Enterprise Knowledge Graphs: When They Help RAG and When They Don't

A knowledge graph represents entities and their relationships explicitly — this contract references that vendor, that vendor is subject to this policy. Layered on top of retrieval, it lets a system answer relationship questions a pure similarity search can't: not just 'find documents about X' but 'find everything connected to X two hops away.'

That capability is genuinely valuable for domains with dense, explicit relationships — vendor and contract networks, org charts, regulatory dependency chains. It's substantially less valuable for domains where the useful signal is mostly in unstructured prose, where the added modeling and maintenance cost of a graph outweighs what it buys you over well-tuned hybrid retrieval.

The maintenance cost is the part that gets underestimated. A knowledge graph is only as good as the entity extraction and relationship modeling that built it, and both drift as source documents change. Without a real pipeline to keep it in sync, a knowledge graph becomes stale faster than a vector index does, because there's more structure that can go wrong.

Our default is to start with strong hybrid retrieval and only introduce a graph layer once there's a concrete class of relationship question the retrieval-only system can't answer — not because a graph sounds more sophisticated in a pitch.