Vector database
A vector database stores embeddings and finds the items most similar in meaning to a query, making search by meaning fast at large scale.
Vector database is a kind of database built to store embeddings, the numeric representations of meaning, and to find the closest matches to a query very quickly. Instead of looking for exact text, it ranks stored items by how near they are to the query in meaning.
This is what makes search by meaning practical when there are large numbers of documents. A vector database can scan many points and return the most relevant ones in a fraction of a second, which is why it is a common building block for AI systems that retrieve knowledge.
In context
In customer support, a vector database holds the embeddings of help articles, past tickets, and policy documents. When a question comes in, the system converts it to an embedding and asks the database for the closest matches, which become the source material for an accurate answer.
Because it ranks by meaning, it returns relevant content even when the customer uses different words than the documentation. This is the retrieval step that keeps automated answers grounded in the company's real, approved content.
How Auralis uses Vector database
Auralis stores each client's approved help content in a vector database so its done-for-you service retrieves the right source material for every answer, which keeps automation grounded and helps deflect 74% of repetitive tickets.