Ask your AI system which clients have exposure to a supplier two steps removed in a pending regulatory action.
Vector search returns documents that resemble the query. It cannot follow the actual relationships between clients, suppliers, regulators, and filings to the answer. For a growing class of enterprise queries, that's the whole problem.
Microsoft Research named the mechanism: vector retrieval fails on global questions because answering them requires assembling a view across structure, not ranking chunks by similarity.
Seven companies are now betting against that failure mode. Two populations, same architectural conclusion:
RAG-accuracy side: WhyHowAI, Nand AI, AIntropy — built specifically around the failure of chunk retrieval on complex enterprise corpora.
Graph database side: Neo4j, ArangoDB, Diffbot, Memgraph — different origin, same destination.
Cross-population convergence on the same architectural layer is a structural signal. Not one vendor's marketing cycle finding its audience.
But the honest version is narrower than the vendors are advertising. ICLR 2026 benchmark work: graphs beat vectors on multi-hop, global, and schema-intensive queries. Graphs lose on single-hop factual and time-sensitive queries. Most vendors aren't drawing that line.
ArangoDB AutoRAG routes automatically between graph/hybrid/vector based on query type. WhyHowAI builds task-scoped graphs, not monolithic ones. Both are designed around the conditionality.
Most in this layer aren't. That gap is the investment question.
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