Why Tuner exists.

Our Story

Built by the Team That Processed a Million Calls

Before Tuner, we built Ginni.ai — a voice AI platform that processed over a million real human calls for sales, support, and complex enterprise use cases.

To make it work, we spent 18 months building AI judge models from scratch — learning how to write evals that actually measure what matters: correctness, compliance, outcomes, and the complex KPIs that enterprise use cases demand.

We worked directly with raw audio — and that changes how you see everything. We saw how background noise corrupts transcripts. We saw how a poor transcript doesn't just degrade quality — it derails the entire conversation. We learned that building for English is one challenge, but the moment you add other languages, you're dealing with accents, dialects, and linguistic edge cases that no amount of pre-launch testing fully prepares you for.

That work taught us three things the hard way: how conversations fail in ways you never anticipate, how critical signals get buried in noise, and how fast a team loses confidence in a system that can't explain itself.

Then we built our own voice agent. And we hit the same wall.

Production was brutal. Unpredictable questions, noisy environments, silent tool failures. The only way to understand what was happening was to manually review call after call (hours of listening to calls nobody wants to listen to 🎧). Logs weren't enough. Testing hadn't caught it.

We realized this wasn't just our problem. It was every voice AI team's problem.

Tuner is the platform we built because we needed it ourselves — and couldn't find it anywhere else. It takes everything we learned at Ginni and turns it into the production observability layer every serious voice AI deployment needs.