Voice-native simulation
Run thousands of realistic voice conversations before launch to test how agents handle interruptions, hesitations, language switching, noisy environments, and tool calls.
Coval
Freemium
AI evaluation platform for voice and chat agents—simulate, monitor, and review with human feedback.
What is Coval?
Coval is an AI evaluation platform for voice and chat agents. It is designed to help teams simulate agent behavior before launch, observe real calls in production, and review outputs with human feedback in one workflow.
The product focuses on the full lifecycle of agent quality: stress-testing realistic scenarios, monitoring production calls against custom metrics, and using review queues to refine automated verdicts. The site positions Coval as useful for agent platforms, in-house teams, and vendor bakeoffs.
What can Coval do?
Production observability
Score production calls in real time, filter by dimension, and use alerts to catch regressions as soon as they appear.
Human-in-the-loop review
Route failures and low-confidence calls to human reviewers, then use their feedback to improve the AI judge and future evaluations.
Unified voice and chat coverage
Evaluate voice and chat agents in one platform, which helps teams keep one evaluation layer instead of maintaining separate tooling for each modality.
Developer surfaces
Use the API, CLI, MCP server, and skills framework to run evaluation from developer workflows and CI/CD pipelines.
OpenTelemetry-native integrations
Connect to existing observability tools such as Langfuse, LangSmith, Arize, and Datadog, while retaining trace links into production calls.
Use Cases
“Pre-launch simulation”
Test voice agents against realistic caller behavior before a release, including interruptions, noisy backgrounds, and policy-critical paths such as identity verification or escalation.
“Production monitoring”
Track live conversations in production, score them against defined metrics, and alert the right team when regressions or anomalies appear.
“Human quality review”
Have QA or operations staff review failures, edge cases, and low-confidence calls, then use their feedback to correct AI verdicts and improve future scoring.
“Vendor bakeoffs”
Compare multiple vendors or internal agent stacks using the same scenarios and scoring logic so teams can choose based on observed performance rather than assumptions.
“Developer-led testing”
Embed evaluation into engineering workflows through the CLI, API, or CI/CD so every change can be stress-tested before deployment.
Frequently Asked Questions
Does Coval support voice agents only?
Coval supports both voice and chat agents on a single platform, so teams can evaluate either modality without maintaining separate tools.
How do teams use Coval in their workflow?
The pricing page includes an API, CLI, MCP server, and skills framework, and the product pages say teams can run simulations from the CLI and integrate with existing observability tools.
Can teams use Coval before and after launch?
The source says teams can start with self-serve or book a demo for an enterprise pilot, and the simulation flow is designed to run before launch while observability and review cover production usage.
Can Coval compare agents from different vendors?
Coval is positioned as vendor-agnostic, and the product pages say it can grade agents identically across different platforms so teams can compare results on the same scenarios.
What does Coval offer for larger or regulated teams?
The pricing page shows Starter, Growth, and Enterprise plans, with enterprise features such as SAML SSO, SCIM, private/VPC deployment, data residency, and white-label reporting available only on the Enterprise plan.
Quick Facts
- Category
- AI evaluation platform
- Primary focus
- Voice AI, with chat support
- Typical workflows
- Simulate, observe, and review
- Developer surfaces
- API, CLI, MCP server, skills
- Pricing
- Starter, Growth, and Enterprise plans
- Source domain
- coval.dev
Coval Traffic Analysis
Traffic data is for reference only.
- Domain Rating
- 54
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