Vector search finds similar text. It does not know that this clause belongs to that vendor, or that this patient is on that pathway. We implement Neo4j as the relationship layer under Claude, Gemini, or OpenAI — GraphRAG, not another pile of chunks.
These are the use cases we usually pair with Neo4j. Each one is a real page, not a slogan.
Clauses, parties, and obligations as a graph, not a folder.
Which clause affects which vendor, in one traversal.
Join paths the warehouse never modelled cleanly.
Works, places, and people linked the way curators think.
Knowledge graph
People, accounts, contracts, and assets as nodes your agents can traverse.
GraphRAG
Retrieve by relationship first, then generate. Fewer confident wrong answers.
Agent memory
Write-back of facts the agent learned, with provenance.
Tell us what you are working with. We respond within 24 hours with a frank assessment of what it would cost, how long it takes, and whether it fits your situation.
Get in TouchTell us what you are working with. We respond within 24 hours with a frank assessment of what it would cost, how long it takes, and whether it fits your situation.
Get in TouchVectors are good at “text like this.” Graphs are good at “this is connected to that.” Agents that must not invent relationships need both, and we put Neo4j on the relationship side.
Yes. Cognee is often the memory API the agent talks to; Neo4j is the graph store underneath. We will tell you if you need one or both.
Yes. Aura or self-hosted, with the same residency rules as the rest of the stack.
We will sketch the graph for one domain and tell you whether Neo4j, Cognee, or both belong in the build.
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