#7d20“My account number is 4417”#7d14“I want a refund for that order”#7d0b“The card ending 8823 was charged”#7cf9“Cancel it and refund me”
Catch Audio Issues in Production.
Without the Manual Listening.
Test, evaluate and monitor your voice agents in one place. Catch the broken call before your customers do.
TRUSTED BY 500+ LEADING AI COMPANIES
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HOW IT WORKS
Hear every problem without hearing every call.
- 01
Instrument your voice stack.
LiveKit, Pipecat, ElevenLabs, Vapi and more. Every call lands as a trace, audio included.
- 02
Classify every call by ear.
Audio classifiers flag interruptions, silences, glitches and mistranscribed words.
- 03
See what keeps breaking.
Flagged calls roll up into ranked failure modes, with the evidence and the code to fix.
- 04
Route calls to the right experts.
Workflows route flagged calls to domain experts, who listen and confirm each label.
Every Call, Traced
Production calls stream in from your voice stack with their audio attached.
Every Call, Classified
Audio issues, transcription accuracy and glitches, flagged from the audio.
Failure Modes, Ranked
Recurring problems, the evidence behind them, and what to fix first.
The Right Reviewer for Every Call
Each issue routes to its own annotation queue for human review.
THE PLATFORMTurn live traces into simulated calls.
Turn live traces into simulated calls.
Complete the AI quality loop for voice agents.
INTEGRATIONSStay in your voice stack.
Stay in your voice stack.
We'll meet you there.
Voice frameworks, hosted runtimes and every way a call connects, feeding straight into traces and evals.
SECURITY
Designed to handle the most sensitive customer calls.
Self-host or use Confident AI's cloud, keep call audio in your region, and control who can listen to each recording.
Self-host or use Confident AI's cloud.
On-prem · AWS · Azure · GCP
Your data. Your region.
Multi-region residency · HIPAA · GDPR
Granular permissions.
RBAC · Project isolation · Data masking
99.9% uptime SLA.
Enterprise-grade availability
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TESTIMONIALS
Trusted by teams shipping voice AI to real customers.
FinomBefore Confident AI, a single improvement cycle took 10 days — I'd create a task, assign it to an engineer, wait for availability, and go back and forth. Now the same cycle takes three hours, and our product managers can run it themselves.
Igor Kolodkin,Head of AI Quality, Finom
Confident AI saves us 480+ hours of manual AI evaluation every month — and gives us the data to defend every quality decision in front of engineering, product, and leadership.
Anoop Mahajan,Director of QA, Amdocs
Confident AI gave our team one place to turn production failures into datasets, align metrics, and keep regressions out of releases without waiting on custom engineering work.
SD
Senior Director of Engineering,Fortune 500 medical device company
HumachWe run a lot of large-scale, multi-turn simulations, and Confident AI made it far easier to design scenarios and execute those tests without piecing together external tools.
Sean Austin,Chief AI Officer, Humach
Thanks to Confident AI, we were able to move to a fine-tuned model and cut our LLM costs by 80%. This opens up whole new use cases now to generate better output with more targeted LLM calls.
John Lemmon,AI Lead, Supernormal
FAQ
Have a Question?
Checkout our FAQs below, or talk to a human. They won't hallucinate.
LiveKit, Pipecat, ElevenLabs, Vapi and more send production calls to Confident AI. For testing, Confident AI reaches your agent over a phone number, SIP address or WebRTC.
Interruptions, awkward silences and tone problems. They listen to the audio itself, and a review step lets a human hear the clip and confirm the label before it sticks.
No. Issues are marked on the call's timeline, so you jump to the exact second and replay only the part that went wrong.
Whoever knows the domain. Flagged calls route into annotation queues, so billing questions go to billing specialists and tone problems go to CX leads. Reviewers listen to the clip and confirm or relabel it.
Yes. Confident AI places real calls to your agent, speaks each simulated user turn out loud, and records how long your agent took to answer and where either side talked over the other.
Personas set how often the caller interrupts, what it does when both sides talk at once, and the background noise played under its speech. Real users are messy, so your simulations can be too.