GraphIQ.ai is a B2B identity graph that resolves 300M+ organizations into structured, continuously updated data — employees, relationships, signals, and corporate hierarchies — accessible to your revenue team and AI agents through the product UI, API, and MCP server.
300M+ companies globally indexed. Searchable by capabilities, industry categories, revenue, employee count, technology stack, partnerships, and location — and by corporate hierarchy from parent company to every subsidiary.
370M+ employees with monthly updates. 59M business emails. 43M cellphone numbers. Search by function, level, and title — or pull all employees across a parent company and its subsidiaries at once.
65K+ technologies sourced from websites, job postings, and employee skills. Find the companies running the stack your product sits adjacent to.
Connect to Salesforce, HubSpot, Microsoft Dynamics, Clay, ChatGPT, Claude, LangChain, and custom APIs.
428 searchable industry categories built from how companies describe themselves online — not from a taxonomy a committee wrote in 1996. Search for "cylindrical ceramic ball bearing manufacturers serving aerospace" and get actual matches.
1.3B+ news articles tracked in real time. Business events linked to the right entity — hiring moves, funding rounds, partnerships, M&A activity — surfaced for the accounts that matter to your pipeline.
560K+ investment rounds tracked from Seed to IPO. Surface which accounts just received funding — and are about to start buying.
GraphIQ.ai is the data foundation — global, structured, real-time B2B entity data — built so AI systems have something solid to work from. Not as another enrichment layer. As the part most people skip entirely.
Most AI systems rely on incomplete data sources such as: CRM exports, scraped websites, spreadsheet-based enrichment tools, traditional contact databases. These sources contain records but not relationships. As a result, AI systems often generate answers that are confident but incorrect. Even well-designed AI agents hallucinate when the underlying data lacks context.
AI agents must answer questions such as: What subsidiaries belong to a company? What suppliers support a manufacturer? What companies compete in a specific capability area? These questions require relationship data, not static records. Traditional enrichment platforms store data in tables. Knowledge graphs store connections between entities.
Entity resolution means that when GraphIQ.ai encounters "Lucasfilm Ltd," "Lucasfilm," "Lucas Film," and "the company that made Star Wars" across disparate data sources, all four resolve to the same entity: a single verified record with authoritative identifiers, linked to Disney's 153-entity corporate family and updated every time any of its tracked employees or signal sources produce new information. This is what makes GraphIQ.ai structurally different from a contact database. A database stores isolated rows; a graph resolves identities.
What each company actually does — inferred from websites, job postings, and operational signals rather than a static industry code. The dimension that powers capability-based search and lookalike discovery, matching companies on real function instead of a decades-old taxonomy.
300M+ companies resolved to single verified nodes. Each carries a canonical name, industry classifications, verified employee count, technology footprint, and geographic coordinates.
Parent, subsidiary, division, and affiliate relationships mapped and maintained. When a company acquires another, the graph updates. If a subsidiary is renamed, the entity persists with its history intact.
370M+ individuals linked to current employer entity, role, seniority, contact information, and career history.
Supplier, investor, customer, partner, and regulatory relationships mapped as first-class edges in the graph — not text fields.
Job changes, hiring events, funding, M&A, news, and regulatory events anchored directly to the node they describe.
Knowing that a deal with Lucasfilm is also a potential deal with 152 other Disney entities requires the corporate family to be structurally mapped, not guessed.
An AI agent working from a contact database produces answers as confident as its data and as wrong as its duplicates. An agent working from a resolved graph produces answers with verifiable entity references.
Enriching a CRM record against a resolved entity rather than a fuzzy text match means the enriched data is accurate, deduplicated, and traceable to a source.
AI agents interact with GraphIQ through the GraphIQ MCP server. This allows agents to query the graph dynamically. Example agent workflows include: identifying companies similar to a target account, mapping corporate hierarchies, retrieving relevant stakeholders across subsidiaries, incorporating real-time news signals into prospecting decisions.
Here are some examples:
300M+ organizations as of current indexing. Coverage is global, with highest density in North America, Western Europe, and Asia-Pacific.
Continuously. GraphIQ.ai processes signals daily and updates entity records as new information arrives. There is no fixed batch-refresh cadence.
Yes, via the Model Context Protocol (MCP) server available at Pro and above.