The data layer your AI agents need
Connect agentic workflows, LLMs, and AI pipelines to 4.5B+ company, employee an jobs data records — in natural language, no query language required.
1// Natural language query — no schema required
2
3query: "Find VPs of Sales or Chief Revenue Officers who previously worked at Salesforce or HubSpot"
4
5// Response
6{
7 "records found": 531,
8 "id": 21465654,
9 "full_name": "John Doe",
10 "location_country": "United States",
11 "connections_count": 500,
12 "followers_count": 28367,
13 "company_name": "Medidata Solutions",
14 "company_industry": "Software Development",
15 "active_experience_title": "Senior Vice President Of Sales",
16 "active_experience_department": "Sales",
17 "active_experience_management_level": "President/Vice President",
18 <...>
19}
Purpose-built for AI systems
Data access designed specifically for how agents and LLMs consume data — naturally, autonomously, and at scale.
Agentic Search API
Query millions of B2B records using plain English. Agents get structured, machine-ready data without learning Coresignal's query language — just describe what you need.
- Natural language to structured B2B data in one call
- Semantic understanding of employee roles
- Structured, normalized JSON ready for downstream agents
- Easy integration to any AI pipeline
MCP Server
Drop Coresignal data into any MCP-compatible agent or AI application. Your AI tools connect to live B2B data through the Model Context Protocol — zero custom integration required.
- Compatible with Claude, Cursor, and any MCP-compatible client
- Exposes Company, Employee, and Jobs data as MCP resources
- Agents can call multi-source
company,employeeorjobsAPI - Supports multi-turn refinement within agent sessions
Engineered for how models actually process data
Features engineered to maximize signal quality for LLMs, embeddings pipelines, and retrieval-augmented generation.
Natural language search
Query data using plain-text. Our Agentic Search API converts it to ED DSL query and returns structured B2B records in seconds with unmatched level of context and about each lead
Semantic search
Query data using meaning, not keywords. Our API interprets roles so agents can ask "software engineer" and get semantically matched results for similar roles— no filter syntax needed.
Entity recognition
Automatic resolution of company names, job titles, industries, and geographies. Entities are normalized and deduplicated, so downstream agents receive clean, unambiguous identifiers.
Machine-readable docs
Schema definitions, field dictionaries, and usage examples are structured for programmatic consumption. Feed docs directly into your agent context window without manual reformatting.
Historical data
Access 10+ years of point-in-time records. Track headcount evolution, funding progression, and hiring velocity — enabling temporal reasoning and trend analysis in your AI pipelines.
Real-time data access
Real time data from public web sources. Agents always work with current signals — recent funding rounds, new job postings, leadership changes — not stale snapshots.
Context rich agentic search in seconds
Agent sends a natural language query
Your AI agent describes what it needs in plain English — no SQL, no filter DSL, no schema lookup. The Agentic Search API or MCP tool receives the intent.
Coresignal translates query and calls the API
API handles translation to ES DSL, query execution, ranks results by relevance,
and formats the response
Typed JSON returned to your pipeline
Clean, normalized B2B data flows back — ready for enrichment, scoring, storage, or synthesis. No post-processing required before the next agent step.
Comprehensive B2B data across all entity types
Most data providers cover one slice of the picture. Coresignal combines company firmographics, employee profiles, and job market signals into a single, unified context layer — so your agents don't just find companies, they understand them.
The broader the context your agent operates with, the sharper its decisions. That's why teams building serious AI workflows choose Coresignal as their data foundation.
Works where your stack lives
Native connectors for the tools AI builders already use — from automation platforms to cloud data warehouses.
