RAG Voice Agent

Your knowledge is indexed as embeddings in a vector database. The caller's question is embedded too, and the closest matching passages are pulled out.

Those retrieved passages are added to the model's context, so it answers with your facts in front of it instead of guessing from training data.

The language model composes a natural, spoken reply grounded in the retrieved text—phrased conversationally for a voice call.

Retrieval works by meaning. 'How long is the guarantee?' finds your warranty passage even if it never uses the word 'guarantee'.

Because answers trace back to a real passage, the agent stays on-topic and can be configured to decline rather than invent.

Update a document and the index refreshes in minutes. No fine-tuning run, no redeploy—the next call uses the new facts.

Every response is built on retrieved text from your knowledge base, not on the model's imagination.

When nothing relevant is retrieved, the agent is set up to admit it and offer a handoff rather than bluff an answer.

The agent can tell the caller which document an answer came from, so people can trust and verify what they hear.

Each knowledge base has its own vector namespace. Retrieval never crosses into another agent's data.

Tune how many passages are retrieved (default 5) to balance breadth of context against focus and latency.

Low-confidence moments can route the caller to a human instead of forcing the agent to answer.