Making the Knowledge Layer a Graph You Actually Traverse

A follow-up to a prior deployed RAG-plus-knowledge-layer project replaces a keyword-based query router with an always-fused retrieval pipeline that runs semantic search and graph traversal on every query. The redesign adds bitemporal validity windows to graph edges (enabling as-of, timeline, and diff queries), moves contradiction detection from manual curation to automatic detection at ingest time, and introduces a two-threshold entity-resolution pipeline with embedding similarity and LLM adjudication. Measured on a synthetic insurance corpus, alias-only resolution produced 149 extracted concepts against 19 canonical ones; the new resolver cut that to 120. An ablation on a small golden query set found search-only and fused recall tied at this corpus scale, while the fused pipeline added an average of 10 relational paths per bundle that search-only retrieval could not produce. The stack adds Cosmos DB for Apache Gremlin as a second engine alongside Azure AI Search, with a change-feed-driven projection worker keeping both derived from a single system of record.

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