Vector database
A database optimized to store embedding vectors and retrieve the ones most similar to a query vector quickly, serving as the retrieval layer for AI systems.
In more detail
A vector database is built to answer one question efficiently: given this query vector, which stored vectors are closest? That nearest-neighbor search over embeddings is the core operation behind semantic search and retrieval-augmented generation, and doing it fast over millions of items is what a purpose-built vector store provides.
A vector database is a component, not a solution. It needs an embedding step ahead of it and a generation or ranking step after it. On its own it retrieves similar vectors; the surrounding pipeline decides what to retrieve, how to rank, and what to do with the results.
Where this shows up at Ceven
Ceven handles the retrieval layer as part of a workflow rather than asking the customer to stand up and operate a vector database themselves. When a step needs to ground an answer in stored knowledge, the retrieval happens inside the workflow, and the passages it surfaces feed the model with the relevant context.