SuperBryn

Teams building voice agents find that their systems pass demos and tests but fail silently once deployed against real customers. Monitoring at scale typically relies on sampling a fraction of calls, so edge cases and failures go unseen. Existing dashboards surface metrics like latency or sentiment but don't explain the underlying cause of a failure, so every fix becomes a guess rather than a diagnosis. As a result, teams cannot reproduce production failures or learn systematically from them, which the company frames as where customer trust breaks down.

SuperBryn generates test scenarios from a company's own data sources before launch, then runs evaluations on actual production traffic rather than only synthetic tests, covering every call instead of a sample. It monitors calls in real time across the full voice pipeline (speech-to-text, LLM, and text-to-speech), tracking latency, accuracy, sentiment, and task completion with sub-second alerting. Its root cause analysis traces failures through that pipeline to identify whether a breakdown came from transcription, reasoning, or speech synthesis. The platform integrates with major voice AI providers including Vapi, Retell, Bland.ai, and custom implementations through a single SDK, and it offers compliance features such as audit trails and compliance reporting aimed at regulated industries like healthcare, financial services, and insurance. A self-learning loop, described as coming soon, would use an Observer Agent to feed insights to an Improver Agent that automatically updates the main voice agent without manual prompt tuning.