Semantic search finds information based on meaning instead of literal word matching. It converts text into embeddings, numeric representations of meaning, and retrieves results that are conceptually close to the query, so a search for cancel my plan also surfaces documents about ending a subscription.

This matters because keyword search misses anything phrased differently from the query, which frustrates users and hides relevant content. Semantic search understands intent and synonyms, returning better results with less manual tuning. It is also the retrieval engine behind RAG, supplying the relevant context a language model needs to answer grounded questions.

arosplatforms builds semantic search over client knowledge bases, support content, product catalogs, and internal documents, using a vector database for fast matching. We tune and evaluate retrieval quality directly, because in a RAG system the answer is only as good as the documents semantic search brings back.