Glossary · Technical
Vector Database
A database built to store embeddings and quickly find the ones most similar to a given query — the retrieval half of most AI search systems.
How it works
Instead of indexing exact words like a traditional search database, a vector database indexes embeddings and answers questions like "which stored documents are closest in meaning to this query?" That similarity search is what powers retrieval in most systems that combine a language model with an external knowledge source.
Why it matters for AI visibility
If an AI platform's retrieval layer draws from a vector database of indexed web content, a brand's pages need to actually be in that index to ever be retrieved. Being well-written and accurate doesn't help if the underlying system never embedded and stored the page in the first place.
See more terms in the full glossary, or read the AI Visibility 101 guide for the full picture.