What is Coval?
Coval is a comprehensive voice AI testing and evaluation platform designed to help teams prove their voice agents are production-ready. It provides a unified layer for testing, monitoring, and improving agent performance across the full lifecycle—from pre-launch simulation to production evals and human QA. The platform addresses the critical gaps that manual testing misses, such as noisy callers, missing context, policy traps, tool errors, and regressions caused by frequent updates to prompts, models, or workflows.
Key features include voice AI simulation that pressure-tests agents with thousands of realistic callers, including edge cases like accents, interruptions, and policy violations. Production evals run on live conversations to surface patterns in resolution, safety, or user experience. Human QA routing allows teams to review critical calls and turn reviewer insights into sharper automated evals. The continuous quality loop ensures that every production insight feeds back into the next test, driving iterative improvement.
Benefits include a 217% improvement in agent accuracy within 7 days, over 3.1 million evaluation metrics run weekly, and the ability to get the first simulation running in under 15 minutes via CLI. Use cases span product, QA, operations, and compliance teams that need to validate voice AI before deployment, catch failures in production, and compare vendors. Technical details include support for realistic caller scenarios, latency tracking, interruption rate measurement, and fulfillment rate analysis. Coval integrates with existing workflows to provide a single source of truth for voice agent readiness, enabling data-driven decisions and scalable quality assurance.
Use Cases
Key Features
voice AI simulation production evals human QA routing continuous quality loop edge case testing policy violation detection regression detection tool error analysis accuracy improvement iterative testingWho is it for?
voice AI teams, QA engineers, product managers, AI developers, customer experience teams, compliance teams
