Voice AI Reliability Platform

Your voice agents work in the demo.
They break in production.

Name: SuperBryn
Author: SuperBryn

SuperBryn is the evaluation and observability platform that helps you understand why your voice agents fail—and how to fix them.

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The Production Gap

Your voice agents pass every test. Then they fail real customers.

Demos work perfectly. Production breaks silently. The gap between testing and reality is where trust dies.

The Problem

You're flying blind in production.

Demos work. Real calls don't. You can't reproduce the failures.
Monitoring millions of calls means sampling—and missing edge cases.
Dashboards show metrics. They don't explain what went wrong.
Every fix is a guess. You're not learning from production.

Our Solution

Evals, observability, and self-learning for voice AI.

Generate test scenarios from your data—before you go live
Evaluate every production call, not just a sample
Monitor and trace failures across STT → LLM → TTS
Self-learning loop that improves agents automatically (coming soon)

Evals & Observability

Everything you need to trust voice AI at scale.

Pre-deployment evals, production observability, and a roadmap to self-learning—built by researchers with 14+ years in speech recognition.

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Comprehensive Evals

Generate test scenarios from your company's data sources before going live. Then run evals on production traffic to measure what actually happens with real customers.

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Real-Time Observability

Monitor every call across STT, LLM, and TTS. Track latency, accuracy, sentiment, and task completion with sub-second alerting.

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Root Cause Analysis

Trace failures through the entire voice pipeline. Know whether it was the transcription, the reasoning, or the synthesis that broke.

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Enterprise Compliance

Purpose-built for regulated industries. Healthcare, financial services, insurance. Full audit trails and compliance reporting.

Coming Soon

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Self-Learning Loop

Our Observer Agent will feed insights to an Improver Agent that automatically updates your Main Agent. Continuous improvement without human intervention.

Coming Soon

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Multi-Model Routing

Intelligently route between STT, LLM, and TTS providers based on context, cost, and performance. One integration, infinite flexibility.

Research-Backed

Built by researchers. Not just engineers.

SuperBryn's approach is grounded in over a decade of academic research in speech recognition, acoustic modeling, and voice AI.

PhD, IIT MadrasPost-Doc, King's College London

Dr. Neethu Mariam Joy

Co-Founder & CTO

“Voice agents fail in production because they weren't built to learn from real-world conditions. We're changing that.”

14+ years of research in speech recognition, noise-robust acoustic modeling, and assistive voice technology. PhD from IIT Madras (2011–2018), followed by postdoctoral research at King's College London. Published extensively in IEEE and INTERSPEECH on speaker normalization, low-resource language modeling, and speech recognition for impaired speakers.

Selected Publications

FMLLR Speaker Normalization With i-Vector
IEEE/ACM Trans. Audio, Speech & Language Processing, 2018
Improving Acoustic Models in TORGO Dysarthric Speech Database
IEEE Trans. Neural Systems and Rehabilitation Engineering, 2018
DNNs for Unsupervised Extraction of Pseudo Speaker-Normalized Features
Speech Communication, 2017
On Improving Acoustic Models for Dysarthric Speech
INTERSPEECH, 2017

View all publications →

Why SuperBryn

Others build dashboards.
We build understanding.

The first voice AI platform built by speech recognition researchers—for the problems they've spent their careers solving.

Research-First Approach

Our team brings 14+ years of speech AI research. We know why voice agents fail—because we've spent our careers studying it.

Production-First Philosophy

We evaluate on live traffic, not synthetic scenarios. Because a test that doesn't reflect reality is worse than no test at all.

Full-Stack Observability

Trace issues across the entire voice pipeline—STT, LLM, TTS. Know exactly where and why your agent broke.

Built for Regulated Industries

Healthcare, financial services, insurance. We understand compliance requirements because our customers depend on them.

Quick Questions

Frequently asked

How is this different from existing eval platforms?

Most platforms focus on pre-deployment testing. We focus on production—where the real failures happen. Our research background means we understand why voice AI breaks in ways other tools miss.

What voice AI platforms do you integrate with?

We work with all major voice AI providers including Vapi, Retell, Bland.ai, and custom implementations. One SDK, full visibility across your stack.

Is SuperBryn compliant for regulated industries?

Yes. We're built specifically for healthcare, financial services, and insurance. Full audit trails, data residency options, and compliance reporting come standard.

What's coming with the self-learning capability?

Our roadmap includes an Observer Agent that monitors production calls and an Improver Agent that suggests and implements fixes automatically. The goal: agents that get better without manual prompt tuning.

How is your team different from other startups?

Our CTO has a PhD from IIT Madras and postdoctoral research from King's College London—14+ years studying exactly the problems we're solving. This isn't our first rodeo with speech recognition.

Do you support real-time monitoring?

Yes. Sub-second alerting on production calls. You'll know when something breaks before your customers complain about it.

Your voice agents work in the demo.They break in production.