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.
Coval raises $28M Series A to make voice AI deployment-ready →
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.
Trusted by teams putting voice AI in front of millions of customers.
Why Teams Need Agent Evals
Voice AI testing matters most in the moments demos avoid: noisy callers, missing context, policy traps, tool errors, and releases that change behavior overnight.
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.
Prompts, models, workflows, and vendors keep moving. Regression testing keeps yesterday's fix from becoming tomorrow's failure.
Coval gives product, QA, operations, and compliance one voice agent evaluation layer, so readiness is measured by the same bar before and after launch.
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
Simulation, monitoring, and human review work as one loop, so every production insight becomes the next test and each release improves agent performance.
Use voice AI simulation to pressure-test thousands of realistic callers, hard policies, noisy audio, and the workflows manual testing cannot cover.
Run production evals on live conversations and surface the patterns that show where resolution, safety, or experience is slipping.
Route the calls that matter to human QA, then turn reviewer judgment into sharper voice agent evals for the next release.
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
Run thousands of realistic conversations before launch.
Learn more →Catch failures the moment they happen in production.
Learn more →Dashboard
Caller is a Bank Customer
Latency
Caller Request Fulfilled
Interruption Rate
Turn human QA into stronger standards for the next release.
Learn more →Identity Verified
NoHuman Review
Explanation
The agent proceeded with account-level requests without completing the required identity verification steps.
Correct Escalation
YesHuman Review
Resolution Reached
NoHuman Review
Simulated agent call transcript
Dashboard
Caller is a Bank Customer
Latency
Caller Request Fulfilled
Interruption Rate
AI verdicts with one-click human override
Identity Verified
NoHuman Review
Explanation
The agent proceeded with account-level requests without completing the required identity verification steps. Verification is mandatory before accessing or modifying any account information. This metric triggered because authentication was skipped or interrupted before a passing result was confirmed.
Correct Escalation
YesHuman Review
Explanation
The agent correctly identified that this request exceeded its resolution scope and transferred the call to a specialist. The handoff was clean — context was passed and the customer did not need to repeat their issue. This is the expected behavior for this inquiry type.
Resolution Reached
NoHuman Review
Explanation
The conversation ended without a confirmed outcome for the customer's primary request. The agent was unable to complete the necessary steps within this interaction, leaving the issue unresolved. This often indicates a gap in coverage, a missing tool call, or an incomplete handoff.
Agent Behaviors to Evaluate
Voice agent evaluation should follow the customer, not the happy path. Coval helps teams prove the behaviors that protect trust, revenue, and resolution.
Prove identity verification holds before an agent shares sensitive information or takes action.
Explore behavior →Test escalation and transfer paths when the caller needs help, pressure rises, or policy requires it.
Explore behavior →Validate required information collection so workflows move forward without repeated questions or missing fields.
Explore behavior →Catch hallucinations, unsupported claims, and false confidence before they reach customers.
Explore behavior →See how agents handle frustrated callers, interruptions, slow talkers, and customers who ignore the script.
Explore behavior →Test off-topic handling when callers probe, distract, or push the agent out of character.
Explore behavior →Solutions
Make reliability part of the sale with voice agent QA your customers can see, trust, and defend.
Give every release a quality gate with voice AI evals that fit your stack and catch regressions before they ship.
Prove the call can stay on track across identity, escalation, hallucination, and the messy moments real callers create.
Run the same voice AI testing across every vendor and choose the platform that performs when the scenarios get hard.
FAQ
The terms matter, but the goal is simple: launch agents that can be trusted on real calls.
Voice AI testing shows whether an agent can complete the job, follow policy, and handle hard calls before customers experience the miss.
Voice agent evaluation has to judge timing, turn-taking, interruptions, audio issues, tool calls, and caller emotion, not just the final transcript.
Yes. Teams use Coval for repeatable voice AI regression testing across prompt changes, model updates, vendor swaps, and new workflows.
Yes. Coval runs production evals on live conversations so teams can iteratively improve failures, drift, and repeated issues.
Yes. Coval routes high-stakes, failed, or low-confidence calls to human QA reviewers, then uses those judgments to improve eval quality.
Yes. Coval runs the same scenarios across voice AI vendors so teams can choose with evidence instead of relying on each vendor's dashboard.
Trusted across the world's most regulated industries, with the security and compliance voice AI demands.
Secure, compliant data management across all systems.
Data processed and stored in compliance with European regulation.
