Give AI agents access to organizational knowledge through managed vector storage. Ingest, index, and retrieve documents so agents can ground responses in real data instead of general training.
A knowledge pipeline showing documents flowing into vector storage and being retrieved by an agent during a live conversation.
General-purpose models know a lot, but they do not know your policies, procedures, or operational context. Vector storage lets teams give agents access to organizational knowledge so responses are grounded in real documents instead of generic training data.
The managed vector database handles ingestion, indexing, and retrieval so teams can focus on the knowledge itself rather than infrastructure.
An agent responding to a caller question by retrieving relevant policy documents from vector storage in real time.
Document sources (Google Drive, internal wiki, policy database) flowing through an ingestion pipeline into vector storage.
Connect documents from Google Drive, internal wikis, policy databases, and other knowledge sources. The platform handles chunking, embedding, and indexing so content is searchable by agents at query time.
Updates to source documents can be re-ingested to keep agent knowledge current without manual intervention.
When an agent needs to reference organizational knowledge, the vector database retrieves the most relevant content based on semantic similarity. This grounds agent responses in real data and reduces hallucination.
Retrieval is integrated into the agent runtime so knowledge access happens within the same operational and security boundaries as the rest of the platform.
A live conversation where the agent retrieves a relevant policy excerpt from vector storage and uses it to answer a caller question accurately.
Managed vector storage integrated into the trusted neutral platform.