Conversation Memory Storage

DopplerAI's API stores memory, such as prior conversation turns or user context, so it can be retrieved later by a connected application. A Beam agent writes new memory entries when a matching event occurs, for example a conversation ending, and applies the account's approved rule for what gets stored and how it's tagged for later use. This removes someone manually organizing memory entries as conversations accumulate across every application. Entries containing unclear, conflicting, or sensitive data outside the approved rule are routed to a person instead of being stored automatically.

Vector Embedding Updates

When source data changes, such as an updated document or record, its vector embedding in DopplerAI can go stale and stop matching new queries well. Where the connector and account permissions allow it, a Beam agent detects the change and applies the approved rule to regenerate and update the affected embedding. This keeps search and retrieval results aligned with current data without someone re-indexing content by hand. Changes involving an unrecognized data type or a large-scale re-index request are routed to a person before anything proceeds further.

Pipeline Output Checks

DopplerAI handles incoming data before it becomes memory or embeddings, and that handling step can fail or produce unexpected output along the way. A Beam agent reads the resulting output and applies the account's approved rule to confirm it matches expectations before anything is written to DopplerAI. This adds a check on automated data handling rather than assuming every pass succeeded without incident somewhere in the pipeline. Failed or unexpected outputs are routed to a person to check before any data is stored or updated further.