Combine knowledge graphs with LLMs for multi-hop reasoning, traceability, source alignment, and relationship exploration across connected entities.
Uses an explicit graph of entities and relationships to support reasoning that requires following connections — this entity relates to that one, which relates to a third. Retrieval alone struggles here because the answer is in the structure rather than in any single passage.
Questions requiring multi-hop traversal, and domains where relationships between entities carry the meaning — ownership structures, dependency chains, provenance trails.
Where no graph exists and building one is the project. Graph construction is substantial work and only pays back when traversal is genuinely the need.
The traversal path should be exposed, since the value over plain retrieval is precisely that the reasoning is inspectable.
Graph staleness. Relationships change and the graph does not, producing confidently wrong multi-hop conclusions built on edges that no longer hold.
Tracing an audit question backwards through match record to reconciling item to correcting entry to ERP document — a chain of relationships rather than a body of text.