Data for AI

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.

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.

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 in requests’ responses to prevent your data from being cluttered with unnecessary information.

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.

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 3 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.