Glossary · AI & Agents
Coval
Overview
What Coval is
Coval is a testing and evaluation platform built specifically for AI voice and chat agents. Rather than manually dialing an agent to check its behavior, teams use Coval to simulate large volumes of conversations, score the results against defined criteria, and track quality from pre-launch through production.
The company was founded in 2024 by Brooke Hopkins, who previously worked on evaluation infrastructure at Waymo, and raised a $28 million Series A led by Norwest in 2026. Coval positions itself around reliability and safety for autonomous voice agents, borrowing methodology from self-driving-car testing.
How it works
Coval generates simulated callers that interrupt, hesitate, switch languages, and call from noisy environments, then runs them against your agent to surface failures before real users do. Results are scored with configurable evaluators, and production calls can be monitored continuously with alerting on regressions.
A human review layer routes conversations to QA reviewers whose judgments are converted into reusable evaluation standards, so subjective feedback becomes repeatable automated checks over time.
Where it fits in a voice-AI stack
Coval sits at the QA and observability layer, above the agent framework and telephony. It integrates with orchestration tools like Pipecat and observability platforms like Langfuse, so it complements rather than replaces the stack you build agents on.
It is one of several voice-agent testing options; Hamming AI is a close alternative, and some teams instead assemble evaluation using general LLM-eval libraries. Choice usually depends on whether you need built-in call simulation, human review workflows, and CI/CD gating.
Where we use it
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How our team puts Coval to work in real projects.
FAQ
Coval — common questions
What does Coval do?
Coval simulates and evaluates AI voice and chat agents. It runs many realistic test conversations before launch, monitors live calls in production, and turns human reviewer feedback into reusable automated evaluations.
Is Coval the same as a voice agent platform like Vapi or Retell?
No. Vapi and Retell are platforms for building and running voice agents, while Coval is a testing and evaluation layer that sits on top of whatever framework or platform you use to build the agent.
How is Coval different from Hamming AI?
Both test voice agents, but they emphasize different workflows. Coval highlights simulation plus a human-review layer that produces reusable evals, while Hamming AI emphasizes auto-generating test scenarios from your prompt and high-volume concurrent call testing. Evaluate both against your needs.
Need Coval done right?
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