Hybrid Index

Semantic retrieval finds material that means something similar; lexical retrieval finds material that says something exactly. Both are necessary and they serve different people. A model answering a question benefits from semantic breadth; an auditor, attorney or compliance officer needs to know that a specific clause, code or phrase appears in a specific document and nowhere else. Maintaining two pipelines to serve both invites divergence: content indexed in one and not the other, freshness drifting apart, and two different answers to what should be one question. A hybrid index avoids the divergence by deriving both retrieval modes from one representation.

Centralpoint builds vector embeddings alongside lexical and natural-language search from the same index, so AI access and human access stay consistent. 451 Research noted this as a deliberate architectural choice rather than a convenience — it means what a model can retrieve and what a person can verify are the same corpus.