Hamming tests your AI voice agents 100x faster than manual calls. Create Character.ai-style personas and scenarios. Run 100s of simultaneous phone calls to find bugs in your voice agents. Get detailed analytics on where to improve.
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Launch Team / Built With
Render Sandboxes: a computer for every agentIsolated compute, persistent state and a real filesystem
Promoted
Hamming AI (YC S24)
🪄 Our solution involves four steps:
(1) Create diverse but realistic user personas and scenarios covering the expected conversation space. We create these ourselves for each of our customers.
(2) Have our agents call your agent when we test your agent's ability to handle things like background noise, long silences, or interruptions. Or have us test just the LLM / logic layer (function calls, etc.) via an API hook.
(3) We score the outputs for each conversation using deterministic checks and LLM judges tailored to the specific problem domain (e.g., order accuracy, tone, friendliness).
(4) Re-use the checks and judges above to score production traffic and use it to track quality metrics in production. (i.e., online evals)
We created a Loom recording showing our customers' logged-in experience: Logged-in Video Walkthrough
We think there will be more and more voice companies, and making the experimentation process easier is a problem we are excited about solving.
📩 If you're building voice agents and you're struggling to make them reliable, reach out at sumanyu@hamming.ai!
❤️ Shoutout to @rajiv_ayyangar and @gabe for helping us with the launch!
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