Retrieval-Augmented Generation has become the default pattern for grounding large language models in private company data: embed your documents, retrieve the most similar passages for a query, and let the model answer from that context. It works remarkably well for simple lookups. But as enterprises push RAG into real operational workflows, the cracks in pure vector search are showing.
The core problem is that semantic similarity is not the same as relevance. Ask a vector index "which customers are affected by the outage in the Frankfurt region?" and it will happily return passages that talk about outages, Frankfurt, or customers — without ever joining the three. Questions that require traversing relationships between entities are precisely where embedding-based retrieval falls short.
Our answer is a hybrid architecture that pairs semantic search with a property graph. During ingestion we extract entities — customers, systems, contracts, people — and the typed relationships between them, storing prose in the vector index and structure in the graph. At query time, a router decomposes the question: relationship hops are answered by graph traversal, while descriptive context is pulled from the vector store, and both are merged into the model's context window.
In our benchmarks on multi-hop enterprise questions, the hybrid approach improved answer accuracy substantially over vector-only retrieval while also making answers auditable — every fact in the response can be traced to either a graph edge or a source passage. We believe this traceability, as much as the accuracy gain, is what will carry RAG from pilot projects into regulated, production-critical use.
Back to all articles