AI data
Vector DB
A vector database is how AI features find what’s relevant — searching by meaning, so models answer from your data instead of guessing.
What it is
A vector database stores embeddings — numeric representations of meaning — and finds the closest matches to a query. It is the retrieval layer behind semantic search and grounded AI answers.
Why we use it
Models are only as useful as the context you give them. A vector database lets a feature pull the right passages from your own content, which is what turns a generic model into something that knows your business.
Where we put it to work
In the product,
not the slide.
01
Semantic search
Finding content by meaning, not just keywords.
02
Grounded answers
Feeding models the right context so answers cite your data.
03
Recommendations
Surfacing similar items based on meaning and behaviour.
Pairs with