Use case

Know when your agent makes things up.

Voxli flags every claim your agent makes that nothing supports, facts and actions alike. Compare the count before and after a model change.

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You swap the model or change the stack behind the agent.

Hallucination rates move when your stack changes. The model matters most, but retrieval and knowledge base changes shift them too. Nobody notices until a customer shows up to a doctor's appointment the agent confirmed but never booked.

After a model swapRAG changeknowledge base update

Every answer, read claim by claim.

Voxli checks each claim in the reply against your knowledge base and catches the one the agent invented.

Catch made-up facts and made-up actions.

A Voxli test result with a hallucination grounding check

  • Every claim, labeled

    Voxli splits an answer into claims and labels each one supported or unsupported. A claim can be a fact or an action the agent says it took.

  • Exclusion rules cut the noise

    Mark a claim safe once and Voxli skips matching claims on future runs, so the flags you read stay meaningful.

  • A count you can compare

    Track hallucination count over scheduled runs, or compare it before and after a model swap to see which model invents more.

Expertise.ai

Expertise.ai builds conversational AI agents and uses Voxli to catch multi-turn regressions before they ship.

Read the story

Put a number on what your agent makes up.

We are early stage and founder led. Book a call and talk directly with the people building Voxli.

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