Glossary
GraphRAG.
A retrieval architecture that uses a knowledge graph (entities and the relationships between them) instead of, or in addition to, a flat vector store. Lets AI answer questions that require connecting multiple documents through the entities they share.
Basic RAG retrieves a passage. GraphRAG traverses a network. When the answer requires connecting things, only a graph can find it.
What this looks like in practice
What the graph adds.
A knowledge graph stores entities (people, projects, products, decisions) and the relationships between them. Instead of asking "find me a passage about X," GraphRAG asks "find me everything connected to X, and how those things connect to each other." The answer can span dozens of documents because the graph already knows they're related.
The queries it unlocks.
"Who owns the contract renewal for the customer attached to last quarter's launch?" requires hopping from project to customer to owner. "What did everyone who's worked on this account learn about their procurement process?" needs to traverse across all touchpoints. Vector RAG fails both. GraphRAG follows the chain.
The hard part: which connections actually matter.
Common entities flood every document. The names of your platforms, your team, your current quarter. A naive graph treats every mention as a connection and floods retrieval with noise. The harder engineering is distinguishing genuinely central entities from commonplace ones, for THIS company's data, not in general. Building the graph is one problem. Making it useful is a bigger one.
Connections, not just passages.
Microsoft popularized the GraphRAG term in 2024, but the architecture itself isn't new. What's new is making it accessible without building the graph by hand. That's what we shipped: a graph that builds itself from your existing content.
Bring your knowledge.Simply ask.
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