Advanced search solutions that go beyond keywords to use embeddings and language understanding for intent-matching and ranked, contextual responses.
Retrieval by meaning rather than by token overlap. Finds documents that answer the question even when they share no vocabulary with it. Delivers results rather than generated answers, which makes it lower risk than RAG and often sufficient.
Corpora where the same concept is expressed in varied language, and users who know a document exists but not what it is called.
Exact-reference lookup. Searching for a specific transaction reference, invoice number or account code is a job for an index, and semantic search will perform worse than a database query.
Ranking must be explainable enough that a user understands why a result appeared, or the absence of an expected result becomes impossible to diagnose.
Semantically similar but factually irrelevant results ranking above the exact match, because the exact match was terse and the near-miss was verbose.
Searching evidence documents for the support behind an accrual where the reconciliation calls it 'utilities' and the contract calls it 'energy supply services'.