Standard enterprise search fails on cross-document reasoning. An analyst asking what your organization committed to on regulatory capital across the last three board decks gets zero answers. GraphRAG builds a knowledge graph over all documents first, then queries it with context-aware traversal.
Knowledge workers at large enterprises spend an estimated 20–30% of their time locating information that already exists inside the organization. The failure mode is not search accuracy on simple lookups — it is the complete collapse of retrieval on questions that require synthesizing across multiple documents, time periods, or organizational units. Standard vector RAG retrieves relevant chunks but has no model of how those chunks relate to each other.
GraphRAG (Microsoft Research, 2024) changes the retrieval architecture. Before any query is processed, the system builds a structured knowledge graph over the entire corpus — extracting entities, relationships, and hierarchical community summaries. When a query arrives, it traverses this graph rather than scanning a flat vector index, producing answers that require cross-document reasoning that vector RAG cannot handle at any retrieval depth.
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