A retrieval-augmented pilot demos well and stalls in production, and it usually stalls in the same place. Ask it a question that needs one passage and it is excellent. Ask it a question whose answer is distributed across a contract, an amendment and an invoice, and it returns three plausible chunks with no notion that they refer to the same agreement.
Embeddings encode similarity, not relationship. A knowledge graph encodes the relationship explicitly, so retrieval can traverse from the entity the question is about to the documents that actually bear on it. Used together, vectors find the entry point and the graph decides what else belongs in the answer.
Extraction, graph, retrieval and the citation layer that makes it usable in a regulated setting.
Entities and relationships extracted from your documents and systems, into a graph designed around the questions the assistant will be asked. Extraction quality is the ceiling on everything above it, so this is where the effort goes.
Vector search for the entry point, graph traversal for the context around it, and a re-ranking step that decides what actually reaches the model. Bounded hops, because unbounded traversal returns the whole graph.
Every claim in an answer traced to the source passage and the graph path that produced it. In a regulated environment this is not a feature, it is the condition of being allowed to deploy at all.
Persistent, queryable memory across sessions and systems, so an agent knows what it was told last week and which entity that referred to.
A test set from your own questions, scored on retrieval quality and citation correctness separately, so you know which half to fix when a number moves.
Every engagement opens with your data and your question, never a slide deck.
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Ordinary RAG retrieves passages that resemble the question. GraphRAG additionally traverses the relationships between the entities those passages mention, which is how it answers questions whose evidence is spread across several documents that share no vocabulary.
Usually not. The graph and the traversal step sit alongside the vector index you already have. The retrieval layer changes, the embeddings and the model often do not.
More than vector-only retrieval per query, and less than the alternative of an assistant nobody trusts. Extraction is the expensive part and it is a batch cost, not a per-query one. We size it before you commit.
It lowers the ceiling, which is why extraction quality is measured first rather than assumed. Messy is workable. Contradictory is harder, and worth knowing about before you build on top of it.
When your questions are answered by a single passage, when the corpus is small enough to fit in context, or when nothing in the domain is meaningfully connected. We will tell you that on the first call rather than three months in.
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