{
"created": "2023-11-21 09:54:07",
"company_id": 8579547,
"source_id": 1586,
"professional_network_url": "https://www.professional_network.com/company/example-company",
"shorthand_name": "example-company",
"canonical_shorthand_name": "example-company",
"name": "Amazon",
"website": "https://www.about-example-company.com/",
"size": "10,001+ employees",
"industry": "Software Development",
"type": "Private Company",
"location": "San Francisco, CA",
"country": "United States",
"headcount": 86538,
"follower_count": 1711354
},
{
"created": "2023-10-08 18:22:51",
"company_id": 8579547,
"source_id": 1586,
"professional_network_url": "https://www.professional_network.com/company/example-company",
"shorthand_name": "example-company",
"canonical_shorthand_name": "example-company",
"name": "Amazon",
"website": "https://www.example-company.com/",
"size": "10,001+ employees",
"industry": "Software Development",
"type": "Private Company",
"location": "San Francisco, CA",
"country": "United States",
"headcount": 10350,
"follower_count": 12866
}
. . .
]
```
{% endcode %}
# Multi-source Employee Data
Enriched, Multi-source Employee dataset with weekly or monthly delivery of fresh data in JSONL or Parquet format. Includes historical tracking.
| **Save engineering resources** | Our Multi-source Employee dataset is cleaned, enriched, and ready to use. |
| -------------------------------------- | ---------------------------------------------------------------------------- |
| **Comprehensive multi-source dataset** | Multiple sources are integrated to provide a complete employee profile. |
| **Optimized file sizes and formats** | Use JSONL or Parquet format and smaller file sizes for faster download. |
| **Leverage historical data** | Track changes in employee metrics over time with aggregated historical data. |
***
## Summary
| Feature | Details |
| ------------------ | --------------------------------- |
| Available via | Flat files/API |
| Delivery frequency | Daily, weekly, monthly, quarterly |
| Available formats | JSONL, Parquet |
| Scraping since | 2016-07 |
## Related links
# Dictionary: Multi-source Employee Data
On this page, you'll find detailed information about Coresignal's **Multi-source Employee** data.\
Each category includes a table listing the available data fields, their explanations, data types, and sample code snippets.
{% hint style="info" %}
All personal/company information mentioned within this context is entirely fictional and is solely intended for illustrative purposes.
Data fields in the example snippets are rearranged for better grouping.
{% endhint %}
{% tabs %}
{% tab title="Data fields per category" %}
1. [Metadata](#metadata)
2. [Identifiers and URLs](#identifiers-and-urls)
3. [Employee information](#employee-information)
4. [Professional contact information](#professional-contact-information)
5. [Location](#location)
6. [Experience and workplace](#experience-and-workplace)
7. [Full experience information](#full-experience-information)
8. [Workplace details](#workplace-details)
9. [Education](#education)
10. [Salary](#salary)
11. [Profile field changes](#profile-field-changes)
12. [Recent experience changes](#recent-experience-changes)
13. [Recommendations](#recommendations)
14. [Activity](#activity)
15. [Awards](#awards)
16. [Courses](#courses)
17. [Certifications](#certifications)
18. [Languages](#languages)
19. [Patents](#patents)
20. [Publications](#publications)
21. [Projects](#projects)
22. [Organizations](#organizations)
23. [Investments](#investments)
24. [Events and Exits](#events-and-exits)
{% endtab %}
{% endtabs %}
## Metadata
| Data field | Description | Data type |
| -------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `created_at` | The date and time when the employee record was created | String (date) |
| `updated_at` | The date and time when the employee record was last fully updated | String (date) |
| `checked_at` | The date and time when the employee record was last checked partially | String (date) |
| `changed_at` | The date and time when the employee record was last changed | String (date) |
| `processed_at` | The date and time when the employee record was processed on our side – including reparsing events, reloads triggered by data quality fixes, and other operational changes | String (date) |
| `experience_change_last_identified_at` | The date and time when the employee record change was last identified | String (date) |
| `is_deleted` |
Marks if the employee record is deleted or private 1 – the record is deleted 0 – the record is not deleted
| Number (integer) |
| `is_parent` |
Notes if the employee record is the main employee profile: 1 – the record is parent (main) 0 – the record is not parent
| Number (integer) |
**See a snippet of the dataset for reference:**
{% code title="Metadata" %}
```json
"created_at": "2024-07-27T01:56:27.000",
"updated_at": "2026-05-23T05:50:09.000",
"checked_at": "2026-05-23T05:50:09.000",
"changed_at": "2026-05-23T06:30:1.000",
"processed_at": "2026-05-24T06:30:1.000",
"experience_change_last_identified_at": "2024-10-25T06:30:10.000",
"is_deleted": 0,
"is_parent": 1
```
{% endcode %}
## Identifiers and URLs
| Data field | Description | Data type |
| ----------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `id` |
Coresignal's identification key for an employee profile record. Taken from the professional network dataset.
| Number (integer) |
| `parent_id` | Parent category identification key | Number (integer) |
| `historical_ids` | Historical identification keys that are related to the same profile after URL change | Array of longs |
| `professional_network_url` | Most recent profile URL on professional network | String |
| `professional_network_shorthand_names` | Historical variations of shorthand names for the employee | Array of strings |
| `professional_network_canonical_shorthand_name` | The most recent version of employee's shorthand name | String |
| `public_profile_id` | Public profile ID | Number (long) |
| `facebook_url` | Facebook URL | String |
| `twitter_url` | Twitter URL | String |
| `financial_website_url` | Financial website URL | String |
| `website` | Employee's website | String |
**See a snippet of the dataset for reference:**
{% code title="Identifiers & URLs" %}
```json
"id": 12389891,
"parent_id": 12389891,
"historical_ids": [
12389891,
11589843
],
"professional_network_url": "https://www.professional_network.com/john-doe-18729383",
"professional_network_shorthand_names": [
"real-john-doe",
"john-doe-1992"
],
"professional_network_canonical_shorthand_name": "john-doe-18729383",
"facebook_url": "https://www.facebook.com/john-doe",
"twitter_url": "https://www.x.com/john-doe",
"financial_website_url": "https://www.financial-website.com/person/john-doe",
"website": "https://www.john-doe-website.com"
```
{% endcode %}
### Profile score
| Data field | Description | Data type |
| --------------- | ----------------------------------------------------------------------------------------------------------------------- | --------- |
| `profile_score` | Model-derived employee profile quality score based on profile completeness and activity signals. Score range: `0` – `1` | Double |
**See a snippet of the dataset for reference:**
{% code title="Profile score" %}
```json
"profile_score": 0.5
```
{% endcode %}
## Employee information
| Data field | Description | Data type |
| --------------------- | ------------------------------------------------------------------------ | ---------------- |
| `full_name` | Employee's full name | String |
| `first_name` |
Employee's first name Parsed from the full\_name
| String |
| `first_name_initial` |
First name initial Parsed from first\_name
| String |
| `middle_name` |
Employee's middle name Parsed from the full\_name
| String |
| `middle_name_initial` |
Middle name initial Parsed from middle\_name
| String |
| `last_name` |
Employee's last name Parsed from the full\_name
| String |
| `last_name_initial` |
Last name initial Parsed from last\_name
| String |
| `picture_url` | Picture URL | String |
| `connections_count` | Count of profile connections | Number (integer) |
| `followers_count` | Count of profile followers | Number (integer) |
| `interests` | Employee's interests | Array of strings |
**See a snippet of the dataset for reference:**
{% code title="Employee information" %}
```json
"full_name": "John Doe",
"first_name": "John",
"first_name_initial": "J",
"middle_name": "Michael",
"middle_name_initial": "M",
"last_name": "Doe",
"last_name_initial": "D",
"picture_url": "https://static.lnk.com/aero-v1/sc/h/9c8pery4andzj6ohjkjp54ma2"
"connections_count": 472,
"followers_count": 3190,
"interests":
[
"hiking",
"snowboarding",
"cycling",
]
```
{% endcode %}
## Professional contact information
{% hint style="info" %}
Coresignal **collects only publicly available, strictly business-related data** published or released by companies or individuals at their discretion online. The contact information includes **only professional emails**.\
No sensitive or private/ located within the login secured areas information is collected or transmitted.
{% endhint %}
| Data field | Description | Data type |
| ----------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `primary_professional_email` | Employee's business email address tied to their workplace | String |
| `primary_professional_email_status` | The confidence level in the accuracy of the employee's business email address. The field will return four options: `verified`, `matched_email,` `matched_pattern,` or `guessed_common_pattern`. | String |
| `professional_emails_collection` | Collection of employee's business email addresses | Array of structs |
| `professional_email` | Employee's business email address | String |
| `professional_email_status` |
The confidence level in the accuracy of the employee's professional email address. The field will return four options:
verified – the email was matched and verified
matched\_email – the email was matched but could not retrieve "verified" status
matched\_pattern – the exact email was not matched, but it was guessed based on the matched email pattern for the company
guessed\_common\_pattern – neither the exact email nor the email pattern for that company was matched, but the most common global pattern was guessed
| String |
| `order_of_priority` | Order of priority based on confidence in email validity | Number (integer) |
**See a snippet of the dataset for reference:**
{% code title="Professional contact information" %}
```json
"primary_professional_email": "johndoe@company1.com",
"primary_professional_email_status": "matched_pattern",
"professional_emails_collection": [
{
"professional_email": "johndoe@company1.com",
"professional_email_status": "matched_pattern",
"order_of_priority": 1
},
{
"professional_email": "john.doe1@company1.com",
"professional_email_status": "matched_pattern",
"order_of_priority": 2
},
{
"professional_email": "johndoe123@company1.com",
"professional_email_status": "matched_pattern",
"order_of_priority": 3
}
]
```
{% endcode %}
## Location
| Data field | Description | Data type |
| ----------------------- | ----------------------------------------------------------------------------------- | ---------------- |
| `location_country` | Associated country | String |
| `location_city` | Employee location city | String |
| `location_state` | Employee location state | String |
| `location_country_iso2` | ISO 2-letter code of the location country, based on their `location_country` value. | String |
| `location_country_iso3` | ISO 3-letter code of the location country, based on their `location_country` value. | String |
| `location_full` | Full location | String |
| `location_regions` | Associated geographical regions based on their `location_country` value | Array of strings |
**See a snippet of the dataset for reference:**
{% code title="Location" %}
```json
"location_country": "United States",
"location_city": "San Diego",
"location_state": "California",
"location_country_iso2": "US",
"location_country_iso3": "USA",
"location_full": "San Diego, California, United States",
"location_regions": [
{
"region": "Americas"
},
{
"region": "Northern America"
},
{
"region": "AMER"
}
]
```
{% endcode %}
## Experience and workplace
### Active experience overview
| Data field | Description | Data type |
| ------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `headline` | Profile headline | String |
| `summary` | Main description (employee summary) | String |
| `services` | Offered services | String |
| `is_working` |
Marks if the employee is currently employed 1 – the employee is currently working 0 – the employee is currently not working
| Number (integer) |
| `active_experience_company_id` | Coresignal's identification key for a company to identify where the employee is currently working | Number (integer) |
| `active_experience_title` | Title of employee's current position | String |
| `active_experience_description` | Description of the current position | String |
| `active_experience_department` | A list of employee's departments, based on `actve_position_title` | String |
| `active_experience_management_level` | A list of employee's management levels, based on `active_experience_title` | String |
| `active_experience_company_website` | Website of the company in the employee's active experience | String |
| `active_experience_company_shorthand_name` | Shorthand name of the company in the active experience | String |
| `active_experience_company_logo_url` | Logo URL of the company in the active experience | String |
| `is_decision_maker` |
Marks if the employee is a decision maker, based on active\_experience\_title 1 – the employee is a decision maker 0 – the employee is not a decision maker
| Number (integer) |
**See a snippet of the dataset for reference:**
{% code title="Active experience overview" %}
```json
"headline": "Data Analyst | Machine Learning Enthusiast",
"generated_headline": "Data Specialist – Predictive Analytics",
"summary": "
Passionate about uncovering insights from data and applying machine learning techniques to solve global problems.
",
"services": "Data Analysis, Machine Learning Consulting, Business Intelligence",
"is_working": 1,
"active_experience_company_id": 4127532,
"active_experience_title": "Senior Data Analyst",
"active_experience_description": "Leading data-driven projects, building predictive models, and optimizing business intelligence strategies.",
"active_experience_department": "Engineering and Technical",
"active_experience_management_level": "Senior",
"active_experience_company_website": "www.fake-tech-company.com",
"active_experience_company_shorthand_name": "fake-tech-company",
"active_experience_company_logo_url": "https://media.licdn.com/dms/image/v2/example/company-logo",
"is_decision_maker": 1
```
{% endcode %}
### Skills
| Data field | Description | Data type |
| ------------------- | ------------------------------------------------------------------ | ---------------- |
| `inferred_skills` | Lists employees' skills based on the descriptions from the profile | Array of strings |
| `historical_skills` | Historical skills | Array of strings |
**See a snippet of the dataset for reference:**
{% code title="Skills" %}
```json
"inferred_skills": [
"cloud computing",
"data analysis",
"software development",
"troubleshooting",
"web development"
]
```
{% endcode %}
### Experience duration
| Data field | Description | Data type |
| ------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `months_in_management` | Total months spent in management-level roles. Calculated as the union of these positions, so overlapping roles aren't counted twice | Long |
| `total_experience_duration_months` | Total normalized experience duration of all Employee's experiences | Number (integer) |
| `total_experience_duration_months_breakdown_department` | Total normalized experience duration by employee's department | Array of structs |
| `department` | Department | String |
| `total_experience_duration_months` | Experience duration in months | String |
| `total_experience_duration_months_breakdown_management_level` | Total normalized experience duration by Employee's management level | Array of structs |
| `management_level` | Employee's management level | String |
| `total_experience_duration_months` | Experience duration in months | String |
**See a snippet of the dataset for reference:**
{% code title="Experience duration" %}
```json
"months_in_management": 13,
"total_experience_duration_months": 85,
"total_experience_duration_months_breakdown_department": [
{
"department": "C-Suite",
"total_experience_duration_months": 13
},
{
"department": "Marketing",
"total_experience_duration_months": 30
},
{
"department": "Finance & Accounting",
"total_experience_duration_months": 14
},
{
"department": "Engineering and Technical",
"total_experience_duration_months": 28
}
],
"total_experience_duration_months_breakdown_management_level": [
{
"management_level": "C-Level",
"total_experience_duration_months": 10
},
{
"management_level": "Intern",
"total_experience_duration_months": 3
},
{
"management_level": "Senior",
"total_experience_duration_months": 4
},
{
"management_level": "Specialist",
"total_experience_duration_months": 40
},
{
"management_level": "Manager",
"total_experience_duration_months": 18
}
]
```
{% endcode %}
### Tenure statistics
| Data field | Description | Data type |
| ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------- |
| `tenure_stats` | Per-company tenure statistics in months, with concurrent roles at the same company counted once. It presents raw data without plausibility checks, so some values might be unrealistic. `null` if the employee has no datable, company-linked experience | Struct |
| `avg_tenure_months` | Average total tenure per company | Double |
| `median_tenure` | Median total tenure per company | Double |
| `current_tenure` | Tenure at the current (active) position. `null` if not currently employed | Double |
| `longest_tenure` | Longest total tenure at a single company | Double |
**See a snippet of the dataset for reference:**
{% code title="Tenure statistics" %}
```json
"tenure_stats": {
"avg_tenure_months": 26.5,
"median_tenure": 24.0,
"current_tenure": 13.0,
"longest_tenure": 48.0
},
```
{% endcode %}
### Internal promotion rate
| Data field | Description | Data type |
| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------- |
| `internal_promotion_rate` | Internal promotion metrics, derived from position-title changes within the same company, ordered by start date, counting only changes where the new role starts strictly after the previous role | Struct |
| `promotion_count` | Count of internal title changes (promotions). `0` if none | Integer |
| `avg_months_per_promotion` | Average months between consecutive promotions. `null` with fewer than two promotions | Double |
| `recently_promoted` | True if a promotion occurred within the last 3 months | Boolean |
**See a snippet of the dataset for reference:**
{% code title="Internal promotion rate" %}
```json
"internal_promotion_rate": {
"promotion_count": 2,
"avg_months_per_promotion": 19.5,
"recently_promoted": false
},
```
{% endcode %}
## Full experience information
| Data field | Description | Data type |
| ------------------- | ---------------------------------------------------------- | ---------------- |
| `experience` | Work experience the Employee has | Array of structs |
| `id` | Profile hash ID uniquely identifying each experience entry | String |
| `active_experience` | Identifies if this is a current (active) position | Integer |
| `position_title` | Employee's position title | String |
| `department` | Employees department | String |
| `management_level` | Employee's management level | String |
| `location` | Job/workplace location | String |
| `date_from` | Employment start date | String (date) |
| `date_from_year` | Employment start year | Integer |
| `date_from_month` | Employment start month | Integer |
| `date_to` | Employment end date | String (date) |
| `date_to_year` | Employment end year | Integer |
| `date_to_month` | Employment end month | Integer |
| `duration_months` | Employment duration in months | Integer |
| `description` | Employment description | String |
| `company_logo_url` | URL pointing to the logo of the company/employer | String |
**See a snippet of the dataset for reference:**
{% code title="Full experience information" %}
```json
"experience": [
{
"id": "bb8790aa02acd752b8ac5ec3ccd9f278",
"active_experience": 0,
"position_title": "Senior Data Analyst",
"department": "Data Science",
"management_level": "Mid-Level",
"location": "San Francisco, California, United States",
"date_from": "March 2020",
"date_from_year": 2020,
"date_from_month": 3,
"date_to": "July 2022",
"date_to_year": 2022,
"date_to_month": 7,
"duration_months": 126,
"company_logo_url": "https://media.licdn.com/dms/image/v2/example/company-logo"
}
]
```
{% endcode %}
## Workplace details
### Metadata and firmographics
| Data field | Description | Data type |
| -------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `company_id` | Company record identification key in Coresignal's database | Integer |
| `company_name` | Company name | String |
| `company_type` | Company type | String |
| `company_founded_year` | Founding year | String (date) |
| `company_size_range` | Company size based on employee count range (as selected by the company profile administrator) | String |
| `company_employees_count` | Number of employees that associated their experience with the company | Number (integer) |
| `company_categories_and_keywords` | Categories and keywords are assigned to the company profile and products across various platforms | Array of strings |
| `company_employees_count_change_yearly_percentage` | Company employee count change (percentage) | Number (integer) |
| `company_industry` | Company's industry | String |
| `company_last_updated_at` | The last update date of the record in the `YYYY-MM-DD` format | String (date) |
| `company_is_b2b` |
Indicates if the company operates in a business-to-business model: 1 – b2b company 0 – b2c company
| Number (integer) |
| `order_in_profile` | The order number of the workplace in the profile | Number (integer) |
**See a snippet of the dataset for reference:**
{% code title="Metadata & firmographics" %}
```json
"company_id": 87272276,
"company_name": "Company123, LLC",
"company_type": "Privately Held",
"company_founded_year": "2023",
"company_size_range": "51-200 employees",
"company_employees_count": 157,
"company_categories_and_keywords": [
"Data Analysis",
"AI",
"Management",
"Consulting",
],
"company_employees_count_change_yearly_percentage": 17.43222222222222
"company_industry": "Manufacturing",
"company_last_updated_at": "2025-02-03",
"company_is_b2b": 1,
"order_in_profile": 1
```
{% endcode %}
### Social media
| Data field | Description | Data type |
| ------------------------- | -------------------------------------------------------- | ---------------- |
| `company_followers_count` | Company's profile follower count on professional network | Integer |
| `company_website` | Company's website | String |
| `company_facebook_url` | Company's Facebook URL | Array of strings |
| `company_twitter_url` | Company's X (Twitter) URL | Array of strings |
| `company_linkedin_url` | Company's LinkedIn URL | String |
**See a snippet of the dataset for reference:**
{% code title="Social media" %}
```json
"company_followers_count": 527712,
"company_website": "https://www.company1.com",
"company_facebook_url": [
"https://www.facebook.com/company1global",
"https://www.facebook.com/company1"
],
"company_twitter_url": [
"https://www.x.com/company1"
],
"company_linkedin_url": "https://www.linkedin.com/company/company1"
```
{% endcode %}
### Financials
| Data field | Description | Data type |
| ----------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------- | ---------------- |
|
# Dictionary: Clean Employee Data
## Overview
Clean Employee Data provides high-quality, structured workforce data that is ready for immediate use. Our data is meticulously cleaned and enriched, enabling businesses to streamline operations, enhance decision-making, and optimize workforce analysis.
By leveraging Clean Employee Data, organizations can reduce engineering overhead, gain access to additional insights, and work with optimized data formats for improved efficiency. The data is available in JSONL, Parquet, and CSV formats, ensuring faster downloads and seamless integration.
With flexible retrieval options, including flat file downloads and API access, businesses in sales tech, HR intelligence, and investment sectors can efficiently access the workforce insights they need.
Clean Employee Data is derived from our Base Employee Data.
{% hint style="info" %}
The data fields are separated into collections to visualize the data better.
{% endhint %}
{% tabs %}
{% tab title="Data fields per category" %}
1. [Metadata](#metadata)
2. [Identifiers](#identifiers)
3. [Skills](#skills)
4. [Experience](#experience)
5. [Education](#education)
6. [Hidden collections](#hidden-collections)
7. [Location](#location)
8. [Recommendations and connections](#recommendations-and-connections)
9. [Languages](#languages)
10. [Certifications](#certifications)
11. [Courses](#courses)
12. [Awards](#awards)
13. [Activity](#activity)
14. [Organizations](#organizations)
15. [Patents](#patents)
16. [Publications](#publications)
{% endtab %}
{% endtabs %}
{% hint style="info" %}
All personal/company information mentioned within this context is entirely fictional and is solely intended for illustrative purposes.
{% endhint %}
## Metadata
| Data field | Processing | Description | Data type |
| --------------------- | ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------ | --------- |
| `member_last_updated` | Cleaned | Date the record was last updated | String |
| `processed_at` | – | Exposes when data was processed on our side – including reparsing events, reloads triggered by data quality fixes, and other operational changes | Timestamp |
| `member_is_deleted` | Raw |
Indicates whether the profile was accessible: 1 – deleted or private 0 – publicly available
| Integer |
{% code title="Meta data" %}
```json
"member_last_updated": "2026-05-19",
"processed_at": "2026-05-20",
"member_is_deleted": 0
```
{% endcode %}
Cleaning actions
| Data field | Cleaning action |
| --------------------- | ---------------------------------------------- |
| `member_last_updated` | Value is converted to the *yyyy-mm-dd* format. |
***
## Identifiers
| Data field | Processing | Description | Data type |
| -------------------------------------- | ---------- | ------------------------------------------------- | ---------------- |
| `member_id` | Raw | Identification key in our database | Integer |
| `member_websites_professional_network` | Raw | Professional network profile URL | String |
| `member_picture_url` | Raw | Profile picture URL | String |
| `member_full_name` | Cleaned | Full name | String |
| `member_name_first` | Raw | First name | String |
| `member_name_middle` | Enriched | Middle name | String |
| `member_name_last` | Enriched | Last name | String |
| `member_shorthand_names` | Raw | A list of all historical employee shorthand names | Array of strings |
| `member_follower_count` | Raw | Number of profile followers | Integer |
| `member_public_profile_id` | Raw | Publicly provided employee URN | String |
{% code title="Identifiers" %}
```json
"member_id": 4290,
"member_full_name": "John Leonardo Doe",
"member_name_first": "John",
"member_name_middle": "Leonardo",
"member_name_last": "Doe",
"member_websites_professional_network": "https://www.professional_network.com/john-leonardo-doe",
"member_picture_url": "https://static.lnk.com/aero-v1/sc/h/9c8pery4andzj6ohjkjp54ma2",
"member_shorthand_names": [
"john-lenoardo-doe"
],
"member_follower_count": 445,
"member_public_profile_id": "123456789",
```
{% endcode %}
Cleaning actions
| Data field | Cleaning action |
| -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `member_full_name` |
Special characters/emojis are removed;
Any words that follow a comma or are in parentheses are removed;
Titles (preceding or following the name) are removed.
|
| `member_name_middle` | Parsed from `member_full_name`*.* |
| `member_name_last` | Parsed from `member_full_name` |
***
## Skills
| Data field | Processing | Description | Data type |
| --------------- | ---------- | ------------------------- | ---------------- |
| `member_skills` | Enriched | List of employees' skills | Array of strings |
{% code title="Skills" %}
```json
"member_skills": [
"creative",
"design",
"electronics",
"photography",
"programming"
]
```
{% endcode %}
Enriching action
| Data field | Enriching action |
| --------------- | ------------------------------------------------------------- |
| `member_skills` | Enriched with our ML model from different description fields. |
***
## Experience
| Data field | Processing | Description | Data type |
| ---------------------------------- | ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------- |
| `member_description` | Raw | Job position description | String |
| `company_id` | Enriched | Identification key for the company associated with the employee's experience | Integer |
| `member_job_title` | Cleaned | Current job position title | String |
| `is_decision_maker` | Enriched |
Indicates whether the employee is a decision-maker based on member\_job\_title 1 – Employee is marked as a decision-maker in the current role 0 – Employee is not marked as a decision-maker in the current role
| Integer |
| `member_job_description` | Raw | Current job position description | String |
| `member_headline` | Raw | Job title found in the profile headline | String |
| `member_generated_headline` | Raw |
A user-written headline that can be found in web search, also viewed and other publicly available spaces. It serves the same purpose as the title but is derived from a different source, potentially providing more accurate and up-to-date profile information. This field should be used in placetitle as it reflects the latest user activity.
| String |
| `total_experience_duration` | Enriched | Summed up experience (displayed as years and months) | String |
| `total_experience_duration_months` | Enriched | Summed up employee experience (displayed as months) | Integer |
{% code title="Experience" %}
```json
"member_description": "Results-driven professional with extensive experience in supervisory roles, business analysis, project management, and financial analysis. Skilled in managing enterprise-wide implementations of healthcare information systems, with expertise in gathering and defining client data requirements.",
"company_id": 1111111,
"member_job_title": "Senior Consultant",
"is_decision_maker": 1,
"member_job_description": "Senior Business analyst @ Company123",
"member_headline": "Healthcare Consultant",
"member_generated_headline": "Healthcare Consultant at Company 123",
"total_experience_duration": "2 years 4 months",
"total_experience_duration_months": 28,
```
{% endcode %}
Cleaning and enriching actions
| Data field | Cleaning/enriching action |
| ---------------------------------- | ----------------------------------------------------------------------- |
| `company_id` | Company ID from an active experience record from `member_experience`*.* |
| `job_title` | Special characters are removed. |
| `total_experience_duration` | Values converted to readable text. |
| `total_experience_duration_months` | Field aggregated from `duration`values. |
{% hint style="warning" %}
The `member_experience` table is mapped with our historical data due to professional network hiding the work experience on certain employees' profiles.
{% endhint %}
| Data field | Processing | Description | Data type |
| ------------------------ | ---------- | ------------------------------------------------ | ---------------- |
| `member_experience` | - | Employee's work experience | Array of objects |
| `company_id` | Raw | Workplace (company) identifier in our database | Integer |
| `date_from` | Cleaned | Employment start date | String (date) |
| `date_from_year` | Cleaned | Employment start year | Integer |
| `date_from_month` | Cleaned | Employment start month | Integer |
| `date_to` | Cleaned | Employment end date | String (date) |
| `date_to_year` | Cleaned | Employment end year | Integer |
| `date_to_month` | Cleaned | Employment end month | Integer |
| `company_name` | Raw | Employer company | String |
| `company_url` | Raw | Employee's workplace URL on professional network | String |
| `company_shorthand_name` | - | Shorthand/vanity identifier for the company | String |
| `company_website` | Raw | Company website URL | String |
| `title` | Raw | Job title | String |
| `department` | Enriched | Department the employee works in | String |
| `management_level` | Enriched | Employee's management level | String |
| `description` | Cleaned | Job description | String |
| `order_in_profile` | Raw | Record order as seen on the employee's profile | Integer |
| `duration` | Enriched | Employment duration | String (date) |
| `duration_months` | Cleaned | Employment duration in months | Integer |
| `location` | Cleaned | Job/workplace location | String |
| `company_logo_url` | – | URL pointing to the logo of the company/employer | String |
{% code title="Experience" %}
```json
"member_experience": [
{
"company_id": 1774347,
"date_from": "2015-10-01",
"date_from_year": 2015,
"date_from_month": 10,
"date_to": "2016-09-01",
"date_to_year": 2016,
"date_to_month": 9,
"company_name": "Company123, Ltd.",
"company_url": "https://www.professional_network.com/company/company123",
"company_shorthand_name": "company123",
"company_website": "https://www.company123.com",
"title": "Senior Analyst",
"description": "Financialconsulting for a leading manufacturing organizations.",
"order_in_profile": 5,
"duration": "1 year",
"duration_months": 12,
"department": "Project Management",
"management_level": "Senior",
"location": "Jacksonville, Florida Area",
"company_logo_url": "https://media.licdn.com/dms/image/v2/example/company-logo"
}
],
```
{% endcode %}
Cleaning and enriching actions
| Data field | Cleaning/enriching action |
| ----------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `date_from` | Value is converted to the *yyyy-mm-dd* format. |
|
date\_from\_year, date\_from\_month
|
Year value extracted from date\_from value;
Value converted to integer.
|
| `date_to` | Value is converted to the *yyyy-mm-dd* format. |
|
date\_to\_year, date\_to\_month
|
Year value extracted from date\_to value;
Value converted to integer.
|
| `department` | Enriched with our ML model from the `title` value. |
| `management_level` | Enriched with our ML model from the `member_job_title` value. |
| `description` |
Values \["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None;
Value is replaced to None if the description is shorter than 3 characters;
Text styling tags removed;
Multiple spaces are replaced with single ones.
|
| `duration` | Derived from `date_from` and `date_to` values. |
| `duration_months` | Duration converted in numerical value. |
| `location` | Values *\["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"]* are replaced with value `None`. |
***
| Data field | Processing | Description | Data type |
| ------------------------- | ---------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------- |
| `member_department` | Enriched | Departments derived from the `member_job_title` | String |
| `member_management_level` | Enriched | Management levels identified from the `member_job_title` | String |
| `is_working` | Enriched |
Represents if the employee is currently working 0 – the employee is currently not working 1 – the employee is currently working
| Integer |
{% code title="Experience" %}
```json
"member_department": "Project Management",
"member_management_level": "Senior",
"is_working": 1
```
{% endcode %}
Enriching actions
| Data field | Cleaning/enriching action |
| ------------------------- | ----------------------------------------------------------------- |
| `member_department` | Enriched with our ML model from the `member_job_title` value. |
| `member_subdepartment` | Enriched with our ML model from the `member_job_title` value. |
| `member_management_level` | Enriched with our ML model from the `member_job_title` value. |
| `is_working` | Based on `date_to` and `date_from` values of employee experience. |
***
## Education
| Data field | Processing | Description | Data type |
| ---------------------------- | ---------- | ----------------------------------------------------------------------------------------------- | ---------------- |
| `member_education` | | Employee's education | Array of objects |
| `major` | Cleaned | Field of study | String |
| `title` | Cleaned | Educational institution | String |
| `date_to` | Cleaned | Graduation date | String |
| `date_from` | Cleaned | Enrolment date | String |
| `institution_url` | Cleaned | Institution's profile URL | String |
| `institution_id` | – | Internal institution identifier for education records | Long |
| `institution_shorthand_name` | – | Shorthand name from the institution profile URL | String |
| `institution_logo_url` | – | URL pointing to the logo of the educational institution (university, school, training provider) | String |
| `description` | Cleaned | Education description | String |
| `activities_and_societies` | Cleaned | Details about activities and societies | String |
{% code title="Education" %}
```json
"member_education": [
{
"major": "Associate's degree, Business Administration and Management",
"title": "Business College",
"date_to": "2017",
"date_from": "2015",
"institution_url": "https://www.professional_network.com/school/business-college",
"institution_id": 123123123000,
"institution_shorthand_name": "business-college",
"institution_logo_url": "https://media.licdn.com/dms/image/v2/example/institution-logo",
"description": "Attended Business College from 2015 to 2017",
"activities_and_societies": "Activities and Societies: Phi Theta Kappa"
}
],
```
{% endcode %}
Cleaning actions
| Data field | Cleaning action |
| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `title` |
Values \["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None;
Values are capitalized.
|
| `major` | Values *\["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"]* are replaced with value `None`. |
| `date_from` | Value is converted to the *yyyy* format. |
| `date_to` | Value is converted to the *yyyy* format. |
| `institution_url` | Values *\["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"]* are replaced with value `None`. |
| `description` |
Values \["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None;
Text styling tags are removed;
Multiple spaces are replaced with single ones.
|
| `activities_and_societies` |
Values \["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None;
Text styling tags are removed;
Multiple spaces are replaced with single ones.
|
***
## Hidden collections
| Data field | Description | Data type |
| ----------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `is_hidden` |
Marks if the employee profile has a hidden education/experience collection.
0 – education/experience information was available at the time of profile scraping. 1 – education/experience information was not available at the time of profile scraping
| Number (integer) |
{% tabs %}
{% tab title="is\_hidden + experience" %}
{% code title="is\_hidden + experience" %}
```json
"is_hidden": 0,
"member_experience": [
{
"company_id": 23124977,
"date_from": "2020-02-01",
"date_to": "2020-09-01"
},
{
"company_id": 3140930,
"date_from": "2023-06-01",
"date_to": null
}
]
}
```
{% endcode %}
{% endtab %}
{% tab title="is\_hidden + education" %}
{% code title="is\_hidden + education" %}
```json
"member_education": [
{
"title": "Harvard Law School",
"major": null,
"date_from": null,
"date_to": null
}
],
"is_working": 1,
```
{% endcode %}
{% endtab %}
{% endtabs %}
***
## Location
| Data field | Processing | Description | Data type |
| ------------------------------- | ---------- | -------------------------------------------------- | --------- |
| `member_location_raw_address` | Cleaned | Raw address of the employee's location | String |
| `member_location_country` | Cleaned | Country of the employee's location | String |
| `member_location_regions` | Cleaned | Geographical regions within the employee's country | String |
| `member_location_city` | Cleaned | Employee location city | String |
| `member_location_state` | Cleaned | Employee location state | String |
| `member_location_country_iso_2` | – | ISO 2-letter code of the location country | String |
| `member_location_country_iso_3` | – | ISO 3-letter code of the location country | String |
{% code title="Location" %}
```json
"member_location_raw_address": "Nashville Metropolitan Area United States",
"member_location_country": "United States",
"member_location_regions": "Northern America",
"member_location_city": "Nashville",
"member_location_state": "Tennessee",
"member_location_country_iso_2": "US",
"member_location_country_iso_3": "USA"
```
{% endcode %}
Cleaning actions
| Data field | Cleaning action |
| ---------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `location_raw_address` |
Values \["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None;
Special trailed characters are trimmed;
Value is set to None if it is shorter than three characters;
The value of member\_location\_country is added at the end of the string.
|
| `location_country` | Values *\["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"]* are replaced with value `None`. |
***
## Recommendations and connections
| Data field | Processing | Description | Data type |
| ------------------------------ | ---------- | ---------------------------------- | ---------------- |
| `member_recommendations` | Cleaned | Employee recommendations | Array of objects |
| `recommendation` | Cleaned | Recommendation text | String |
| `referee_name` | Raw | Referee's name | String |
| `referee_url` | Raw | Referee's profile URL | String |
| `member_recommendations_count` | Cleaned | Number of received recommendations | Integer |
| `member_connections_count` | Raw | Number of employee's connections | Integer |
{% code title="Recommendations and connections" %}
```json
"member_recommendations": [
{
"recommendation": "“John was a great asset in collaborating the tasks in different departments to produce the same goal. He was great at providing advice and asking questions to avoid even a tiny error during the process. Great to work with him!”",
"referee_name": "Marry Doe",
"referee_url": "www.professional_network.com/marry-doe",
"order_in_profile": 1
}
],
"member_recommendations_count": 1,
"member_connections_count": 15535,
```
{% endcode %}
Cleaning actions
| Data field | Cleaning action |
| ------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `member_recommendations` | Deleted rows are filtered out. |
| `recommendation` |
Values \["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None;
Value is set to None if it is shorter than three characters;
Text styling tags are removed;
Multiple spaces are replaced with single ones;
Empty recommendations are filtered out.
|
| `member_recommendations_count` |
Values \["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None;
None values are replaced with 0 and made an integer.
|
***
## Languages
| Data field | Processing | Description | Data type |
| ------------------ | ---------- | ----------------------------- | ---------------- |
| `member_languages` | | Employee's language knowledge | Array of objects |
| `language` | Cleaned | Language | String |
| `proficiency` | Cleaned | Language proficiency | String |
| `order_in_profile` | Raw | Record order in the section | Integer |
{% code title="Languages" %}
```json
"member_languages": [
{
"language": "English",
"proficiency": "Intermediate",
"order_in_profile": 1
}
],
```
{% endcode %}
Cleaning actions
| Data field | Cleaning action |
| ------------- | -------------------------------------------------------------------------------------------------------------------- |
| `language` | Values *\["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"]* are replaced with value `None`. |
| `proficiency` | Values *\["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"]* are replaced with value `None`. |
***
## Certifications
| Data field | Processing | Description | Data type |
| ----------------------- | ---------- | --------------------------------------------------------------------------------------- | ---------------- |
| `member_certifications` | | Employee's certifications | Array of objects |
| `title` | Cleaned | Language | String |
| `issuer` | Cleaned | Language proficiency | String |
| `credential_id` | Cleaned | Record order in the section | String |
| `certificate_url` | Cleaned | Certificate URL | String |
| `certificate_logo_url` | – | URL pointing to the logo of the certification provider (AWS, Microsoft, Coursera, etc.) | String |
| `date_from` | Cleaned | Issue date | String |
| `date_to` | Cleaned | Expiration date | String |
| `issuer_url` | Cleaned | Issuer profile URL | String |
| `order_in_profile` | Raw | Section record order | Integer |
| `date_from_year` | Cleaned | Issue year | Integer |
| `date_from_month` | Cleaned | Issue month | Integer |
| `date_to_year` | Cleaned | Expiration year | Integer |
| `date_to_month` | Cleaned | Expiration month | Integer |
{% code title="Certifications" %}
```json
"member_certifications": [
{
"title": "Data Analysis Certification B4",
"issuer": "Data School123",
"credential_id": "1345",
"certificate_url": "http://data-analysis-certification-school123.com/verify?trk=public_profile_certification-title",
"certificate_logo_url": "https://media.licdn.com/dms/image/v2/example/certificate-logo",
"date_from": "2021-06-01",
"date_to": "2024-06-01",
"issuer_url": "https://www.professional_network.com/company/data-school-123",
"order_in_profile": 1,
"date_from_year": 2021,
"date_from_month": 6,
"date_to_year": 2024,
"date_to_year": 6
}
],
```
{% endcode %}
Cleaning actions
| Data field | Cleaning action |
| ---------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------- |
| `title` | Values *\["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"]* are replaced with value `None`. |
| `issuer` | Values *\["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"]* are replaced with value `None`. |
| `date_from` | Value is converted to the *yyyy-mm-dd* format. |
| `date_to` | Value is converted to the *yyyy-mm-dd* format. |
| `issuer_url` | Values *\["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"]* are replaced with value `None`. |
|
date\_from\_year, date\_to\_year
| Year value from `date` is converted to an integer. |
|
date\_from\_month, date\_to\_month
| Month value from `date` is converted to an integer. |
***
## Courses
| Data field | Processing | Description | Data type |
| ------------------ | ---------- | --------------------------- | ---------------- |
| `member_courses` | | Attended courses | Array of objects |
| `organizer` | Cleaned | Course organizer | String |
| `title` | Cleaned | Course title | String |
| `order_in_profile` | Raw | Record order in the section | Integer |
{% code title="Courses" %}
```json
"member_courses": [
{
"organizer": "IT Academy",
"title": "Microsoft Certified Excel Expert",
"order_in_profile": 1
}
],
```
{% endcode %}
Cleaning actions
| Data field | Cleaning action |
| ----------- | -------------------------------------------------------------------------------------------------------------------- |
| `organizer` | Values *\["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"]* are replaced with value `None`. |
| `title` | Values *\["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"]* are replaced with value `None`. |
***
## Awards
| Data field | Processing | Description | Data type |
| ------------------ | ---------- | -------------------- | ---------------- |
| `member_awards` | | Held awards | Array of objects |
| `title` | Cleaned | Award | String |
| `issuer` | Cleaned | Award issuer | String |
| `description` | Cleaned | Award description | String |
| `date` | Cleaned | Issue date | String |
| `order_in_profile` | Raw | Section record order | Integer |
| `date_year` | Cleaned | Issue year | Integer |
| `date_month` | Cleaned | Issue month | Integer |
| `date_day` | Cleaned | Issue day | Integer |
{% code title="Awards" %}
```json
"member_awards": [
{
"title": "Certified in Inventory Management",
"issuer": "School of Operations Management",
"description": "Certification in Production and Inventory Management",
"date": "2001-01-01",
"order_in_profile": 5,
"date_year": 2001,
"date_month": 1,
"date_day": 1
}
],
```
{% endcode %}
Cleaning actions
| Data field | Cleaning action |
| ------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `title` |
Values \["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None;
Values are capitalized.
|
| `issuer` | Values *\["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"]* are replaced with value `None`. |
| `date` | Value is converted to the *yyyy-mm-dd* format. |
| `date_year` | Year value from `date` is converted to an integer. |
| `date_month` | Month value from `date` is converted to an integer. |
***
## Activity
| Data field | Processing | Description | Data type |
| ------------------ | ---------- | ---------------------------------------------- | ---------------- |
| `member_activity` | | Interaction with posts on professional network | Array of objects |
| `activity_url` | Raw | Post URL | String |
| `title` | Cleaned | Post title | String |
| `action` | Cleaned | Interaction type | String |
| `order_in_profile` | Raw | Section record order | Integer |
{% code title="Activity" %}
```json
"member_activity": [
{
"activity_url": "https://www.professional_network.com/posts/company123-incorporated_healthcare-laborproductivity-activity-7161365554581172224-XUpZ",
"title": "Company 123 is excited to introduce our Team Spotlight featuring John Doe! @Health Systems, Ltd #Healthcare #LaborProductivity",
"action": "Liked by",
"order_in_profile": 1
}
],
```
{% endcode %}
Cleaning actions
| Data field | Cleaning action |
| ----------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
title
|
Values \["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None;
Text styling tags removed;
Multiple spaces are replaced with single ones.
|
## Organizations
| Data field | Description | Data type |
| ---------------------- | ---------------------------------------------------------- | ---------------- |
| `member_organizations` | Memberships in organizations | Array of structs |
| `organization` | Organization title | String |
| `position` | Position in the organization | String |
| `description` | Description of the activity/experience in the organization | String |
| `date_from` | Membership start date | String |
| `date_from_year` | Membership start year | Integer |
| `date_from_month` | Membership start month | Integer |
| `date_to` | Membership end date | String |
| `date_to_year` | Membership end year | Integer |
| `date_to_month` | Membership end month | Integer |
| `order_in_profile` | The exact position of the organization in the profile | Integer |
{% code title="Organizations" %}
```json
"member_organizations": [
{
"organization": "Example Organization",
"position": "Lead Software Engineer",
"description": "Led a team of developers providing great services.",
"date_from": "2019-06",
"date_from_year": 2019,
"date_from_month": 6,
"date_to": "2023-09",
"date_to_year": 2023,
"date_to_month": 9,
"order_in_profile": 1
}
],
```
{% endcode %}
***
## Patents
| Data field | Description | Data type |
| ------------------------------ | ----------------------------------------------- | ---------------- |
| `member_patents` | Authored patents | Array of structs |
| `title` | Patent title | String |
| `status` | Patent status | String |
| `inventors` | Inventors of the patent | Array of structs |
| `full_name` | Full name of the inventor | String |
| `profile_url` | Profile URL | String |
| `order_in_profile` | Order in profile | Integer |
| `date` | Patent filing date | String |
| `date_year` | Filling year | Integer |
| `date_month` | Filling month | Integer |
| `date_day` | Filling day | Integer |
| `patent_url` | Patent URL | String |
| `description` | Patent description | String |
| `patent_or_application_number` | Patent or application number | String |
| `order_in_profile` | The exact position of the patent in the profile | Integer |
{% code title="Patents" %}
```json
"member_patents": [
{
"title": "Data Synchronization System",
"status": "Granted",
"inventors": [
{
"full_name": "John Doe",
"profile_url": "https://www.professional-network.com/profile/johndoe",
"order_in_profile": 1
},
{
"full_name": "Jane Smith",
"profile_url": "https://www.professional-network.com/profile/janesmith",
"order_in_profile": 2
}
],
"date": "2022-01-01",
"date_year": 2022,
"date_month": 1,
"date_day": 1,
"patent_url": "https://wwww.patents.example.com/US1234567",
"description": "A method for efficient synchronization of distributed systems in real-time environments.",
"patent_or_application_number": "US1234567B2",
"order_in_profile": 1
}
```
{% endcode %}
***
## Publications
| Data field | Description | Data type |
| --------------------- | ---------------------------------------------------- | ---------------- |
| `member_publications` | Memberships in organizations | Array of structs |
| `title` | Publication title | String |
| `publisher` | Publisher name | String |
| `date` | Publication release date | String |
| `date_year` | Release year | Integer |
| `date_month` | Release month | Integer |
| `date_day` | Release day | Integer |
| `description` | Publication description | String |
| `authors` | Authors of the publication | Array of structs |
| `full_name` | Full name of the author | String |
| `profile_url` | Profile URL | String |
| `order_in_profile` | Order in the profile | Integer |
| `publication_url` | Publication website URL | String |
| `order_in_profile` | The exact position of the publication in the profile | Integer |
{% code title="Publications" %}
```json
"member_publications": [
{
"title": "Microservices Architecture in Cloud Environments",
"publisher": "Journal of Software Systems",
"date": "2024-08-01",
"date_year": 2024,
"date_month": 8,
"date_day": 1,
"description": "An in-depth analysis of architectural patterns and scalability challenges in cloud-native microservices.",
"authors": [
{
"full_name": "John Doe",
"profile_url": "https://www.professional-network.com/profile/johndoe",
"order_in_profile": 1
}
],
"publication_url": "https://www.publications.example.com/microservices-architecture",
"order_in_profile": 1
}
]
}
```
{% endcode %}
# Sample: Clean Employee Data
Review Coresignal's Clean Employee Data sample below or [contact sales](https://coresignal.com/contact-us/?utm_source=web\&utm_medium=public-docs\&utm_campaign=data-consultation) for more information.
Interested in checking out more data samples? **Visit our self-service platform**:
* Visit Clean Employee API playground
* Search or download employee data
* No credit card required
Start 7-day free trial
{% hint style="info" %}
All personal/company information mentioned within this context is entirely fictional and is solely intended for illustrative purposes.
{% endhint %}
{% code title="Clean employee sample" %}
```json
{
"member_id": 1234,
"member_full_name": "Jane A. Doe",
"member_name_first": "Jane",
"member_name_middle": "A.",
"member_name_last": "Doe",
"member_websites_professional_network": "https://www.professional_network.com/profile/janedoe",
"member_picture_url": "https://www.example.com/janedoe.jpg",
"member_description": "Experienced data analyst with a passion for insights.",
"member_job_title": "Data Analyst",
"is_decision_maker": 1,
"member_job_description": "Analyzing large datasets to extract meaningful insights.",
"company_id": 67890,
"member_experience": [
{
"title": "Senior Data Analyst",
"description": "Leading data analysis projects.",
"order_in_profile": 1,
"company_name": "Example Company",
"company_url": "https://www.professional_network.com/example-company",
"company_shorthand_name": "example-company",
"company_website": "https://www.example-company.com",
"company_logo_url": "https://media.licdn.com/dms/image/v2/example/company-logo",
"company_id": 987654,
"date_from": "2019-06-01",
"date_from_year": 2019,
"date_from_month": 6,
"date_to": "2023-08-01",
"date_to_year": 2023,
"date_to_month": 8,
"location": "San Francisco, CA",
"duration": "4 years 2 months",
"duration_months": 50,
"department": "Engineering and Technical",
"management_level": "Senior"
}
],
"member_education": [
{
"title": "Bachelor of Science in Computer Science",
"major": "Computer Science",
"date_from": "2015-09-01",
"date_to": "2019-05-01",
"description": "Studied data structures, algorithms, and machine learning.",
"activities_and_societies": "AI Club, Coding Bootcamp",
"institution_url": "https://www.professional_network.com/example-university",
"institution_id": 123123123000,
"institution_shorthand_name": "example-university",
"institution_logo_url": "https://media.licdn.com/dms/image/v2/example/institution-logo"
}
],
"member_languages": [
{
"language": "English",
"proficiency": "Native",
"order_in_profile": 1
}
],
"member_certifications": [
{
"title": "Certified Data Analyst",
"issuer": "Example Institute",
"credential_id": "DA123456",
"certificate_url": "https://www.example-certificates.com/DA123456",
"certificate_logo_url": "https://media.licdn.com/dms/image/v2/example/certificate-logo",
"date_from": "2020-05-01",
"date_from_year": 2020,
"date_from_month": 5,
"date_to": "2025-05-01",
"date_to_year": 2025,
"date_to_month": 5,
"issuer_url": "https://www.example-institute.com",
"order_in_profile": 1
}
],
"member_courses": [
{
"organizer": "Example Courses",
"title": "Machine Learning",
"order_in_profile": 1
}
],
"member_awards": [
{
"title": "Best Data Scientist Award",
"issuer": "Example Company",
"description": "Recognized for outstanding contributions to data analytics.",
"date": "2022-12-01",
"date_year": 2022,
"date_month": 12,
"date_day": 1,
"order_in_profile": 1
}
],
"member_activity": [
{
"activity_url": "https://www.professional_network.com/posts/example-company-123456",
"title": "Example Company is excited to introduce our Team Spotlight featuring Jane Doe!",
"action": "Liked by",
"order_in_profile": 1,
}
],
"member_skills": ["Python," "SQL," "Data Visualization"],
"member_recommendations": [
{
"recommendation": "Jane is a top-notch analyst with deep insights into big data.",
"referee_url": "https://www.professional-network.com/profile/johndoe",
"order_in_profile": 1,
"referee_name": "John Doe"
}
],
"member_recommendations_count": 5,
"member_connections_count": 500,
"member_location_raw_address": "123 Main St, New York, NY",
"member_location_country": "USA",
"member_location_regions": "New York, NY",
"member_location_city": "New York",
"member_location_state": "NY",
"member_location_country_iso_2": "US",
"member_location_country_iso_3": "USA",
"member_last_updated": "2026-05-01",
"processed_at": "2026-05-02",
"member_is_deleted": 0,
"member_department": "Data Science",
"member_management_level": "Senior",
"is_working": 1,
"is_hidden": 0,
"member_generated_headline": "Expert in Big Data & Machine Learning",
"member_headline": "Senior Data Analyst at Example Company",
"member_shorthand_names": ["JaneD", "JDoe"],
"member_follower_count": 150,
"total_experience_duration": "4 years",
"total_experience_duration_months": 48,
"member_public_profile_id": "123456789",
"member_organizations": [
{
"organization": "Example Organization",
"position": "Lead Software Engineer",
"description": "Led a team of developers providing great services.",
"date_from": "2019-06",
"date_from_year": 2019,
"date_from_month": 6,
"date_to": "2023-09",
"date_to_year": 2023,
"date_to_month": 9,
"order_in_profile": 1
}
],
"member_patents": [
{
"title": "Data Synchronization System",
"status": "Granted",
"inventors": [
{
"full_name": "John Doe",
"profile_url": "https://www.professional-network.com/profile/johndoe",
"order_in_profile": 1
},
{
"full_name": "Jane Smith",
"profile_url": "https://www.professional-network.com/profile/janesmith",
"order_in_profile": 2
}
],
"date": "2022-01-01",
"date_year": 2022,
"date_month": 1,
"date_day": 1,
"patent_url": "https://wwww.patents.example.com/US1234567",
"description": "A method for efficient synchronization of distributed systems in real-time environments.",
"patent_or_application_number": "US1234567B2",
"order_in_profile": 1
}
],
"member_publications": [
{
"title": "Microservices Architecture in Cloud Environments",
"publisher": "Journal of Software Systems",
"date": "2024-08-01",
"date_year": 2024,
"date_month": 8,
"date_day": 1,
"description": "An in-depth analysis of architectural patterns and scalability challenges in cloud-native microservices.",
"authors": [
{
"full_name": "John Doe",
"profile_url": "https://www.professional-network.com/profile/johndoe",
"order_in_profile": 1
}
],
"publication_url": "https://www.publications.example.com/microservices-architecture",
"order_in_profile": 1
}
]
}
```
{% endcode %}
# Base Employee Data
Base Employee Data — fresh professional profiles scraped on an ongoing basis. Ideal for HR tech, lead generation, and talent intelligence.
Base Employee Data is designed to be used in **HR tech, lead generation, and HR intelligence**.
| **Talent sourcing** | Build data-driven HR technology tools and enhance your talent intelligence using data about global talent. |
| --------------------------- | ---------------------------------------------------------------------------------------------------------- |
| **Investment intelligence** | Discover promising talent and analyze changes in specific companies or industries. |
| **Lead enrichment** | Get comprehensive employee data at scale and fill in the blanks with up-to-date information. |
***
## Summary
| Feature | Details |
| ------------------ | ------------------- |
| Available via | Flat files/API |
| Delivery frequency | Daily and monthly |
| Available formats | JSONL, Parquet, CSV |
| Scraping since | 2016-07 |
{% hint style="info" %}
Employee profiles are constantly being scraped from a queue of profiles, prioritizing the ones with recent changes. Some profiles are no longer accessible.\
As such, a complete data refresh is not feasible.
{% endhint %}
## Related links
# Dictionary: Base Employee Data
Complete data dictionary for Base Employee Data – all fields explained across experience, education, certifications, skills, and fresh profile metadata.
This data dictionary displays all available data fields, explains their values, and provides snippets of data from the Employee dataset.
{% tabs %}
{% tab title="Data fields per category" %}
1. [Root table](#root-table)
2. [Awards](#awards)
3. [Certifications](#certifications)
4. [Courses](#courses)
5. [Education](#education)
6. [Experience](#experience)
7. [Languages](#languages)
8. [Organizations](#organizations)
9. [Patents](#patents)
10. [Projects](#projects)
11. [Publications](#publications)
12. [Recommendations](#recommendations)
13. [Similar profiles](#similar-profiles)
14. [Others named](#others-named)
15. [Test scores](#test-scores)
16. [Volunteering positions](#volunteering-positions)
17. [Websites](#websites)
18. [Course suggestions](#course-suggestions)
19. [Activity](#activity)
20. [Inferred skills](#inferred-skills)
{% endtab %}
{% tab title="Legacy data – obtain on demand" %}
{% hint style="danger" %}
The following legacy categories are excluded from Base Employee schema and data dumps. This data can be obtained only on demand.
{% endhint %}
1. [Also viewed](#also-viewed)
2. [Groups](#groups)
3. [Interests](#interests)
4. [See more URLs](#see-more-urls)
5. [Skills](#skills)
6. [Volunteering cares](#volunteering-cares)
7. [Volunteering opportunities](#volunteering-opportunities)
8. [Volunteering supports](#volunteering-supports)
{% endtab %}
{% endtabs %}
{% hint style="info" %}
All personal/company information mentioned within this context is entirely fictional and is solely intended for illustrative purposes.
{% endhint %}
### Root table
| Data field | Description | Data type |
| -------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `id` | Record identification key | Long |
| `parent_id` | Employee profile identification key | Long |
| `full_name` | Full name | String |
| `first_name` | First name | String |
| `last_name` | Last name | String |
| `headline` | Employee headline | String |
| `checked_at` | Timestamp of the last time the profile was checked for key changes in `ISO 8601` format | String (date) |
| `public_profile_id` | Employee URN provided by professional network | String |
| `profile_url` | Employee profile URL | String |
| `location` | Displayed location | String |
| `city` | Employee location city | String |
| `state` | Employee location state | String |
| `industry` |
Associated industry
Note: legacy field
| String |
| `summary` | Professional experience summary | String |
| `services` | Offered services | String |
| `recommendations_count` | Number of recommendations | Integer |
| `profile_photo_url` | Profile photo URL | String |
| `created_at` | Record creation timestamp in `ISO 8601` format | String (date) |
| `updated_at` | Record update timestamp in `ISO 8601` format | String (date) |
| `processed_at` | Exposes when data was processed on our side (reparsing, reloads, data quality fixes) | Timestamp |
| `deleted` |
Status indicating if the profile is publicly available: 1 – The last time we scraped the profile, the page that was not found was returned. 0 – The last time we scraped the profile, the record was added to our data and marked with the value 0
| Number (integer) |
| `country` | Displayed location (country) parsed by us from the location value | String |
| `country_iso_2` | ISO 3166-1 alpha-2 country code | String |
| `country_iso_3` | ISO 3166-1 alpha-3 country code | String |
| `regions` | Region information (additional location information) | Array of objects |
| `region` | Location region | String |
| `connections_count` | Number of active connections | Number (integer) |
| `follower_count` | Employee follower count | Number (integer) |
| `experience_count` | Number of experience records | Number (integer) |
| `shorthand_name` |
Part of the employee URL used to identify employee profiles
Note: can be changed at any time by the user
| String |
| `canonical_shorthand_name` | The most recent version of the *shorthand\_name* | String |
| `shorthand_names` | A list of all historical `shorthand_names` that we captured | Array of objects |
| `shorthand_name` | Captured shorthand name | String |
| `historical_ids` | A list of all employee identification keys assigned to this record | Array of structs |
**Refer to the table example from the data:**
```json
{
"id": 101,
"parent_id": 101,
"full_name": "John Doe",
"first_name": "John",
"last_name": "Doe",
"headline": "Software Engineer",
"created_at": "2024-12-10T12:00:00Z",
"updated_at": "2026-05-10T12:00:00Z",
"checked_at": "2026-05-10T12:00:00Z",
"processed_at": "2026-05-11T12:00:00Z",
"public_profile_id": "422009225",
"profile_url": "http://example.com/johndoe",
"location": "New York, USA",
"city": "New York",
"state": "New York",
"industry": "Technology",
"summary": "Experienced software engineer with expertise in Python and Spark.",
"services": "Consulting, Development",
"profile_photo_url": "http://example.com/johndoe/photo.jpg",
"deleted": 0,
"country": "USA",
"country_iso_2": "US",
"country_iso_3": "USA",
"regions": [
{
"region": "Northeast"
}
],
"recommendations_count": 10,
"connections_count": 500,
"follower_count": 1500,
"experience_count": 5,
"shorthand_name": "johndoe",
"canonical_shorthand_name": "johndoe",
"shorthand_names": [
{
"shorthand_name": "johndoe"
}
],
"historical_ids": [
{
"id": 1001
}
]
}
```
***
| Data field | Description | Data type |
| ---------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
|
is\_parent
|
Represents if the employee ID is the original ID in our dataset (not a duplicate) 1 – original ID 0 – duplicate ID
| Number (integer) |
**Refer to the table example from the data:**
```json
"is_parent": 1
```
### Awards
| Data field | Description | Data type |
| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------- |
| `awards` | Awards | Array of objects |
| `id` | Record UUID identifier | String |
| `title` | Award title | String |
| `issuer` | Award issuer | String |
| `description` | Award description | String |
| `date` |
Issue date
Note: date format vary
| String (date) |
| `date_year` | Issue year | Number (integer) |
| `date_month` | Issue month | Number (integer) |
| `date_day` | Issue day | Number (integer) |
| `order_in_profile` | Section record order | Number (integer) |
| `created_at` | Record creation timestamp in `ISO 8601` format | String (date) |
| `updated_at` | Record update timestamp in `ISO 8601` format | String (date) |
| `deleted` |
Status indicating if the record is publicly available: 1 – The record was publicly unavailable the last time we scraped the profile 0 – The last time we scraped the profile, the record was available and added to the profile's data
| Number (integer) |
**See a snippet of the dataset for reference:**
{% code title="Awards" %}
```json
"awards": [
{
"id": "c8f68c94-1c90-4b36-8e0e-905cf2e1283e",
"title": "Best Innovator",
"issuer": "Tech Conference 2023",
"description": "Awarded for outstanding innovation in technology.",
"date": "2023-11-15",
"date_year": 2023,
"date_month": 11,
"date_day": 15,
"order_in_profile": 1,
"deleted": 0,
"created_at": "2023-11-15T10:00:00Z",
"updated_at": "2023-11-15T12:00:00Z"
}
]
```
{% endcode %}
### Certifications
| Data field | Description | Data type |
| ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------- |
| `certifications` | Certifications | Array of objects |
| `id` | Record UUID identifier | String |
| `title` | Certification title | String |
| `issuer` | Certification issuer | String |
| `credential_id` | Credential identification key | String |
| `certificate_url` | Certification URL | String |
| `certificate_logo_url` | URL pointing to the logo of the certification provider (AWS, Microsoft, Coursera, etc.) | String |
| `issuer_url` | Issuer profile URL | String |
| `order_in_profile` | Section record order | Number (integer) |
| `date_from` | Certification issue date | String (date) |
| `date_from_year` | Issue year | Number (integer) |
| `date_from_month` | Issue month | Number (integer) |
| `date_to` | Certification expiry date | String (date) |
| `date_to_year` | Expiry year | Number (integer) |
| `date_to_month` | Expiry month | Number (integer) |
| `created_at` | Record creation timestamp in `ISO 8601` format | String (date) |
| `updated_at` | Record update timestamp in `ISO 8601` format | String (date) |
| `deleted` |
Status indicating if the record is publicly available: 1 – The record was publicly unavailable the last time we scraped the profile. 0 – The last time we scraped the profile, the record was available and added to the profile's data
| Number (integer) |
**See a snippet of the dataset for reference:**
{% code title="Certifications" %}
```json
"certifications": [
{
"id": "f1c4f870-8c4e-456c-a5e6-892a2be9a3b2",
"title": "Certified Data Scientist",
"issuer": "Data Science Institute",
"credential_id": "6245866",
"certificate_url": "https://example.com/certificates/dsi-2023-001",
"certificate_logo_url": "https:/example-images.com/dms/image/v2/certificate",
"date_from": "2023-01-01",
"date_from_year": 2023,
"date_from_month": 1,
"date_to": "2025-01-01",
"date_to_year": 2025,
"date_to_month": 1,
"issuer_url": "https://datascienceinstitute.org",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2023-01-01T10:00:00Z",
"updated_at": "2023-01-01T12:00:00Z"
}
]
```
{% endcode %}
### Courses
| Data field | Description | Data type |
| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------- |
| `courses` | Courses | Array of objects |
| `id` | Record UUID identifier | String |
| `organizer` | Course organizer | String |
| `title` | Course title | String |
| `order_in_profile` | Section record order | Number (integer) |
| `created_at` | Record creation timestamp in `ISO 8601` format | String (date) |
| `updated_at` | Record update timestamp in `ISO 8601` format | String (date) |
| `deleted` |
Status indicating if the record is publicly available: 1 – The record was publicly unavailable the last time we scraped the profile. 0 – The last time we scraped the profile, the record was available and added to the profile's data
| Number (integer) |
**See a snippet of the dataset for reference:**
{% code title="Courses" %}
```json
"courses": [
{
"id": "d0f7e8e5-abc3-4e9b-8e0c-2a8b80b9f5e9",
"organizer": "Coursera",
"title": "Introduction to Machine Learning",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2023-05-01T10:00:00Z",
"updated_at": "2023-05-01T12:00:00Z"
}
]
```
{% endcode %}
### Education
| Data field | Description | Data type |
| ---------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------- |
| `education` | Education details | Array of objects |
| `id` | Record UUID identifier | String |
| `institution` | Education institution | String |
| `program` | Program title | String |
| `institution_id` | ID of the company (institution) relating to the company table | Number (integer, int64) |
| `institution_source_id` | Record identification key assigned by professional network | Number (integer, int64) |
| `date_from` | Enrollment date | String (date) |
| `date_from_year` | Enrollment year | Number (integer) |
| `date_to` | Graduation date | String (date) |
| `date_to_year` | Graduation year | Number (integer) |
| `activities_and_societies` | Activities and societies considered part of education | String |
| `description` | Education experience described by the person | String |
| `created_at` | Record creation timestamp in `ISO 8601` format | String (date) |
| `updated_at` | Record update timestamp in `ISO 8601` format | String (date) |
| `deleted` |
Status indicating if the record is publicly available: 1 – The record was publicly unavailable the last time we scraped the profile. 0 – The last time we scraped the profile, the record was available and added to the profile's data
| Number (integer) |
| `institution_url` | Institution profile URL | String |
| `institution_shorthand_name` | Shorthand name from the institution profile URL | String |
| `institution_logo_url` | URL pointing to the logo of the educational institution (university, school, training provider) | String |
| `order_in_profile` | Section record order | Number (integer) |
| `deleted_at` | Indicates when an education record was no longer available on the profile | Timestamp |
**See a snippet of the dataset for reference:**
{% code title="Education" %}
```json
"education": [
{
"id": "d1e2f3b4-56a7-8901-bcde-234567890abc",
"institution": "Harvard University",
"program": "Bachelor of Science in Computer Science",
"institution_id": 1026807,
"institution_source_id": 166807,
"date_from": "2015-09-01",
"date_from_year": 2015,
"date_to": "2019-06-01",
"date_to_year": 2019,
"activities_and_societies": "Member of the Computer Science Club and Debate Team",
"description": "Studied core computer science concepts including algorithms, machine learning, and distributed systems.",
"institution_url": "https://www.harvard.edu",
"institution_shorthand_name": "harvard",
"institution_logo_url": "https://example-images.com/dms/image/v2/education",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2015-09-01T10:00:00Z",
"updated_at": "2019-06-01T12:00:00Z",
"deleted_at": null
}
]
```
{% endcode %}
### Experience
| Data field | Description | Data type |
| ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------- |
| `experience` | Work experience | Array of objects |
| `id` | Record UUID identifier | String |
| `title` | Job title | String |
| `location` | Job (company) location | String |
| `company_id` | ID of the company relating to the company table | Number (integer, int64) |
| `company_source_id` | Record identification key assigned by professional network | Number (integer, int64) |
| `company_name` | Workplace name | String |
| `company_shorthand_name` | Shorthand name from the company profile URL | String |
| `company_url` | The profile URL of the workplace | String |
| `company_logo_url` | URL pointing to the logo of the company/employer | String |
| `date_from` | Employment start date | String (date) |
| `date_from_year` | Employment start year | Number (integer) |
| `date_from_month` | Employment start month | Number (integer) |
| `date_to` | Employment end date | String (date) |
| `date_to_year` | Employment end year | Number (integer) |
| `date_to_month` | Employment end month | Number (integer) |
| `is_current` |
Marks if the record is for current employment
1 – Current workplace
0 – Former workplace
| Boolean |
| `duration` | Employment duration | String |
| `description` | Job or job experience description | String |
| `created_at` | Record creation timestamp in `ISO 8601` format | String (date) |
| `updated_at` | Record update timestamp in `ISO 8601` format | String (date) |
| `deleted` |
Status indicating if the record is publicly available: 1 – The record was publicly unavailable the last time we scraped the profile. 0 – The last time we scraped the profile, the record was available and added to the profile's data
| Number (integer) |
| `order_in_profile` | Section record order | Number (integer) |
| `deleted_at` | Indicates when an experience record was no longer available on the profile | Timestamp |
**See a snippet of the dataset for reference:**
{% code title="Experience" %}
```json
"experience": [
{
"id": "a1b2c3d4-e5f6-7890-ab12-cd34ef567890",
"title": "Senior Software Engineer",
"location": "San Francisco, CA",
"company_id": 91823973,
"company_source_id": 1015745,
"company_name": "Tech Innovations Inc.",
"company_url": "https://www.techinnovations.com",
"company_shorthand_name": "tech-innovations",
"company_logo_url": "https://example-images.com/dms/image/v2/company-logo",
"date_from": "2019-05-01",
"date_from_year": 2019,
"date_from_month": 5,
"date_to": "2023-03-01",
"date_to_year": 2023,
"date_to_month": 3,
"is_current": 0,
"duration": "3 years 10 months",
"description": "Led a team of engineers to develop scalable web applications and improve system performance by 40%.",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2019-05-01T08:00:00Z",
"updated_at": "2023-03-01T17:00:00Z",
"deleted_at": null
}
]
```
{% endcode %}
### Languages
| Data field | Description | Data type |
| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------- |
| `languages` | Language knowledge | Array of objects |
| `id` | Record UUID identifier | String |
| `language` | Listed language | String |
| `proficiency` | Language proficiency | String |
| `order_in_profile` | Section record order | Number (integer) |
| `created_at` | Record creation timestamp in `ISO 8601` format | String (date) |
| `updated_at` | Record update timestamp in `ISO 8601` format | String (date) |
| `deleted` |
Status indicating if the record is publicly available: 1 – The record was publicly unavailable the last time we scraped the profile. 0 – The last time we scraped the profile, the record was available and added to the profile's data
| Number (integer) |
**See a snippet of the dataset for reference:**
{% code title="Languages" %}
```json
"languages": [
{
"id": "d3f4e5b6-7890-ab12-cd34-ef5678901234",
"language": "English",
"proficiency": "Native",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2022-01-01T10:00:00Z",
"updated_at": "2022-01-01T12:00:00Z"
}
]
```
{% endcode %}
### Organizations
| Data field | Description | Data type |
| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------- |
| `organizations` | Memberships in organizations | Array of objects |
| `id` |
Record UUID identifier
| String |
| `organization` |
Organization title
Note: Contains HTML tags
| String |
| `position` |
Position in the organization
Note: Contains HTML tags
| String |
| `description` | Description of the activity/experience in the organization | String |
| `date_from` | Membership start date | String (date) |
| `date_from_year` | Membership start year | Number (integer) |
| `date_from_month` | Membership start month | Number (integer) |
| `date_to` | Membership end date | String (date) |
| `date_to_year` | Membership end year | Number (integer) |
| `date_to_month` | Membership end month | Number (integer) |
| `order_in_profile` | Section record order | Number (integer) |
| `created_at` | Record creation timestamp in `ISO 8601` format | String (date) |
| `updated_at` | Record update timestamp in `ISO 8601` format | String (date) |
| `deleted` |
Status indicating if the record is publicly available: 1 – The record was publicly unavailable the last time we scraped the profile. 0 – The last time we scraped the profile, the record was available and added to the profile's data
| Number (integer) |
**See a snippet of the dataset for reference:**
{% code title="Organizations" %}
```json
"organizations": [
{
"id": "b1a2c3d4-5678-90ab-cdef-1234567890ab",
"organization": "Tech Community Network",
"position": "Member",
"description": "Participated in monthly meetups and collaborative projects focused on technology innovation.",
"date_from": "2020-03-01",
"date_from_year": 2020,
"date_from_month": 3,
"date_to": "2023-03-01",
"date_to_year": 2023,
"date_to_month": 3,
"order_in_profile": 1,
"deleted": 0,
"created_at": "2020-03-01T08:00:00Z",
"updated_at": "2023-03-01T17:00:00Z"
}
],
```
{% endcode %}
### Patents
| Data field | Description | Data type |
| ------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `patents` | Authored patents | Array of objects |
| `id` | Record UUID identifier | String |
| `title` | Patent title | String |
| `status` | Patent status | String |
| `inventors` | Patent inventors | Array of objects |
| `full_name` | Inventor's name | String |
| `profile_url` | Inventor's profile URL | String |
| `order_in_profile` | Section record order | Number (integer) |
| `date` | Patent filing date | String (date) |
| `date_year` | Filling year | Number (integer) |
| `date_month` | Filling month | Number (integer) |
| `date_day` | Filling day | Number (integer) |
| `patent_url` | Patent URL | String |
| `description` |
Patent description
Note: contains HTML tags
| String |
| `patent_or_application_number` | Patent details, such as the area the patent is valid in | String |
| `order_in_profile` | Section record order | Number (integer) |
| `created_at` | Record creation timestamp in `ISO 8601` format | String (date) |
| `updated_at` | Record update timestamp in `ISO 8601` format | String (date) |
| `deleted` |
Status indicating if the record is publicly available: 1 – The record was publicly unavailable the last time we scraped the profile. 0 – The last time we scraped the profile, the record was available and added to the profile's data
| Number (integer) |
**See a snippet of the dataset for reference:**
{% code title="Patents" %}
```json
"patents": [
{
"id": "f1a2b3c4-d5e6-7890-ab12-cd34ef567890",
"title": "System for Optimized Data Processing",
"status": "Granted",
"inventors": [
{
"full_name": "John Doe",
"profile_url": "www.professional_network.com/johndoe",
"order_in_profile": 1
},
{
"full_name": "Jane Smith",
"profile_url": "www.professional_network.com/janesmith",
"order_in_profile": 2
}
],
"date": "2021-08-15",
"date_year": 2021,
"date_month": 8,
"date_day": 15,
"patent_url": "https://example.com/patents/123456",
"description": "An innovative system for efficient data processing in distributed environments.",
"patent_or_application_number": "US1234567890A",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2021-08-15T10:00:00Z",
"updated_at": "2023-03-01T12:00:00Z"
}
]
```
{% endcode %}
### Projects
| Data field | Description | Data type |
| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------- |
| `projects` | Projects | Array of objects |
| `id` | Record UUID identifier | String |
| `name` | Project name | String |
| `project_url` | Project website URL | String |
| `description` | Project description | String |
| `date_from` | Project start date | String (date) |
| `date_from_year` | Start year | Number (integer) |
| `date_from_month` | Start year | Number (integer) |
| `date_to` | Project end date | String (date) |
| `date_to_year` | End year | Number (integer) |
| `date_to_month` | End month | Number (integer) |
| `team_members` | Project team members | Array of objects |
| `full_name` | Member's full name | String (date) |
| `profile_url` | Member's profile URL on professional network | String (date) |
| `order_in_profile` | Section record order | Number (integer) |
| `created_at` | Record creation timestamp in `ISO 8601` format | String (date) |
| `updated_at` | Record update timestamp in `ISO 8601` format | String (date) |
| `deleted` |
Status indicating if the record is publicly available: 1 – The record was publicly unavailable the last time we scraped the profile. 0 – The last time we scraped the profile, the record was available and added to the profile's data
| Number (integer) |
**See snippets of the dataset for reference:**
{% code title="Projects" %}
```json
"projects": [
{
"id": "e1f2a3b4-c5d6-7890-ab12-cd34ef567890",
"name": "AI-Powered Recommendation System",
"project_url": "https://example.com/projects/recommendation-system",
"description": "Developed an AI-powered recommendation engine for e-commerce platforms, increasing conversion rates by 20%.",
"date_from": "2020-01-01",
"date_from_year": 2020,
"date_from_month": 1,
"date_to": "2022-06-01",
"date_to_year": 2022,
"date_to_month": 6,
"team_members": [
{
"full_name": "Jane Johnson",
"profile_url": "www.professional_network.com/janejohnson",
"order_in_profile": 1
},
{
"full_name": "Bob Doe",
"profile_url": "www.professional_network.com/bobdoe",
"order_in_profile": 2
}
],
"order_in_profile": 1,
"deleted": 0,
"created_at": "2020-01-01T10:00:00Z",
"updated_at": "2022-06-01T12:00:00Z"
}
]
```
{% endcode %}
### Publications
| Data field | Description | Data type |
| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------- |
| `publications` | Authored publications | Array of objects |
| `id` | Record UUID identifier | String |
| `title` | Publication title | String |
| `publisher` | Publication publisher | String |
| `date` | Publication release date | String (date) |
| `date_year` | Release year | Number (integer) |
| `date_month` | Release month | Number (integer) |
| `date_day` | Release day | Number (integer) |
| `description` |
Publication description
Note: contains HTML tags
| String |
| `authors` | Publication authors | Array of objects |
| `full_name` | Author's name | String |
| `profile_url` | Author's profile URL | String |
| `order_in_profile` | Section record order | Number (integer) |
| `publication_url` | Publication website URL | String |
| `created_at` | Record creation timestamp in `ISO 8601` format | String (date) |
| `updated_at` | Record update timestamp in `ISO 8601` format | String (date) |
| `deleted` |
Status indicating if the record is publicly available: 1 – The record was publicly unavailable the last time we scraped the profile. 0 – The last time we scraped the profile, the record was available and added to the profile's data
| Number (integer) |
**See a snippet of the dataset for reference:**
{% code title="Publications" %}
```json
"publications": [
{
"id": "f1e2d3c4-b5a6-7890-ab12-cd34ef567890",
"title": "Advances in Machine Learning",
"publisher": "Tech Journal",
"date": "2021-10-15",
"date_year": 2021,
"date_month": 10,
"date_day": 15,
"description": "A comprehensive study on the latest trends and advancements in machine learning techniques.",
"authors": [
{
"full_name": "John Doe",
"profile_url": "https://example.com/johndoe",
"order_in_profile": 1
},
{
"full_name": "Jane Smith",
"profile_url": "https://example.com/janesmith",
"order_in_profile": 2
}
],
"publication_url": "https://example.com/publications/machine-learning",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2021-10-15T10:00:00Z",
"updated_at": "2021-10-15T12:00:00Z"
}
]
```
{% endcode %}
### Recommendations
| Data field | Description | Data type |
| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------- |
| `recommendations` | Recommendations from other previous/current colleagues | Array of objects |
| `id` | Record UUID identifier | String |
| `recommendation` | Recommendation text | String |
| `full_name` | Referee's name | String |
| `referee_url` | Referee's profile URL | String |
| `order_in_profile` | Section record order | Number (integer) |
| `created_at` | Record creation timestamp in `ISO 8601` format | String (date) |
| `updated_at` | Record update timestamp in `ISO 8601` format | String (date) |
| `deleted` |
Status indicating if the record is publicly available: 1 – The record was publicly unavailable the last time we scraped the profile. 0 – The last time we scraped the profile, the record was available and added to the profile's data
| Number (integer) |
**See a snippet of the dataset for reference:**
{% code title="Recommendations" %}
```json
"recommendations": [
{
"id": "a1b2c3d4-e5f6-7890-ab12-cd34ef567890",
"recommendation": "John is a highly skilled and dependable software engineer. His ability to solve complex problems and lead projects to success is remarkable.",
"full_name": "Jane Johnson",
"referee_url": "https:/www.professional_network.com/janejohnson",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2022-05-01T10:00:00Z",
"updated_at": "2022-05-01T12:00:00Z"
}
]
```
{% endcode %}
### Similar profiles
| Data field | Description | Data type |
| ------------------ | ----------------------------------------------------------------------------------------------------------- | ---------------- |
| `similar_profiles` | Similar profiles | Array of objects |
| `id` | Record UUID identifier | String |
| `profile_url` | Profile URL | String |
| `full_name` | Full name | String |
| `headline` |
Profile headline
Note: not directly related to any particular work experience
| String |
| `location` | Displayed location | String |
| `company` | Company name associated with the similar profile | String |
| `followers` | Follower count or description for the similar profile | String |
| `order_in_profile` | Section record order | Number (integer) |
| `created_at` | Record creation timestamp in `ISO 8601` format | String (date) |
| `updated_at` | Record update timestamp in `ISO 8601` format | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Similar profiles" %}
```json
"similar_profiles": [
{
"id": "b1a2c3d4-f5e6-7890-ab12-cd34ef567890",
"profile_url": "www.professional_network.com/janedoe",
"full_name": "Jane Doe",
"headline": "Senior Data Scientist at Tech Corp",
"location": "New York, NY",
"company": "Tech Corp",
"followers": "210",
"order_in_profile": 1,
"created_at": "2023-01-01T10:00:00Z",
"updated_at": "2023-01-01T12:00:00Z"
}
]
```
{% endcode %}
### Others named
| Data field | Description | Data type |
| ------------------ | ----------------------------------------------------------------------------------------------------------- | ---------------- |
| `others_named` | List of employees with similar or exact name | Array of objects |
| `id` | Record UUID identifier | String |
| `profile_url` | Profile URL | String |
| `full_name` | Full name | String |
| `headline` |
Profile headline
Note: not directly related to any particular work experience
| String |
| `location` | Displayed location | String |
| `order_in_profile` | Section record order | Number (integer) |
| `created_at` | Record creation timestamp in `ISO 8601` format | String (date) |
| `updated_at` | Record update timestamp in `ISO 8601` format | String (date) |
{% code title="Others named" %}
```json
"others_named": [
{
"id": "b1a2c3d4-f5e6-7890-ab12-cd34ef567890",
"profile_url": "www.professional_network.com/janedoe",
"full_name": "Jane Doe",
"headline": "Senior Data Scientist at Tech Corp",
"location": "New York, NY",
"order_in_profile": 1,
"created_at": "2023-01-01T10:00:00Z",
"updated_at": "2023-01-01T12:00:00Z"
}
]
```
{% endcode %}
### Test scores
| Data field | Description | Data type |
| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------- |
| `test_scores` | Relevant tests and scores | Array of objects |
| `id` | Record UUID identifier | String |
| `title` | Test title | String |
| `date` | Test completion date | String (date) |
| `date_year` | Test completion year | Number (integer) |
| `date_month` | Test completion month | Number (integer) |
| `description` | Test completion day | String |
| `score` | Test score | String |
| `order_in_profile` | Section record order | String |
| `created_at` |
Record creation timestamp in ISO 8601 format
| String (date) |
| `updated_at` | Record update timestamp in `ISO 8601` format | String (date) |
| `deleted` |
Status indicating if the record is publicly available: 1 – The record was publicly unavailable the last time we scraped the profile. 0 – The last time we scraped the profile, the record was available and added to the profile's data
| Number (integer) |
**See a snippet of the dataset for reference:**
{% code title="Test scores" %}
```json
"test_scores": [
{
"id": "c1d2e3f4-a5b6-7890-ab12-cd34ef567890",
"title": "GRE General Test",
"date": "2021-09-15",
"date_year": 2021,
"date_month": 9,
"date_day": 15,
"description": "Scored in the 95th percentile on the GRE General Test, with a focus on quantitative reasoning and analytical writing.",
"score": "328",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2021-09-15T10:00:00Z",
"updated_at": "2021-09-15T12:00:00Z"
}
]
```
{% endcode %}
### Volunteering positions
| Data field | Description | Data type |
| ----------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------- |
| `volunteering_positions` | Participation in volunteering organizations | Array of objects |
| `id` | Record UUID identifier | String |
| `organization` | Volunteer organization | String |
| `role` | Role in the organization | String |
| `cause` | Volunteering cause | String |
| `date_from` | Volunteering start date | String (date) |
| `date_from_year` | Start year | Number (integer) |
| `date_from_month` | Start month | Number (integer) |
| `date_to` | Volunteering end date | String (date) |
| `date_to_year` | End year | Number (integer) |
| `date_to_month` | End month | Number (integer) |
| `duration` | Volunteering duration | String |
| `created_at` | Record creation timestamp in `ISO 8601` format | String (date) |
| `updated_at` | Record update timestamp in `ISO 8601` format | String (date) |
| `deleted` |
Status indicating if the record is publicly available: 1 – The record was publicly unavailable the last time we scraped the profile. 0 – The last time we scraped the profile, the record was available and added to the profile's data
| Number (integer) |
| `description` |
Experience description
Note: contains HTML tags
| String |
| `organization_url` | Organization's profile URL | String |
| `organization_shorthand_name` | Shorthand name from the profile URL | String |
| `order_in_profile` | Section record order | Number (integer) |
**See a snippet of the dataset for reference:**
{% code title="Volunteering positions" %}
```json
"volunteering_positions": [
{
"id": "d1e2f3a4-b5c6-7890-ab12-cd34ef567890",
"organization": "Community Aid Network",
"role": "Volunteer Coordinator",
"cause": "Poverty Alleviation",
"date_from": "2018-04-01",
"date_from_year": 2018,
"date_from_month": 4,
"date_to": "2021-12-01",
"date_to_year": 2021,
"date_to_month": 12,
"duration": "3 years 8 months",
"description": "Organized and managed volunteer activities to support low-income families through food distribution and educational programs.",
"organization_url": "www.professional_network.com/company/community-aid",
"organization_shorthand_name": "CAN",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2018-04-01T08:00:00Z",
"updated_at": "2021-12-01T17:00:00Z"
}
]
```
{% endcode %}
### Websites
| Data field | Description | Data type |
| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------- |
| `websites` | Personal websites | Array of objects |
| `id` | Record UUID identifier | String |
| `personal_website` | Personal website | String |
| `order_in_profile` | Section record order | Number (integer) |
| `deleted` |
Status indicating if the record is publicly available: 1 – The record was publicly unavailable the last time we scraped the profile. 0 – The last time we scraped the profile, the record was available and added to the profile's data
| Number (integer) |
| `created_at` | Record creation timestamp in `ISO 8601` format | String (date) |
| `updated_at` | Record update timestamp in `ISO 8601` format | String (date) |
{% code title="Websites table" %}
```json
"websites": [
{
"id": "a1b2c3d4-e5f6-7890-ab12-cd34ef567890",
"personal_website": "https://www.johndoeportfolio.com",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2023-01-01T10:00:00Z",
"updated_at": "2023-01-01T12:00:00Z"
}
]
```
{% endcode %}
### Course suggestions
| Data field | Description | Data type |
| -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------- |
| `course_suggestions` | Suggested courses | Array of objects |
| `id` | Record UUID identifier | String |
| `title` | Course title | String |
| `course_url` | Course URL | String |
| `order_in_profile` | Section record order | Number (integer) |
| `deleted` |
Status indicating if the record is publicly available: 1 – The record was publicly unavailable the last time we scraped the profile. 0 – The last time we scraped the profile, the record was available and added to the profile's data
| Number (integer) |
| `created_at` | Record creation timestamp in `ISO 8601` format | String (date) |
| `updated_at` | Record update timestamp in `ISO 8601` format | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Course suggestions" %}
```json
"course_suggestions": [
{
"id": "b1c2d3e4-f5a6-7890-ab12-cd34ef567890",
"title": "Advanced Data Science and Machine Learning",
"course_url": "https://www.example.com/courses/advanced-data-science",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2023-06-01T10:00:00Z",
"updated_at": "2023-06-01T12:00:00Z"
}
]
```
{% endcode %}
### Activity
| Data field | Description | Data type |
| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------- |
| `activity` | Activity on professional network | Array of objects |
| `id` | Record UUID identifier | String |
| `activity_url` | Post URL | String |
| `title` |
Post title
Note: contains control characters
| String |
| `action` | Interaction type | String |
| `order_in_profile` | Section record order | Number (integer) |
| `deleted` |
Status indicating if the record is publicly available: 1 – The record was publicly unavailable the last time we scraped the profile. 0 – The last time we scraped the profile, the record was available and added to the profile's data
| Number (integer) |
| `created_at` | Record creation timestamp in `ISO 8601` format | String (date) |
| `updated_at` | Record update timestamp in `ISO 8601` format | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Activity" %}
```json
"activity": [
{
"id": "d1e2f3a4-b5c6-7890-ab12-cd34ef567890",
"activity_url": "https://www.example.com/activity/12345",
"title": "Shared an article on AI advancements",
"action": "Shared",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2023-07-01T10:00:00Z",
"updated_at": "2023-07-01T12:00:00Z"
}
]
```
{% endcode %}
### Inferred skills
| Data field | Description | Data type |
| ----------------- | ------------------------------------------------------------------ | ---------------- |
| `inferred_skills` | Lists employees' skills based on the descriptions from the profile | Array of strings |
**See a snippet of the dataset for reference:**
{% code title="Inferred skills" %}
```json
"inferred_skills": [
"software development",
"troubleshooting",
"web development"
]
```
{% endcode %}
## Legacy tables
The following legacy categories are excluded from Base Employee schema and data dumps. This data can be obtained only on demand.
### Also viewed
{% hint style="danger" %}
This data is not included in the schema and is available only on demand.
{% endhint %}
| Data field | Description | Data type |
| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------- |
| `also_viewed` | Other employee profiles from the`People also viewed` section | Array of objects |
| `id` | Record UUID identifier | String |
| `full_name` | Employee's name | String |
| `profile_url` | Employee's URL | String |
| `headline` | Employee's headline | String |
| `location` | Associated location | String |
| `order_in_profile` | Section record order | Number (integer) |
| `deleted` |
Status indicating if the record is publicly available: 1 – The record was publicly unavailable the last time we scraped the profile. 0 – The last time we scraped the profile, the record was available and added to the profile's data
| Number (integer) |
| `created_at` | Record creation timestamp in `ISO 8601` format | String (date) |
| `updated_at` | Record update timestamp in `ISO 8601` format | String (date) |
**See a snippet of the dataset for reference:**
{% code title="Also viewed" %}
```json
"also_viewed": [
{
"id": "406e4bfd-4c27-42a0-8f17-1ef70152265a",
"full_name": "John Doe",
"profile_url": "https://www.professional_network.com/john-doe",
"headline": "Executive Director, Capture, ManTech",
"location": "Virginia Beach, VA",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2024-12-14 12:15:41",
"updated_at": "2024-12-14 12:15:41"
}
],
```
{% endcode %}
### Groups
{% hint style="danger" %}
This data is not included in the schema and is available only on demand.
{% endhint %}
| Data field | Description | Data type |
| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------- |
| `groups` | Listed groups | Array of objects |
| `title` | Group title | String |
| `url` | Group page URL | String |
| `order_in_profile` | Section record order | Number (integer) |
| `deleted` |
Status indicating if the record is publicly available: 1 – The record was publicly unavailable the last time we scraped the profile. 0 – The last time we scraped the profile, the record was available and added to the profile's data
| Number (integer) |
| `created_at` | Record creation timestamp in `ISO 8601` format | String (date) |
| `updated_at` | Record update timestamp in `ISO 8601` format | String (date) |
**Refer to the table example from the data:**
{% code title="Groups" %}
```json
"groups": [
{
"id": "e6612926024662a2857650ab6765af8a",
"title": "AI Group",
"url": "https://www.professional_network.com/groups/3205179",
"order_in_profile": 1,
"deleted": 1,
"created_at": "2023-10-07 00:31:30",
"updated_at": "2024-11-28 13:21:21"
}
]
```
{% endcode %}
### Interests
{% hint style="danger" %}
This data is not included in the schema and is available only on demand.
{% endhint %}
| Data field | Description | Data type |
| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------- |
| `interests` | Employee's interests | Array of objects |
| `interest` | Listed interest | String |
| `order_in_profile` | Section record order | Number (integer) |
| `deleted` |
Status indicating if the record is publicly available: 1 – The record was publicly unavailable the last time we scraped the profile. 0 – The last time we scraped the profile, the record was available and added to the profile's data
| Number (integer) |
| `created_at` | Record creation timestamp in `ISO 8601` format | String (date) |
| `updated_at` | Record update timestamp in `ISO 8601` format | String (date) |
**Refer to the table example from the data:**
{% code title="Interests table" %}
```json
"interests": [
{
"id": "d87ee2b130a249a8ab411ed0485abef0",
"interest": "sociology",
"order_in_profile": 1,
"deleted": 1,
"created_at": "2016-10-23 01:59:41",
"updated_at": "2021-11-24 10:15:02"
}
]
```
{% endcode %}
### See more URLs
{% hint style="danger" %}
This data is not included in the schema and is available only on demand.
{% endhint %}
| Data field | Description | Data type |
| --------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------- |
| `posts_see_more_urls` | URLs seen on the profile | Array of objects |
| `url` | Link to the post feed on professional network | String |
| `order_in_profile` | Section record order | Number (integer) |
| `deleted` |
Status indicating if the record is publicly available: 1 – The record was publicly unavailable the last time we scraped the profile. 0 – The last time we scraped the profile, the record was available and added to the profile's data
| Number (integer) |
| `created_at` | Record creation timestamp in `ISO 8601` format | String (date) |
| `updated_at` | Record update timestamp in `ISO 8601` format | String (date) |
**Refer to the table example from the data:**
{% code title="post\_see\_more\_urls" %}
```json
"posts_see_more_urls": [
{
"id": "0c7211374be4061de35455d7f4c29342",
"url": "https://www.professional_network.com/today/author",
"order_in_profile": 1,
"deleted": 1,
"created_at": "2016-10-21 12:27:33",
"updated_at": "2020-04-18 12:42:54"
}
]
```
{% endcode %}
### Skills
{% hint style="danger" %}
This data is not included in the schema and is available only on demand.
{% endhint %}
| Data field | Description | Data type |
| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------- |
| `skills` | Employee's skills | Array of objects |
| `skill` | Listed skill | String |
| `order_in_profile` | Section record order | Number (integer) |
| `deleted` |
Status indicating if the record is publicly available: 1 – The record was publicly unavailable the last time we scraped the profile. 0 – The last time we scraped the profile, the record was available and added to the profile's data
| Number (integer) |
| `created_at` | Record creation timestamp in `ISO 8601` format | String (date) |
| `updated_at` | Record update timestamp in `ISO 8601` format | String (date) |
**Refer to the table example from the data:**
{% code title="Skills table" %}
```json
"skills": [
{
"id": "9050bf5227ebbb956c56ed0aa17acb52",
"skill": "cabinetry",
"order_in_profile": 1,
"deleted": 1,
"created_at": "2018-06-29 18:09:12",
"updated_at": "2018-11-14 07:56:41"
}
]
```
{% endcode %}
### Volunteering cares
{% hint style="danger" %}
This data is not included in the schema and is available only on demand.
{% endhint %}
| Data field | Description | Data type |
| -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------- |
| `volunteering_cares` | Employee's volunteering cares | Array of objects |
| `care` | Listed volunteering care | String |
| `order_in_profile` | Section record order | Number (integer) |
| `deleted` |
Status indicating if the record is publicly available: 1 – The record was publicly unavailable the last time we scraped the profile. 0 – The last time we scraped the profile, the record was available and added to the profile's data
| Number (integer) |
| `created_at` | Record creation timestamp in `ISO 8601` format | String (date) |
| `updated_at` | Record update timestamp in `ISO 8601` format | String (date) |
**Refer to the table example from the data:**
{% code title="Volunteering cares table" %}
```json
"volunteering_cares": [
{
"id": "57a8edada05719738df4323c5afbd22f",
"care": "poverty alleviation",
"order_in_profile": 1,
"deleted": 1,
"created_at": "2018-06-29 18:09:12",
"updated_at": "2018-11-14 07:56:41"
}
]
```
{% endcode %}
### Volunteering opportunities
{% hint style="danger" %}
This data is not included in the schema and is available only on demand.
{% endhint %}
| Data field | Description | Data type |
| ---------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------- |
| `volunteering_opportunities` | Employee's volunteering opportunities | Array of objects |
| `opportunity` | Listed volunteering opportunity | String |
| `order_in_profile` | Section record order | Number (integer) |
| `deleted` |
Status indicating if the record is publicly available: 1 – The record was publicly unavailable the last time we scraped the profile. 0 – The last time we scraped the profile, the record was available and added to the profile's data
| Number (integer) |
| `created_at` | Record creation timestamp in `ISO 8601` format | String (date) |
| `updated_at` | Record update timestamp in `ISO 8601` format | String (date) |
**Refer to the table example from the data:**
{% code title="Volunteering opportunities" %}
```json
"volunteering_opportunities": [
{
"id": "67c30beacec25e39de50659fb8e150bb",
"opportunity": "skills-based volunteering (pro bono consulting)",
"order_in_profile": 1,
"deleted": 1,
"created_at": "2016-11-27 12:25:29",
"updated_at": "2020-04-17 19:07:33"
}
]
```
{% endcode %}
### Volunteering supports
{% hint style="danger" %}
This data is not included in the schema and is available only on demand.
{% endhint %}
| Data field | Description | Data type |
| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------- |
| `volunteering_supports` | Employee's volunteering supports | Array of objects |
| `support` | Listed volunteering support | String |
| `order_in_profile` | Section record order | Number (integer) |
| `deleted` |
Status indicating if the record is publicly available: 1 – The record was publicly unavailable the last time we scraped the profile. 0 – The last time we scraped the profile, the record was available and added to the profile's data
| Number (integer) |
| `created_at` | Record creation timestamp in `ISO 8601` format | String (date) |
| `updated_at` | Record update timestamp in `ISO 8601` format | String (date) |
**Refer to the table example from the data:**
{% code title="Volunteering supports table" %}
```json
"volunteering_supports": [
{
"id": "82131a9a7776d23c8dac09f623e66c61",
"support": "atlanta area council boy scouts of america",
"order_in_profile": 1,
"deleted": 1,
"created_at": "2016-10-19 20:23:42",
"updated_at": "2021-11-25 00:17:50"
}
]
```
{% endcode %}
# Sample: Base Employee Data
Review Coresignal's Base Employee Data sample below, or [contact sales](https://coresignal.com/contact-us/?utm_source=web\&utm_medium=public-docs\&utm_campaign=data-consultation) for more information.
Interested in checking out more data samples? **Visit our self-service platform**:
* Visit Base Employee API playground
* Search, download Employee data
* No credit card required
Start 7-day free trial
{% hint style="info" %}
All personal/company information mentioned within this context is entirely fictional and is solely intended for illustrative purposes.
{% endhint %}
{% code title="Data sample" %}
```json
{
"id": 1234567890,
"parent_id": 987654321,
"is_parent": 1,
"full_name": "John Doe",
"first_name": "John",
"last_name": "Doe",
"headline": "Senior Software Engineer at Example Corp",
"created_at": "2023-01-01T12:00:00Z",
"updated_at": "2026-01-01T12:00:00Z",
"checked_at": "2026-01-01T12:00:00Z",
"processed_at": "2026-05-02T00:00:00Z",
"public_profile_id": "123000123",
"profile_url": "https://www.professional-network.com/john-doe",
"location": "New York, USA",
"city": "New York",
"state": "New York",
"industry": "Computer Software",
"inferred_skills": [
"software development"
],
"summary": "Experienced software engineer with a focus on backend systems and distributed computing.",
"services": "Software Development, Technical Consulting",
"profile_photo_url": "https://www.example.com/photo.jpg",
"deleted": 0,
"country": "United States",
"country_iso_2": "US",
"country_iso_3": "USA",
"regions": [
{
"region": "New York"
}
],
"recommendations_count": 12,
"connections_count": 500,
"follower_count": 600,
"experience_count": 3,
"shorthand_name": "jdoe",
"canonical_shorthand_name": "john-doe",
"shorthand_names": [
{
"shorthand_name": "jdoe"
},
{
"shorthand_name": "john-doe"
}
],
"historical_ids": [
{
"id": 1122334455
},
{
"id": 9988776655
}
],
"awards": [
{
"id": "award-001-award-002abcdef",
"title": "Outstanding Innovation Award",
"issuer": "Tech Innovators Association",
"description": "Recognized for groundbreaking work in AI automation.",
"date": "2021-11-15",
"date_year": 2021,
"date_month": 11,
"date_day": 15,
"order_in_profile": 1,
"deleted": 0,
"created_at": "2021-11-20T10:15:00Z",
"updated_at": "2026-01-20T00:00:00Z"
}
],
"certifications": [
{
"id": "cert-001-abcefg123456a1b2c3",
"title": "Certified Administrator",
"issuer": "Cloud Native Computing Foundation",
"credential_id": "CKA-123456",
"certificate_url": "https://example.com/cert/cka-123456",
"certificate_logo_url": "https://media.licdn.com/dms/image/v2/example-image234",
"date_from": "2022-05-01",
"date_from_year": 2022,
"date_from_month": 5,
"date_to": "2025-05-01",
"date_to_year": 2025,
"date_to_month": 5,
"issuer_url": "https://www.example-institute.net",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2022-05-02T09:00:00Z",
"updated_at": "2026-01-01T00:00:00Z"
}
],
"courses": [
{
"id": "course-001qwerttyuiio123456789",
"organizer": "Examp Online",
"title": "Machine Learning Foundations",
"order_in_profile": 0,
"deleted": 0,
"created_at": "2020-06-15T13:00:00Z",
"updated_at": "2026-01-01T00:00:00Z"
}
],
"education": [
{
"id": "edu-001-plmkn951263abc",
"institution": "Institute of Technology",
"program": "Bachelor of Science in Computer Science",
"institution_id": 101001,
"institution_source_id": 5001,
"date_from": "2010-09-01",
"date_from_year": 2010,
"date_to": "2014-06-01",
"date_to_year": 2014,
"activities_and_societies": "AI Club, Robotics Team",
"description": "Focused on artificial intelligence, software engineering, and systems programming.",
"institution_url": "https://www.tech-inst.edu",
"institution_logo_url": "https://media.licdn.com/dms/image/v2/example-institution-image123",
"institution_shorthand_name": "IT",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2014-06-10T09:00:00Z",
"updated_at": "2026-01-01T00:00:00Z",
"deleted_at": null
}
],
"experience": [
{
"id": "exp-001-test123456a1s2d3f4g5",
"title": "Software Engineer",
"location": "San Francisco, CA",
"company_id": 201001,
"company_source_id": 7001,
"company_name": "Good Company",
"company_url": "https://www.good-company.com",
"company_logo_url": "https://media.licdn.com/dms/image/v2/example/company-logo",
"company_shorthand_name": "good-company",
"date_from": "2017-07-01",
"date_from_year": 2017,
"date_from_month": 7,
"date_to": "2020-12-01",
"date_to_year": 2020,
"date_to_month": 12,
"is_current": 0,
"duration": "3 yrs 5 mos",
"description": "Worked on backend systems, data pipelines, and internal tools for data analytics.",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2017-07-01T09:00:00Z",
"updated_at": "2026-01-01T00:00:00Z",
"deleted_at": null
}
],
"languages": [
{
"id": "lang-00112345asddfg456ghj",
"language": "English",
"proficiency": "Native",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2020-02-20T09:30:00Z",
"updated_at": "2026-01-01T00:00:00Z"
}
],
"organizations": [
{
"id": "org-001a1a2a3a4a5a6a7a8a9",
"organization": "Examp Organization",
"position": "Member",
"description": "Participated in events and contributed to AI working group publications.",
"date_from": "2018-03-01",
"date_from_year": 2018,
"date_from_month": 3,
"date_to": "2019-03-01",
"date_to_year": 2019,
"date_to_month": 3,
"order_in_profile": 1,
"deleted": 0,
"created_at": "2018-03-05T12:00:00Z",
"updated_at": "2026-01-01T00:00:00Z"
}
],
"patents": [
{
"id": "pat-001b1b2b3b4b5b6b7b8b9",
"title": "Adaptive Energy Optimization System",
"status": "Granted",
"inventors": [
{
"full_name": "John Doe",
"profile_url": "www.professional_network.com/john-doe",
"order_in_profile": 1
},
{
"full_name": "Jane Smith",
"profile_url": "www.professional_network.com/jane-smith",
"order_in_profile": 2
}
],
"date": "2021-04-20",
"date_year": 2021,
"date_month": 4,
"date_day": 20,
"patent_url": "https://patents.example.com/pat-001",
"description": "A system that dynamically adjusts energy usage across smart grids using predictive modeling.",
"patent_or_application_number": "US10987654B2",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2021-04-21T08:00:00Z",
"updated_at": "2026-01-01T00:00:00Z"
}
],
"projects": [
{
"id": "proj-001c1c2c3c4c5c6c7c8c9",
"name": "OpenClimate API",
"project_url": "https://www.example.com/johndoe/openclimate",
"description": "An open-source project providing real-time climate data aggregation and analysis.",
"date_from": "2022-01-01",
"date_from_year": 2022,
"date_from_month": 1,
"date_to": "2024-01-01",
"date_to_year": 2024,
"date_to_month": 2024,
"team_members": [
{
"full_name": "John Doe",
"profile_url": "www.professional-network.com/john-doe",
"order_in_profile": 1
},
{
"full_name": "Jane Doe",
"profile_url": "www.professional-network.com/jane-doe",
"order_in_profile": 1
}
],
"order_in_profile": 1,
"deleted": 0,
"created_at": "2022-01-02T13:00:00Z",
"updated_at": "2026-01-01T00:00:00Z"
}
],
"publications": [
{
"id": "pub-001e1e2e3e4e5e6e7e8e9",
"title": "Advances in Machine Learning",
"publisher": "Examp Journal",
"date": "2021-10-15",
"date_year": 2021,
"date_month": 10,
"date_day": 15,
"description": "A comprehensive study on the latest trends and advancements in machine learning techniques.",
"authors": [
{
"full_name": "John Doe",
"profile_url": "www.professional-network.com/john-doe",
"order_in_profile": 1
},
{
"full_name": "Jane Smith",
"profile_url": "www.professional-network.com/jane-smith",
"order_in_profile": 1
}
],
"publication_url": "https://examp-journal.org/document/1234567",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2023-07-16T10:30:00Z",
"updated_at": "2026-01-01T00:00:00Z"
}
],
"recommendations": [
{
"id": "rec-001r1r2r3r4r5r6r7r8r9",
"recommendation": "John consistently delivers thoughtful, high-impact engineering work. He’s a team player and an innovator.",
"full_name": "Jane Smith",
"referee_url": "www.professional-network.com/jane-smith",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2022-09-10T14:00:00Z",
"updated_at": "2026-01-01T00:00:00Z"
}
],
"similar_profiles": [
{
"id": "sim-001s1s2s3s4s56s7s8s9",
"profile_url": "www.professional-network.com/jane-doe",
"full_name": "Jane Doe",
"headline": "Principal ML Engineer at ExampleAI",
"location": "San Francisco, CA",
"company": "ExampleAI",
"followers": "210",
"order_in_profile": 1,
"created_at": "2024-01-20T11:00:00Z",
"updated_at": "2026-01-01T00:00:00Z"
}
],
"others_named": [
{
"id": "other-001o1o2o3o4o5o6o7o8o89",
"profile_url": "https://www.professional-network.com/john-doe-1",
"full_name": "John Doe",
"headline": "Consultant | Startup Advisor",
"location": "Austin, TX",
"order_in_profile": 1,
"created_at": "2024-03-05T09:00:00Z",
"updated_at": "2026-01-01T00:00:00Z"
}
],
"test_scores": [
{
"id": "score-001c1c4c7c2c5c8c3c6c9",
"title": "GRE",
"date": "2019-11-01",
"date_year": 2019,
"date_month": 11,
"date_day": 1,
"description": "Graduate Record Examination, general test",
"score": "330",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2019-12-01T08:00:00Z",
"updated_at": "2026-01-01T00:00:00Z"
}
],
"volunteering_positions": [
{
"id": "vol-001v1v2v3v4v5v6v7v8v9",
"organization": "Code for Good",
"role": "Volunteer Software Engineer",
"cause": "Technology for Social Impact",
"date_from": "2021-04",
"date_from_year": 2021,
"date_from_month": 4,
"date_to": "2023-09",
"date_to_year": 2023,
"date_to_month": 9,
"duration": "2 years 5 months",
"description": "Developed open-source tools to help local nonprofits manage community resources.",
"organization_url": "https://www.professional-network.com/codeforgood",
"organization_shorthand_name": "CFG",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2021-04-15T10:00:00Z",
"updated_at": "2026-01-01T00:00:00Z"
}
],
"websites": [
{
"id": "website-001w1w2w3w4w5w6w7w8w9",
"personal_website": "https://johndoe.dev",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2022-08-15T10:00:00Z",
"updated_at": "2026-01-01T00:00:00Z"
}
],
"course_suggestions": [
{
"id": "course-001c3c6c9c2c5c8c1c4c7c",
"title": "Machine Learning with Python",
"course_url": "https://www.examp-learn.org/learn/machine-learning-python",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2023-03-01T09:00:00Z",
"updated_at": "2026-01-01T00:00:00Z"
}
],
"activity": [
{
"id": "activity-001ac123c12ca123ac13a52",
"activity_url": "https://www.professional-network.com/posts/johndoe_open-source-initiative",
"title": "Open Source Contributor at CivicTech Tools",
"action": "Shared an update",
"order_in_profile": 1,
"deleted": 0,
"created_at": "2024-01-12T11:00:00Z",
"updated_at": "2026-05-01T00:00:00Z"
}
]
}
```
{% endcode %}
# Employee Posts Data
Employee Posts Data provides freshly collected professional network posts with engagement metrics, authorship details, and publication timestamps.
Employee Posts Data is designed to be used in **Sales Tech, Investment, Market Research, and AI/ML applications.**
| **Enhanced sales personalization** | Access to full post content enables highly personalized outreach based on prospects' actual interests, achievements, and current priorities. |
| ---------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Company intelligence** | Capture unique business insights and news directly from employee posts that may not appear in traditional news sources. |
| **Intent signal detection** | Identify optimal timing for outreach based on company funding announcements, executive changes, product launches, and other business developments shared through posts. |
***
## Summary
| Feature | Details |
| ------------------ | -------------- |
| Available via | Flat files/API |
| Delivery frequency | Monthly |
| Available formats | JSONL, Parquet |
| Scraping since | 2025-03 |
## Related links
***
# Dictionary: Employee Posts Data
Data dictionary for Employee Posts Data, with all fields explained across author details, recent post content, engagement metrics, and reshared post data.
## Overview
This data dictionary shows all available data fields, explains their values, and provides data samples from the Employee Posts dataset.
{% tabs %}
{% tab title="Data fields per category" %}
1. [Author](#author)
2. [Post information](#post-information)
3. [Comments](#comments)
4. [Reshared post](#reshared-post)
{% endtab %}
{% endtabs %}
{% hint style="info" %}
All personal/company information mentioned within this context is entirely fictional and is solely intended for illustrative purposes.
{% endhint %}
## Author
| Data field | Description | Data type |
| -------------------- | ---------------------------------------------- | --------- |
| `author_name` | Employee's full name | String |
| `author_profile_url` | Employee's profile URL | String |
| `author_headline` | Headline or title of the author (if available) | String |
| `author_posts_count` | Number of all author's posts | Integer |
**Refer to the table example from the data:**
{% code title="Author" %}
```json
"author_name": "John Doe",
"author_profile_url": "https://professional-network.com/john-doe",
"author_headline": "Data Analyst @Company Example | Data is my passion",
"author_posts_count": 100,
```
{% endcode %}
## Post information
| Data field | Description | Data type |
| -------------------- | --------------------------------------------------------------------------------------------- | ---------------- |
| `id` | Post's ID | String |
| `employee_parent_id` | Employee profile identification key | String |
| `url` | Post's URL | String |
| `date_published` | Post publication date | String |
| `article_body` | Content of the post | String |
| `image_url` | URL of an image attached to the post (if available) | String |
| `hashtags` | List of hashtags used in the post | Array |
| `mentions` | Mentions of other profiles | Array of strings |
| `full_name` | Mention within the post | String |
| `url` | Profile URL of the mentioned entity | String |
| `reaction_count` | Number of reactions (likes, claps, etc.) on the post | Integer |
| `created_at` | The date and time when the employee post record was scraped and recorded in `ISO 8601` format | String (date) |
**Refer to the table example from the data:**
{% code title="Post information" %}
```json
"id": "1234567890123456",
"employee_parent_id": "111222333444555",
"url": "https://www.professional-network.com/posts/johndoe_example-post-123456",
"date_published": "2025-07-01",
"article_body": "Data is only as valuable as the insights you can draw from it, and the right structure makes all the difference. With Jane Doe, we're building tools that turn raw data into clear, actionable stories.",
"image_url": "https://example.com/image/link123456789",
"hashtags": [
"#Data",
"#Inspiring"
],
"mentions": [
{
"full_name": "Jane Doe",
"url": "https://professional-network.com/jane-doe"
}
],
"reaction_count": 10,
"created_at": "2026-04-16 18:55:11",
```
{% endcode %}
## Comments
| Data field | Description | Data type |
| ---------------- | ------------------------------------------------- | ---------------- |
| `comment_count` | Number of comments on the post | Integer |
| `comments` | List of comments on the post | Array of objects |
| `full_name` | Name of the person who made the comment | String |
| `headline` | Headline or title of the commenter (if available) | String |
| `profile_url` | URL of the commenter’s profile | String |
| `body` | Content of the comment | String |
| `reaction_count` | Number of reactions on the comment | Integer |
| `date_published` | Time when comment was published | String |
**Refer to the table example from the data:**
{% code title="Comments" %}
```json
"comment_count": 2,
"comments": [
{
"full_name": "John Smith",
"headline": "Talent Intelligence@ Example corp | Meta Alumni",
"profile_url": "https://www.professional-network.com/john-smith",
"body": "Great opportunity",
"reaction_count": 1,
"date_published": "2025-07-01"
},
{
"full_name": "Jane Smith",
"headline": "Looking for a job in QA | Functional Testing ",
"profile_url": "https://professional-network.com/jane-smith",
"body": "I'd be interested to join your team",
"reaction_count": 0,
"date_published": "2025-07-02"
}
]
```
{% endcode %}
## Reshared post
| Data field | Description | Data type |
| -------------------- | ------------------------------------------------------------------------------------ | --------- |
| `reshared_post` | Reshared posts information | Object |
| `id` | Unique identifier of the reshared post. Used to distinguish it from other posts | String |
| `company_id` | The `company_id` of the reshared post in case the post belongs to a company | Integer |
| `employee_parent_id` | Author profile identification key | String |
| `url` | Direct URL to the reshared post on Professional network | String |
| `author_name` | Name of the original author of the reshared post | String |
| `author_profile_url` | Professional network profile URL of the original post’s author | String |
| `author_headline` | Headline or summary text shown under the author’s name, often follower count or role | String |
| `article_body` | Main text content of the reshared post | String |
| `image_url` | List of image URLs attached to the reshared post | String |
| `hashtags` | List of hashtags included in the reshared post text | String |
| `date_published` | Time since the post was published | String |
**Refer to the table example from the data:**
{% code title="Reshared post" %}
```json
"reshared_post": {
"id": "12345677899",
"company_id": 12340000,
"employee_parent_id": "111222333444555",
"url": "https://www.professional-network.com/link/12345677899",
"author_name": "Post author",
"author_profile_url": "https://professional-network.com/post-author",
"author_headline": "1,000 followers",
"article_body": "Interesting post about AI usage in your workplace",
"image_url": "https://example.com/image/link12345677899",
"hashtags": [
"#AI"
],
"date_published": "2w"
}
```
{% endcode %}
# Sample: Employee Posts Data
Review an Employee Posts Data sample with fresh post content, author profiles, engagement counts, and reshared post details in JSONL format.
Review Coresignal's Employee Posts Data sample below, or [contact sales](https://coresignal.com/contact-us/?utm_source=web\&utm_medium=public-docs\&utm_campaign=data-consultation) for more information.
{% hint style="info" %}
All personal/company information mentioned within this context is entirely fictional and is solely intended for illustrative purposes.
{% endhint %}
{% code title="Data sample" %}
```json
{
"author_name": "John Doe",
"author_profile_url": "https://professional-network.com/john-doe",
"author_headline": "Data Analyst @Company Example | Data is my passion",
"author_posts_count": 100,
"id": "1234567890123456",
"employee_parent_id": "111222333444555",
"url": "https://www.professional-network.com/posts/johndoe_example-post-123456",
"date_published": "2025-07-01",
"created_at": "2026-02-16 18:55:11",
"article_body": "Data is only as valuable as the insights you can draw from it, and the right structure makes all the difference. With Jane Doe, we're building tools that turn raw data into clear, actionable stories.",
"image_url": "https://example.com/image/link123456789",
"hashtags": [
"#Data",
"#Inspiring"
],
"mentions": [
{
"full_name": "Jane Doe",
"url": "https://professional-network.com/jane-doe"
}
],
"reaction_count": 10,
"comment_count": 2,
"comments": [
{
"full_name": "John Smith",
"headline": "Talent Intelligence@ Example corp | Meta Alumni",
"profile_url": "https://www.professional-network.com/john-smith",
"body": "Great opportunity",
"reaction_count": 1,
"date_published": "2025-07-01"
},
{
"full_name": "Jane Smith",
"headline": "Looking for a job in QA | Functional Testing ",
"profile_url": "https://professional-network.com/jane-smith",
"body": "I'd be interested to join your team",
"reaction_count": 0,
"date_published": "2025-07-02"
}
],
"reshared_post":
{
"id": "12345677899",
"company_id": 1234000,
"employee_parent_id": "111222333444555",
"url": "https://www.professional-network.com/feed/urn:12345677899",
"author_name": "Post author",
"author_profile_url": "https://professional-network.com/post-author",
"author_headline": "1,000 followers",
"article_body": "Interesting post about AI usage in your workplace",
"image_url": "https://example.com/image/link12345677899",
"hashtags": [
"#AI"
],
"date_published": "2w"
}
}
```
{% endcode %}
# Multi-source Employee API
Multi-source Employee API documentation covering search and collect endpoints, request types, bulk data retrieval, webhooks, rate limits, and credit usage.
## Overview
This section covers basic information about Multi-source Employee API.\
To learn more about the API and its endpoints, follow the links below:
## Multi-source Employee endpoints
{% hint style="info" %}
Our API is a data retrieval tool. The endpoints do not support analytic features.
{% endhint %}
Multi-source Employee API features **three search** and **two collect** endpoints for searching and collecting relevant Multi-source Employee data.
{% hint style="warning" %}
All Multi-source Employee API requests must be made over HTTPS. Requests made over HTTP will fail or be redirected to HTTPS.
{% endhint %}
Multi-source Employee API supports two types of requests:
* **Search** endpoints support POST requests only.
* **Collect** endpoints support the GET requests only.
Collect Multi-source Employee data using profile URLs or shorthand names*
20
\*📌 Full profile URL example: [www.professional-network.com/john-doe.\\](http://www.professional-network.com/john-doe.\\)
Shorthand name example: john-doe.
### Bulk Collect
Use the following endpoints to retrieve Multi-source Employee API data in bulk:
| Request type | Endpoint |
| ------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
| POST | [*/v2/data\_requests/employee\_multi\_source/ids*](/employee-api/multi-source-employee-api/bulk-collect/post-requests#ids-requests) |
| POST | [*/v2/data\_requests/employee\_multi\_source/es\_dsl*](/employee-api/multi-source-employee-api/bulk-collect/post-requests#elasticsearch-dsl-requests) |
| GET | */v2/data\_requests/{data\_request\_id}/files* |
| GET | */v2/data\_requests/{data\_request\_id}/files/{file\_name}* |
Read more about Bulk Collect in the following article:
### Webhook subscriptions
Webhooks enable you to receive automatic, event-driven notifications when changes are detected in your subscribed employee profiles.
Use the following endpoints to subscribe to profile changes in employee data:
| Request type | Endpoint |
| ------------ | --------------------------------------------------- |
| POST | */v2/subscriptions/employee\_multi\_source/es\_dsl* |
| POST | */v2/subscriptions/employee\_multi\_source/ids* |
And use the following endpoints to subscribe to employee experience updates:
| Request type | Endpoint |
| ------------ | ----------------------------------------------------------------------- |
| POST | */v2/subscriptions/experience\_changes/employee\_multi\_source/ids* |
| POST | */v2/subscriptions/experience\_changes/employee\_multi\_source/es\_dsl* |
Learn more about Webhook subscriptions
# Data Dictionary: Multi-source Employee API
Full data dictionary for Multi-source Employee API: field definitions and examples covering fresh profile data, salaries, experience changes, and more.
On this page, you'll find detailed information about Coresignal's **Multi-source Employee** data.\
Each category includes a table listing the available data fields, their explanations, data types, and sample code snippets.
{% hint style="info" %}
All personal/company information mentioned within this context is entirely fictional and is solely intended for illustrative purposes.
Data fields in the example snippets are rearranged for better grouping.
{% endhint %}
{% tabs %}
{% tab title="Data fields per category" %}
1. [Metadata](#metadata)
2. [Identifiers and URLs](#identifiers-and-urls)
3. [Employee information](#employee-information)
4. [Professional contact information](#professional-contact-information)
5. [Locations](#location)
6. [Experience and workplace](#experience-and-workplace)
7. [Full experience information](#full-experience-information)
8. [Workplace details](#workplace-details)
9. [Education](#education)
10. [Salary](#salary)
11. [Profile field changes](#profile-field-changes)
12. [Recent experience changes](#recent-experience-changes)
13. [Recommendations](#recommendations)
14. [Activity](#activity)
15. [Awards](#awards)
16. [Courses](#courses)
17. [Certifications](#certifications)
18. [Languages](#languages)
19. [Patents](#patents)
20. [Publications](#publications)
21. [Projects](#projects)
22. [Organizations](#organizations)
23. [Investments](#investments)
24. [Events and exits](#events-and-exits)
{% endtab %}
{% endtabs %}
## Metadata
| Data field | Description | Data type |
| -------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `created_at` | The date and time when the employee record was created | String (date) |
| `updated_at` | The date and time when the employee record was last fully updated | String (date) |
| `checked_at` | The date and time when the employee record was last checked partially | String (date) |
| `changed_at` | The date and time when the employee record was last changed | String (date) |
| `experience_change_last_identified_at` | The date and time when the employee record change was last identified | String (date) |
| `is_deleted` |
Marks if the employee record is deleted or private 1 – the record is deleted 0 – the record is not deleted
| Number (integer) |
| `is_parent` |
Notes if the employee record is the main employee profile: 1 – the record is parent (main) 0 – the record is not parent
| Number (integer) |
**See a snippet of the dataset for reference:**
{% code title="Metadata" %}
```json
"created_at": "2024-07-27T01:56:27.000",
"updated_at": "2024-10-23T05:50:09.000",
"checked_at": "2025-09-23T05:50:09.000",
"changed_at": "2025-10-01T06:30:1.000",
"experience_change_last_identified_at": "2024-10-25T06:30:10.000",
"is_deleted": 0,
"is_parent": 1
```
{% endcode %}
## Identifiers and URLs
| Data field | Description | Data type |
| ----------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `id` |
Coresignal's identification key for an employee profile record. Taken from the professional network dataset.
| Number (integer) |
| `parent_id` | Parent category identification key | Number (integer) |
| `historical_ids` | Historical identification keys that are related to the same profile after URL change | Array of longs |
| `professional_network_url` | Most recent profile URL on professional network | String |
| `professional_network_shorthand_names` | Historical variations of shorthand names for the employee | Array of strings |
| `professional_network_canonical_shorthand_name` | The most recent version of employee’s shorthand name | String |
| `public_profile_id` | Public profile ID | Number (long) |
| `facebook_url` | Facebook URL | String |
| `twitter_url` | Twitter URL | String |
| `financial_website_url` | Financial website URL | String |
| `website` | Employee's website | String |
**See a snippet of the dataset for reference:**
{% code title="Identifiers & URLs" %}
```json
"id": 12389891,
"parent_id": 12389891,
"public_profile_id": 123456789,
"historical_ids": [
12389891,
11589843
],
"professional_network_url": "https://www.professional_network.com/john-doe-18729383",
"professional_network_shorthand_names": [
"real-john-doe"
"john-doe-1992"
],
"professional_network_canonical_shorthand_name": "john-doe-18729383",
"facebook_url": "https://www.facebook.com/john-doe",
"twitter_url": "https://www.x.com/john-doe",
"financial_website_url": "https://www.financial-website.com/person/john-doe",
"website": "https://www.john-doe-website.com"
```
{% endcode %}
### Profile score
| Data field | Description | Data type |
| --------------- | ----------------------------------------------------------------------------------------------------------------------- | --------- |
| `profile_score` | Model-derived employee profile quality score based on profile completeness and activity signals. Score range: `0` – `1` | Double |
**See a snippet of the dataset for reference:**
{% code title="Profile score" %}
```json
"profile_score": 0.5
```
{% endcode %}
## Employee information
| Data field | Description | Data type |
| --------------------- | ------------------------------------------------------------------------ | ---------------- |
| `full_name` | Employee's full name | String |
| `first_name` |
Employee's first name Parsed from the full\_name
| String |
| `first_name_initial` |
First name initial Parsed from first\_name
| String |
| `middle_name` |
Employee's middle name Parsed from the full\_name
| String |
| `middle_name_initial` |
Middle name initial Parsed from middle\_name
| String |
| `last_name` |
Employee's last name Parsed from the full\_name
| String |
| `last_name_initial` |
Last name initial Parsed from last\_name
| String |
| `picture_url` | Picture URL | String |
| `connections_count` | Count of profile connections | Number (integer) |
| `followers_count` | Count of profile followers | Number (integer) |
| `interests` | Employee's interests | Array of strings |
**See a snippet of the dataset for reference:**
{% code title="Employee information" %}
```json
"full_name": "John Doe",
"first_name": "John",
"first_name_initial": "J",
"middle_name": "Michael",
"middle_name_initial": "M",
"last_name": "Doe",
"last_name_initial": "D",
"picture_url": "https://static.lnk.com/aero-v1/sc/h/9c8pery4andzj6ohjkjp54ma2"
"connections_count": 472,
"followers_count": 3190,
"interests":
[
"hiking",
"snowboarding",
"cycling",
]
```
{% endcode %}
## Professional contact information
{% hint style="info" %}
Coresignal **collects only publicly available, strictly business-related data** published or released by companies or individuals at their discretion online. The contact information includes **only professional emails**.\
No sensitive or private/ located within the login secured areas information is collected or transmitted.
{% endhint %}
| Data field | Description | Data type |
| ----------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `primary_professional_email` | Employee's business email address tied to their workplace | String |
| `primary_professional_email_status` | The confidence level in the accuracy of the employee's business email address. The field will return four options: `verified`, `matched_email, ``matched_pattern,` or`guessed_common_pattern`. | String |
| `professional_emails_collection` | Collection of employee's business email addresses | Array of structs |
| `professional_email` | Employee's business email address | String |
| `professional_email_status` |
The confidence level in the accuracy of the employee's professional email address. The field will return four options:
verified – the email was matched and verified
matched\_email – the email was matched but could not retrieve "verified" status
matched\_pattern – the exact email was not matched, but it was guessed based on the matched email pattern for the company
guessed\_common\_pattern – neither the exact email nor the email pattern for that company was matched, but the most common global pattern was guessed
| String |
| `order_of_priority` | Order of priority based on confidence in email validity | Number (integer) |
**See a snippet of the dataset for reference:**
{% code title="Professional contact information" %}
```json
"primary_professional_email": "johndoe@company1.com",
"primary_professional_email_status": "matched_pattern",
"professional_emails_collection": [
{
"professional_email": "johndoe@company1.com",
"professional_email_status": "matched_pattern",
"order_of_priority": 1
},
{
"professional_email": "john.doe1@company1.com",
"professional_email_status": "matched_pattern",
"order_of_priority": 2
},
{
"professional_email": "johndoe123@company1.com",
"professional_email_status": "matched_pattern",
"order_of_priority": 3
}
]
```
{% endcode %}
## Location
| Data field | Description | Data type |
| ----------------------- | ----------------------------------------------------------------------------------- | ---------------- |
| `location_country` | Associated country | String |
| `location_city` | Employee location city | String |
| `location_state` | Employee location state | String |
| `location_country_iso2` | ISO 2-letter code of the location country, based on their `location_country` value. | String |
| `location_country_iso3` | ISO 3-letter code of the location country, based on their `location_country`value. | String |
| `location_full` | Full location | String |
| `location_regions` | Associated geographical regions based on their `location_country` value | Array of strings |
**See a snippet of the dataset for reference:**
{% code title="Location" %}
```json
"location_country": "United States",
"location_city": "San Diego",
"location_state": "California",
"location_country_iso2": "US",
"location_country_iso3": "USA",
"location_full": "San Diego, California, United States",
"location_regions": [
{
"region": "Americas"
},
{
"region": "Northern America"
},
{
"region": "AMER"
}
]
```
{% endcode %}
## Experience and workplace
### Active experience overview
| Data field | Description | Data type |
| ------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `headline` | Profile headline. Semantic search is applied with `/synonym_search/es_dsl` endpoint | String |
| `summary` | Main description (employee summary) | String |
| `services` | Offered services | String |
| `is_working` |
Marks if the employee is currently employed 1 – the employee is currently working 0 – the employee is currently not working
| Number (integer) |
| `active_experience_company_id` | Coresignal's identification key for a company to identify where the employee is currently working | Number (integer) |
| `active_experience_title` | Title of employee's current position. Semantic search is applied with `/synonym_search/es_dsl` endpoint | String |
| `active_experience_description` | Description of the current position | String |
| `active_experience_department` | A list of employee's departments, based on `actve_position_title` | String |
| `active_experience_management_level` | A list of employee's management levels, based on `active_experience_title` | String |
| `active_experience_company_website` | Website of the company in the employee's active experience | String |
| `active_experience_company_shorthand_name` | Shorthand name of the company in the active experience | String |
| `active_experience_company_logo_url` | Logo URL of the company in the active experience | String |
| `is_decision_maker` |
Marks if the employee is a decision maker, based on active\_experience\_title 1 – the employee is a decision maker 0 – the employee is not a decision maker
| Number (integer) |
**See a snippet of the dataset for reference:**
{% code title="Active experience overview" %}
```json
"headline": "Data Analyst | Machine Learning Enthusiast",
"summary": "
Passionate about uncovering insights from data and applying machine learning techniques to solve global problems.
",
"services": "Data Analysis, Machine Learning Consulting, Business Intelligence",
"is_working": 1,
"active_experience_company_id": 4127532,
"active_experience_title": "Senior Data Analyst",
"active_experience_description": "Leading data-driven projects, building predictive models, and optimizing business intelligence strategies.",
"active_experience_department": "Engineering and Technical",
"active_experience_management_level": "Senior",
"active_experience_company_website": "www.fake-tech-company.com",
"active_experience_company_shorthand_name": "fake-tech-company",
"active_experience_company_logo_url": "https://media.licdn.com/dms/image/v2/example/company-logo",
"is_decision_maker": 1
```
{% endcode %}
### Skills
| Data field | Description | Data type |
| ------------------- | ------------------------------------------------------------------ | ---------------- |
| `inferred_skills` | Lists employees' skills based on the descriptions from the profile | Array of strings |
| `historical_skills` | Historical skills | Array of strings |
**See a snippet of the dataset for reference:**
{% code title="Skills" %}
```json
"inferred_skills": [
"cloud computing",
"data analysis",
"deep learning",
"software development",
"system architecture",
"troubleshooting",
"web development"
]
```
{% endcode %}
### Experience duration
| Data field | Description | Data type |
| ------------------------------------------------------------- | ------------------------------------------------------------------- | ---------------- |
| `total_experience_duration_months` | Total normalized experience duration of all Employee's experiences | Number (integer) |
| `total_experience_duration_months_breakdown_department` | Total normalized experience duration by employee's department | Array of structs |
| `department` | Department | String |
| `total_experience_duration_months` | Experience duration in months | String |
| `total_experience_duration_months_breakdown_management_level` | Total normalized experience duration by Employee's management level | Array of structs |
| `management_level` | Employee's management level | String |
| `total_experience_duration_months` | Experience duration in months | String |
**See a snippet of the dataset for reference:**
{% code title="Experience duration" %}
```json
"total_experience_duration_months": 85,
"total_experience_duration_months_breakdown_department": [
{
"department": "C-Suite",
"total_experience_duration_months": 13
},
{
"department": "Marketing",
"total_experience_duration_months": 30
},
{
"department": "Finance & Accounting",
"total_experience_duration_months": 14
},
{
"department": "Engineering and Technical",
"total_experience_duration_months": 28
}
],
"total_experience_duration_months_breakdown_management_level": [
{
"management_level": "C-Level",
"total_experience_duration_months": 10
},
{
"management_level": "Intern",
"total_experience_duration_months": 3
},
{
"management_level": "Senior",
"total_experience_duration_months": 4
},
{
"management_level": "Specialist",
"total_experience_duration_months": 40
},
{
"management_level": "Manager",
"total_experience_duration_months": 18
}
]
```
{% endcode %}
## Full experience information
| Data field | Description | Data type |
| ------------------- | -------------------------------------------------------------------------------------------- | ---------------- |
| `experience` | Work experience the Employee has | Array of structs |
| `active_experience` | Identifies if this is a current (active) position | Integer |
| `position_title` | Employee's position title. Semantic search is applied with `/synonym_search/es_dsl` endpoint | String |
| `department` | Employees department | String |
| `management_level` | Employee's management level | String |
| `location` | Job/workplace location | String |
| `date_from` | Employment start date | String (date) |
| `date_from_year` | Employment start year | Integer |
| `date_from_month` | Employment start month | Integer |
| `date_to` | Employment end date | String (date) |
| `date_to_year` | Employment end year | Integer |
| `date_to_month` | Employment end month | Integer |
| `duration_months` | Employment duration in months | Integer |
| `description` | Employment description | String |
| `company_logo_url` | URL pointing to the logo of the company/employer | String |
**See a snippet of the dataset for reference:**
{% code title="Full experience information" %}
```json
"experience": [
{
"active_experience": 0,
"position_title": "Senior Data Analyst",
"department": "Data Science",
"management_level": "Mid-Level",
"location": "San Francisco, California, United States",
"date_from": "March 2020",
"date_from_year": 2020,
"date_from_month": 3,
"date_to": "July 2022",
"date_to_year": 2022,
"date_to_month": 7,
"duration_months": 126,
"company_logo_url": "https://example.com/image/company-logo"
}
]
```
{% endcode %}
## Workplace details
### Metadata and firmographics
| Data field | Description | Data type |
| -------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| `company_id` | Company record identification key in Coresignal's database | Integer |
| `company_name` | Company name | String |
| `company_type` | Company type | String |
| `company_founded_year` | Founding year | String (date) |
| `company_size_range` | Company size based on employee count range (as selected by the company profile administrator) | String |
| `company_employees_count` | Number of employees that associated their experience with the company | Integer |
| `company_categories_and_keywords` | Categories and keywords are assigned to the company profile and products across various platforms | Array of strings |
| `company_employees_count_change_yearly_percentage` | Company employee count change (percentage) | Number (double) |
| `company_industry` | Company's industry | String |
| `company_last_updated_at` | The last update date of the record in the `YYYY-MM-DD` format | String (date) |
| `company_is_b2b` |
Indicates if the company operates in a business-to-business model: 1 – b2b company 0 – b2c company
| Integer |
| `order_in_profile` | The order number of the workplace in the profile | Integer |
**See a snippet of the dataset for reference:**
{% code title="Metadata & firmographics" %}
```json
"company_id": 87272276,
"company_name": "Company123, LLC",
"company_type": "Privately Held",
"company_founded_year": "2023",
"company_size_range": "51-200 employees",
"company_employees_count": 157,
"company_categories_and_keywords": [
"Data Analysis",
"AI",
"Management",
"Consulting",
],
"company_employees_count_change_yearly_percentage": 17.43222222222222
"company_industry": "Manufacturing",
"company_last_updated_at": "2025-02-03",
"company_is_b2b": 1,
"order_in_profile": 1
```
{% endcode %}
### Social media
| Data field | Description | Data type |
| ------------------------- | -------------------------------------------------------- | ---------------- |
| `company_followers_count` | Company's profile follower count on professional network | Integer |
| `company_website` | Company's website | String |
| `company_facebook_url` | Company's Facebook URL | Array of strings |
| `company_twitter_url` | Company's X (Twitter) URL | Array of strings |
| `company_linkedin_url` | Company's LinkedIn URL | String |
**See a snippet of the dataset for reference:**
{% code title="Social media" %}
```json
"company_followers_count": 527712,
"company_website": "https://www.company1.com",
"company_facebook_url": [
"https://www.facebook.com/company1global",
"https://www.facebook.com/company1"
],
"company_twitter_url": [
"https://www.x.com/company1"
],
"company_linkedin_url": "https://www.linkedin.com/company/company1"
```
{% endcode %}
### Financials
| Data field | Description | Data type |
| ---------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------- | ---------------- |
|
## Using pagination in cURL requests
{% hint style="info" %}
This tutorial requires prior knowledge of how to compile and execute POST requests in Multi-source Employee API.
{% endhint %}
Use parameter `x-next-page-after` to retrieve a second page of IDs.
1. Navigate to the **Headers** section and click it:
2. Find the following information:\
– `x-next-page-after`\
– `x-total-pages`\
– `x-total-results`
3. Add parameter `?after={x-next-page-after}` to the POST request to see the next results page:
4. Execute the request, and you will see the next page in the **Body** section:
```json
[
1000,
1001,
3000,
4004
]
```
### Pagination using sorting
Pagination using **ID** sorting has similar `x-next-page-after` format, but the **last updated** date is excluded.
#### **Pagination usage example (cURL request in Postman)**
1. Add parameter `?after={x-next-page-after}` to the POST request:\
Refer to the example below for the exact parameter placement:
{% code title="Pagination (sorted by id)" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/employee_multi_source/search/es_dsl?after=541168785' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"query":{
"match":{
"full_name":{
"query":"John Smith",
"operator":"and"
}
}
},
"sort": [
"id"
]
}'
```
{% endcode %}
**Send** the request, and you will see the next page in the **(Response) Body**.
***
Pagination using **score** sorting has a different ID format. The format difference is seen by the `x-next-page-after` parameter, showing the **score**, the **last updated** date, and the **last ID** on the page.
#### **Pagination usage example (cURL request in Postman)**
Add parameter `?after={x-next-page-after}` to the POST request to see the next results page. Refer to the example below for the exact parameter placement:
{% code title="Pagination (sorted by score)" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/employee_multi_source/search/es_dsl?after=11.812105,"2024-06-02T08:27:03.317837Z",572523155' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"query":{
"match":{
"full_name":{
"query":"John Smith",
"operator":"and"
}
}
},
"sort": [
"_score"
]
}'
```
{% endcode %}
**Send** the request, and you will see the next page in the **(Response) Body**.
## Limiting search results per page
Query parameter `?items_per_page={int}` allows you to specify the number of results retrieved per Search results page. The current limit is 1,000. Thus, this parameter lets you set a smaller limit value for the results page.
{% code title="Items per page" %}
```json
curl -X 'POST' \
'https://api.coresignal.com/cdapi/v2/employee_multi_source/search/es_dsl?items_per_page=200' \
-H 'accept: application/json' \
-H 'apikey: {API Key}' \
-H 'Content-Type: application/json' \
-d '{
"query":{
"match":{
"full_name":{
"query":"John Smith",
"operator":"and"
}
}
}
}'
```
{% endcode %}
# Search Preview: Multi-source Employee API
{% columns %}
{% column width="16.666666666666664%" %}
Data type:
Query type:
URL:
{% endcolumn %}
{% column %}
Multi-source Employee
Elasticsearch DSL
{% endcolumn %}
{% endcolumns %}
***
## Overview
Retrieve a limited set of fields from top-matching records in real time, and search suggestion features. Here, Multi-source Employee API search `/v2/employee_multi_source/search/es_dsl/preview` endpoint's usage is reviewed.
Learn how to obtain Multi-source Employee data using profile URLs or shorthand names
| Used key | Collect endpoints | Function |
| -------------- | -------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Employee ID | */v2/employee\_multi\_source/collect/{employee\_id}* | Retrieved using Employee API search endpoints. |
| Shorthand name | */v2/employee\_multi\_source/collect/{profile\_url/shorthand\_name}* | Use profile URL or shorthand name, taken directly from the URL (e.g., john-doe from *[www.professiona\\\_network.com/john-doe](http://www.professiona\\_network.com/john-doe)*). |
***
## Collection using IDs
Examples in this article are prepared using Postman.
However, you can use the most convenient tool for you: terminal, Postman, or any API-compatible application.
### cURL (Postman)
Use the provided request template below. Enter a valid `employee_id` value and your `API Key`.
{% tabs %}
{% tab title="Full collect" %}
{% code title="cURL request template" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/employee_multi_source/collect/{employee_id}' \
-H 'accept: application/json' \
-H 'apikey: {API Key}'
```
{% endcode %}
{% endtab %}
{% tab title="Field selection" %}
{% code title="cURL request template with several fields" %}
```json
curl -X 'GET' \
'https://api.coresignal.com/cdapi/v2/employee_multi_source/collect/{employee_id}?fields=full_name&fields=follower_count' \
-H 'accept: application/json' \
-H 'apikey: {API Key}'
```
{% endcode %}
{% endtab %}
{% endtabs %}
## Collection using profile URLs or shorthand names
Examples in this article are prepared using Postman.
However, you can use the most convenient tool for you: terminal, Postman, or any API-compatible application.
### cURL (Postman)
Use the provided request template below. Enter a valid `profile_url` or `shorthand_name` value and your `API Key`.
{% tabs %}
{% tab title="Full collect" %}
{% code title="cURL request template" %}
```json
curl -X GET "https://api.coresignal.com/cdapi/v2/employee_multi_source/collect/{profile_url/shorthand_name}"
-H "accept: application/json"
-H "apikey: {API Key}"
```
{% endcode %}
{% endtab %}
{% tab title="Field selection" %}
{% code title="cURL request example with several fields" %}
```json
curl -X GET "https://api.coresignal.com/cdapi/v2/employee_multi_source/collect/{profile_url/shorthand_name}?fields=full_name&fields=follower_count"
-H "accept: application/json"
-H "apikey: {API Key}"
```
{% endcode %}
{% endtab %}
{% endtabs %}
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