Cekura enables Conversational AI teams to automate QA across the entire agent lifecycle—from pre-production simulation and evaluation to monitoring of production calls. We also support seamless integration into CI/CD pipelines, ensuring consistent quality and reliability at every stage of development and deployment.
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Cekura
Launching today
Cekura is the testing, observability, and self-improvement platform for production voice and chat AI agents. It simulates thousands of scenarios, catches failures, diagnoses the root cause, rewrites prompts and config, then re-validates with a full regression sweep. Unlike tools that hand failures back to your team, Cekura closes the loop by fixing the agent itself and proving the fix holds without overfitting.
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When monitoring production calls, how does Cekura detect quality issues in real time, and what kind of alerting or reporting does it provide to teams?
Does Cekura support testing across multiple languages and accents, given how critical that is for conversational AI reliability?
How does Cekura evaluate more nuanced qualities like tone, empathy, or conversational flow, beyond just accuracy or task completion?
For teams building voice based agents specifically, does Cekura account for latency and audio quality issues, or is the focus primarily on conversational logic?
How customizable are the evaluation metrics? Can teams define their own quality benchmarks based on their specific use case?
Thanks — the GitHub Actions path is the answer to that question. The follow-up I'd have is whether the infra regression suite runs against a live agent clone or replays recorded sessions, because customer-support agents with integration state tend to behave differently on replay versus a live environment.