Speechmatics and Cekura bring real-world STT testing to voice agent pipelines

Most voice AI failures do not happen in the demo. They happen in production, with real accents, real background noise, and users who switch mid-sentence between languages. Clean-audio benchmarks rarely surface these issues before deployment. By the time a transcription problem becomes visible, it is already in front of users.

Cambridge-based voice AI company Speechmatics and Cekura, an automated QA platform for conversational AI teams, are today announcing a new integration. The partnership embeds Speechmatics' speech-to-text engine directly into Cekura's testing and production monitoring platform, giving voice agent teams a way to test against the full complexity of production audio at every stage of development and deployment.

For Cekura, the decision to build the integration around Speechmatics came down to performance on the edge cases that matter most:

We were really impressed by Speechmatics' performance on complex medical scribing and seamless mid-sentence language switching. What stood out even more is their commitment to providing independent, unbiased benchmarks. We are excited about what this collaboration means for teams building at the frontier of Voice AI. – Sidhant Kabra, Co-Founder, Cekura.

Cekura supports the complete QA lifecycle, from pre-production simulations and CI/CD pipeline integration through to monitoring of live conversations. Adding Speechmatics to that layer means teams are testing transcription inside a working pipeline, against the conditions it will actually encounter, rather than in isolation.

The practical scope is significant. Teams can assign Speechmatics to specific testing personas to validate agent performance against complex speech patterns and diverse dialects, and simulate multi-speaker audio, noisy environments, and rapid back-and-forth dialogue.

Builders get access to capabilities including advanced speaker intelligence, real-time and recorded media transcription, and a dedicated Medical Model that allows clinical agents to be tested on drug names, dosages, and terminology before any patient interaction occurs, reducing the risk of errors where accuracy carries direct consequences.

The integration also introduces controlled, head-to-head comparisons between STT providers, including Azure, Gemini, and Deepgram, within a consistent testing environment. Teams can evaluate performance against their own audio conditions and user base rather than published benchmarks that may not reflect their production reality.

Most voice agent failures don't happen in the demo, they happen in production, with real accents, real noise, and real complexity. Development teams can now test against those conditions before they go live, with a transcription layer already proven in the world's most demanding environments. – Ricardo Herreros-Symons, Chief Strategy and Revenue Officer, Speechmatics.

To book a demo, visit cekura.ai/expert.