Why meaning beats keywords
Traditional keyword search only matches the exact words a user types, so it misses synonyms, paraphrases, and related concepts — and it has no sense of which results are most relevant. Semantic search represents both the query and your content as vectors (numerical fingerprints of meaning) and returns the closest matches, so “reset my password” finds an “account recovery” article even though they share no words.
The architecture we build
- Embeddings: turn your documents, records, and queries into vectors using a model suited to your domain and languages.
- Vector index: store and search those vectors at scale (e.g. Qdrant, pgvector) with metadata filters for permissions, dates, and categories.
- Hybrid retrieval: blend keyword and vector search so you keep exact-match precision while gaining semantic recall.
- Re-ranking: apply a re-ranker to order the final results by true relevance, not just raw similarity.
What teams use it for
- Internal knowledge bases and support search.
- Product, document, or catalogue discovery across mixed formats.
- The retrieval layer underneath a RAG assistant.
Our process
Semantic search is part of our AI solutions practice. We start from your real data and a set of representative queries, tune retrieval against measurable relevance, and hand over the full pipeline. To understand the generation layer that often sits on top, read What is RAG?