Cekura stress-tests voice and chat agents with simulated customers

Voice agents fail in ways chatbots don't. They mishear you, they stumble on pauses, they sound robotic at exactly the wrong moment. Cekura, a startup working with more than 70 conversational AI companies, has built its whole business around catching those failures before customers do.

The pitch is straightforward: instead of waiting for angry support tickets, run your voice agent against simulated callers with different personalities and see where it breaks. Cekura's platform then layers on production monitoring — tracking things like interruption handling, latency, sentiment, even pitch — across every real call, not just the test ones. It's the kind of instrumentation that sounds obvious once you hear it, but almost nobody was doing systematically for voice until recently.

What's notable is the emphasis on tuning the judges themselves. Cekura's Labs feature lets teams edit and replay evaluation prompts against actual call recordings until the automated scoring matches what a human would say. That's a tacit admission that off-the-shelf LLM evaluation is often wrong, and that getting voice QA right means treating the judge as something you calibrate, not something you trust blindly.

The company's recent blog posts hint at where this is heading: clustering thousands of failed calls into a handful of root causes, rather than drowning teams in individual transcripts, and building