Embeddings API

A way of turning text into numbers that capture meaning, so a computer can tell that two differently worded passages are about the same thing. Everything useful, from semantic search to AI grounding, builds on that.

Very little per document. Embedding models are among the cheapest in the API, priced per million tokens, and the real cost is in doing the pipeline properly, once.

No. API business data is not used for training by default.

It depends on scale. Options run from managed vector stores to capabilities inside platforms you already own. We recommend the simplest thing that meets your volume and latency, not the trendiest.

Search that matches meaning rather than exact words, so a search for staff leave policy also finds a document titled annual holiday entitlements. Embeddings are what make that possible, and it is why AI answers from the right document instead of the one that happened to share a keyword.

Nearly always because the retrieval step picked the wrong source, not because the model is weak. Fixing the embeddings and how content is chunked usually improves answer quality far more than changing model.

Whenever the underlying content changes. Stale embeddings are one of the quieter failure modes: the AI keeps answering confidently from a policy you replaced six months ago. We build the refresh into the pipeline rather than leaving it manual.

Yes, if data sovereignty requires it. There is a trade-off in quality and effort, and we will set that out plainly rather than assuming either answer.