Launch risk hides in the long tail.
A handful of manual QA calls will not reveal the accents, interruptions, policy traps, and edge cases that decide whether a voice agent holds up.
Scale voice AI agents with confidence.
Coval gives teams the voice AI testing, evals, and QA loop to prove what works before launch, catch failures in production, and keep improving agent performance.
Find signal
in the noise.
Trusted by teams putting voice AI in front of millions of customers.
Why Teams Need Agent Evals
The riskiest call is the one you did not test.
Voice AI testing matters most in the moments demos avoid: noisy callers, missing context, policy traps, tool errors, and releases that change behavior overnight.
Quality drifts after every change.
Prompts, models, workflows, and vendors keep moving. Regression testing keeps yesterday's fix from becoming tomorrow's failure.
Teams need proof they trust.
Coval gives product, QA, operations, and compliance one voice agent evaluation layer, so readiness is measured by the same bar before and after launch.
Full visibility into quality, consistency, and compliance.
Bring voice AI testing, production evals, QA review, and vendor comparisons into one view, so teams can move from anecdotes to decisions.
Scale with Confidence
Agent insights from the first prompt to the millionth call.
Recursive loops for agent improvement.
Simulation, monitoring, and human review work as one loop, so every production insight becomes the next test and each release improves agent performance.
Ship voice agents you can prove are ready.
Use voice AI simulation to pressure-test thousands of realistic callers, hard policies, noisy audio, and the workflows manual testing cannot cover.
Catch quality drift before customers feel it.
Run production evals on live conversations and surface the patterns that show where resolution, safety, or experience is slipping.
Build trust by scaling human judgement.
Route the calls that matter to human QA, then turn reviewer judgment into sharper voice agent evals for the next release.
The results speak for themselves.
Real numbers from real deployments.
217%
Improvement on agent accuracy in 7 days
3.1M
Evaluation metrics run weekly
<15m
To first simulation via CLI
The Continuous Quality Loop
Voice AI evaluation, across every lifecycle stage.
Simulated agent call transcript
AI verdicts with one-click human override
Agent Behaviors to Evaluate
The moments that make or break the call.
Voice agent evaluation should follow the customer, not the happy path. Coval helps teams prove the behaviors that protect trust, revenue, and resolution.
Protect the account.
Prove identity verification holds before an agent shares sensitive information or takes action.
Explore behavior →Hand off without dead ends.
Test escalation and transfer paths when the caller needs help, pressure rises, or policy requires it.
Explore behavior →Get the right details once.
Validate required information collection so workflows move forward without repeated questions or missing fields.
Explore behavior →Keep answers grounded.
Catch hallucinations, unsupported claims, and false confidence before they reach customers.
Explore behavior →Stay steady under pressure.
See how agents handle frustrated callers, interruptions, slow talkers, and customers who ignore the script.
Explore behavior →Keep the call on course.
Test off-topic handling when callers probe, distract, or push the agent out of character.
Explore behavior →Solutions
Built for the teams accountable when agents go live.
For Agent Platforms
Make reliability part of the sale with voice agent QA your customers can see, trust, and defend.
For In-House Teams
Give every release a quality gate with voice AI evals that fit your stack and catch regressions before they ship.
For Agent Behaviors
Prove the call can stay on track across identity, escalation, hallucination, and the messy moments real callers create.
For Vendor Bakeoffs
Run the same voice AI testing across every vendor and choose the platform that performs when the scenarios get hard.
FAQ
Clear answers for high-stakes voice AI.
The terms matter, but the goal is simple: launch agents that can be trusted on real calls.
What is voice AI testing?
Voice AI testing shows whether an agent can complete the job, follow policy, and handle hard calls before customers experience the miss.
How is voice agent evaluation different from chatbot evaluation?
Voice agent evaluation has to judge timing, turn-taking, interruptions, audio issues, tool calls, and caller emotion, not just the final transcript.
Can Coval run regression tests before launch?
Yes. Teams use Coval for repeatable voice AI regression testing across prompt changes, model updates, vendor swaps, and new workflows.
Can Coval evaluate production calls?
Yes. Coval runs production evals on live conversations so teams can iteratively improve failures, drift, and repeated issues.
Does Coval support human QA review?
Yes. Coval routes high-stakes, failed, or low-confidence calls to human QA reviewers, then uses those judgments to improve eval quality.
Can Coval compare multiple voice AI vendors?
Yes. Coval runs the same scenarios across voice AI vendors so teams can choose with evidence instead of relying on each vendor's dashboard.
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