What is semantic search?

Semantic search is built on embeddings — numeric vectors with which a model encodes the meaning of text. The query and fragments of conversations are turned into such vectors, and the system finds records whose vectors are closest to the query's vector. So the query "customer unhappy with delivery" will find conversations about "took forever to arrive," "the parcel got lost" and "when will it finally come," even without those exact words.

This differs fundamentally from full-text search, which matches strings and stumbles on synonyms, paraphrases, typos and colloquial speech. In call transcripts people phrase the same idea in dozens of ways, so keyword search misses much of what's relevant, whereas meaning-based search doesn't.

For a business, semantic search turns an archive of conversations into a base that answers back: you can ask in natural language and get all the relevant calls — by topic, objection, reason for contact — without inventing exact keywords. This speeds up incident review, finding examples and preparing a knowledge base.

Semantic search is an applied layer on top of speech analytics: it works over transcripts and extracted data and makes the archive self-documenting. The same embedding mechanism underlies linking conversations to a knowledge base for AI.