AI glossary
What is vector search?
Vector search is a technique that finds results by meaning rather than exact keywords, by converting text into numerical 'embeddings' and matching items that are closest in meaning.
Traditional search matches keywords — if the words don't appear, it misses the result. Vector search instead turns text into embeddings: long lists of numbers that capture meaning. Two pieces of text about the same idea end up 'close together' in this numeric space, so a search for 'cancel my plan' can find a page titled 'ending your subscription' even with no shared words.
This semantic matching is what makes modern AI search feel smart, and it's the retrieval engine behind RAG — finding the most relevant passages of your content to feed an AI model. It also powers better product search and recommendations.
We build vector search into client products — for AI assistants, documentation search and ecommerce discovery — so users find things by what they mean, not just the exact words they type.
Common questions
How is vector search different from keyword search?
What is vector search used for?
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