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Use case

The structured data layer your AI agents run on.

Agents reason poorly over flat exports — they get text to search, not structure to traverse. GraphIQ.ai exposes the identity graph through a native MCP server, so autonomous agents query relationships directly and act with grounded context.

Give an agent a list and it guesses. Give it a graph and it reasons.

The old way

CSVs and plain REST endpoints — agents pattern-match strings and infer connections that aren't really there.

With GraphIQ.ai

A native MCP server — agents traverse resolved entities and relationships, each carrying provenance and a confidence score.

How it works

From prompt to grounded action.

01

Connect the MCP server

Point your agent or LLM at the GraphIQ.ai MCP endpoint.

02

Query the graph

Agents resolve entities and traverse relationships in-context, as tools.

03

Ground every answer

Responses carry provenance and confidence — verified edges, not guesses.

04

Act in the loop

Agents write resolved results back to CRM, Clay, or downstream tools.

What it unlocks

Fewer hallucinated relationships
Grounded, cited answers
Real-time graph traversal
One data layer for every agent
FAQ

Frequently asked questions

What is the MCP server?

A Model Context Protocol endpoint that lets LLMs and agents call GraphIQ.ai directly — resolving entities and traversing the graph as native tools, with no export step in between.

Do I need Clay or a CRM to use it?

No. Agents can query the MCP server directly; Clay and your CRM are optional activation targets for whatever the agent resolves.

How do you prevent hallucinated connections?

The graph returns resolved entities with confidence scores and provenance, so agents act on verified relationships instead of inferring them from raw text.

Ground your agents in the graph.

Connect the MCP server and let your agents reason over real relationships.