Case Study: SigmaMind AI achieves sub-second voice agent response times with Deepgram

SigmaMind AI reduces voice agent latency by 300ms with Deepgram

sigmind ai builds a no-code platform for deploying production-grade voice AI agents. Their challenge was building a voice infrastructure that could scale reliably, requiring a speech-to-text provider that delivered high accuracy, low latency, and consistent performance under heavy load for their orchestration layer to depend on. They needed this to handle real conversations with interruptions and background noise.

Deepgram provided the real-time streaming speech-to-text solution with its Nova-3 and Flux models. This integration reduced end-to-end agent response latency by approximately 300 milliseconds and enabled agents to act on speech before a sentence was finished. The results for sigmind ai included processing over a million calls per month, a 50% increase in outbound call conversion for one call center customer, and the ability to handle 150 peak concurrent voice sessions without degradation.

SigmaMind AI