Knowledge Graph for AI

A knowledge graph
your agents can traverse.

A knowledge graph for business AI models accounts, deals, contacts, tickets, and documents as typed entities and relationships — enabling multihop queries vector RAG cannot answer.

Vector-only RAG retrieves chunks — it does not join entities across CRM, contacts, tickets, and contracts. Gyri combines hybrid search with a typed graph and multihop GraphQL for business AI.

Related: RAG vs graph · Multihop GraphQL

The problem

Why typical alternatives
fall short.

01

Flat vector retrieval

Similarity search misses relationships — "which open deals share this churn signal across support and email?"

02

Keyword search alone

Traditional enterprise search returns documents, not correlated operational narratives.

03

No structured insight layer

RAG pipelines embed text but do not persist typed conclusions agents and humans can reuse.

Why Gyri

Four capabilities
most lack.

Federated GTM stack

One query across your stack

CRM, email, Slack, and docs federated into a workspace graph — not doc-only search or single-app plugins.

Cited AI answers

Synthesis you can audit

Claim-level citations with links to source records — trace any answer back to source. Verify before customer-facing or legal use.

Context that compounds

Memory that persists

Typed insights in a knowledge graph accumulate across agents and quarters — not session resets.

MCP write-back

Agents that close the loop

Claude, Cursor, and internal agents search, synthesize, and write insights back to CRM and workflows.

Compare

Gyri vs typical
alternatives.

CapabilityGyriVector-only RAGKeyword search
Typed entity graph
Multihop GraphQL
Hybrid keyword + graph retrieval⚠️⚠️
Cited multihop synthesis
Agent graph write-back

Named comparisons: RAG vs knowledge graph · Multihop GraphQL for BI

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Use cases

Built for
GTM operators.

Account 360

Traverse contacts → opportunities → threads → tickets in one graph query.

Pipeline risk

Correlate stale deals with support spikes and email silence — multihop, cited.

M&A diligence

Cross-source entity linking for contracts, comms, and financial docs in one workspace.

Gyri think verbs

Agents that remember
what they conclude.

Gyri think verbs turn federated search into a research lifecycle — recall prior insights, ground in live CRM, reflect drafts, promote cited conclusions that compound across Claude, Cursor, and stored agents.

think verb=recall

Semantic orient — load prior cited conclusions before you re-derive from scratch.

think verb=ground

FTS-only orient on live federated CRM, email, and Slack — zero embed cost.

think verb=consider

Classify novelty against the workspace corpus before you commit a thesis.

think verb=reflect

Capture drafts, tensions, and questions as typed thoughts on the graph.

think verb=traverse

Multihop GraphQL in one think call — deal → contact → email → ticket.

think verb=fetch

Hydrate full rows by ref when search or traverse surfaced the right node.

Thought verbs deep dive · Claude + Gyri · Cursor + Gyri

Upgrade from RAG chunks to a business graph.

Free trial — graph, search, and cited synthesis together.

Frequently asked questions

Why use a knowledge graph instead of RAG?

Vector RAG retrieves similar chunks. Knowledge graphs join entities across systems — e.g. linking churn tickets to open renewals and email silence in one query.

Does Gyri support GraphQL?

Yes. Gyri exposes multihop GraphQL for operators and agents to traverse federated business entities.

Is hybrid search available?

Yes. Gyri combines keyword retrieval with graph traversal and cited synthesis.