Voice AI systems are becoming integral to customer service and virtual assistant applications, but ensuring these voice agents perform reliably and meet quality standards is a key challenge.

A number of innovative platforms have emerged to help teams evaluate and improve voice AI performance. This article provides an overview of five notable solutions –  Coval, Roark, Cekura, Hamming and Leaping AI – each offering unique strengths for testing, quality assurance (QA), and performance monitoring of voice AI agents.

Decision-makers in the voice AI industry can use these insights to compare options as they seek robust evaluation and QA tools.

Coval: End-to-End Evaluation & Observability for Mission-Critical Voice AI

Coval brings simulation methodologies from self-driving cars to voice and chat AI. Founded by a former Waymo engineer, Coval’s platform is built from the ground up to treat conversational AI like autonomous systems: test extensively in simulation before trusting live deployment. Backed by Y Combinator, MaC and General Catalyst, Coval helps enterprise teams confidently test, monitor, and ship mission-critical voice agents.

  • Simulation-First Development, Inspired by Self-Driving: Coval’s core approach is grounded in large-scale scenario simulation. Teams can define voice workflows and run thousands of simulations mirroring diverse user behavior, accents, and edge cases; to validate agents before they go live. This methodology draws from the safety culture of self-driving, where simulation is essential to catch failure cases early and often.
  • Indepth Production Observability: After deployment, Coval continuously monitors real conversations to flag failed intents, behavior drift, latency issues, or policy violations. Teams can define QA, compliance, and business-specific KPIs—like escalation rates or refund eligibility coverage - to ensure ongoing performance aligns with internal standards or regulatory expectations.
  • Unified Platform for Multi-Agent and Multi-Tenant QA: Coval supports managing multiple agents across different environments, deployments, and customer use cases—all from a single platform. Simulation-based testing and live monitoring are tightly integrated, enabling CI-driven regression checks alongside customer-specific production oversight. This makes Coval particularly well-suited for teams running multiple workflows, across geographies or enterprise clients, with high QA and reliability demands.
  • Manual QA Integration: Coval allows you to leave feedback on all simulations and live calls and then resimulate calls from those transcripts, iterate on your metrics to align with human judgement, and improve your agent against this feedback in tight iteration cycles.

Coval’s edge lies in taking all of the best tools from self driving to create the state of the art unified reliability platform. By combining simulation, CI-integrated testing, and production monitoring under one roof, Coval helps companies ship voice (and chat) agents they can trust.

Roark: Observability & Real-Call Testing for Voice AI

Roark (YC W25) turns real customer interactions into automated test suites for comprehensive voice AI agent testing and monitoring. Think Datadog for Voice AI - a single platform to validate every conversation, from development through live production.

End-to-End Voice Agent Testing

•           Production-Based Tests: Instantly transform actual user calls into automated, reusable test cases that preserve sentiment, tone, and timing.

•           CI/CD & Regression: Automatically trigger tests on each deployment, detecting regressions and performance issues before they reach customers.

•           Edge Cases & Variations: Effortlessly test conversational variations across languages, accents, background noise, and network conditions, alongside AI-driven discovery of rare edge cases.

•           Performance & Load Testing: Benchmark latency and infrastructure resilience by simulating realistic peak loads.

Evaluation & Live Monitoring

•           Customizable Evaluations: Build modular test pipelines to enforce latency targets, security checks, compliance standards, and key business flows like identity verification.

•           Real-Time Analytics: Monitor conversational performance with intuitive dashboards showing funnel analytics, conversion rates, and user sentiment - capturing pauses, interruptions, and emotional cues.

•           Proactive Alerts: Receive immediate notifications via Slack, PagerDuty, or existing SRE tools when issues or compliance risks arise.

Roark unifies automated test-case creation, comprehensive scenario testing, and continuous real-time observability, empowering teams to confidently deliver robust, high-quality voice AI agents.

Cekura: End-to-End Testing and Monitoring for Voice Agents

Cekura (formerly Vocera) offers a comprehensive platform for testing and monitoring AI voice agents. Its focus is on helping companies ship reliable voice and chat AI agents faster by providing tools for automated test generation, thorough evaluation metrics, and ongoing observability. Cekura’s solution works across the whole lifecycle – from pre-launch testing to post-deployment analytics.

  • Automated Scenario Generation: Cekura can automatically create diverse test cases from a given agent description or dialogue flow. By simulating varied user inputs, personas, and even using real audio with accents or background noise, it ensures comprehensive coverage of possible interactions. This saves QA teams from writing endless test scripts and catches edge cases early.
  • Custom Evaluation Metrics: Teams can track both default and custom-defined metrics for their voice AI’s performance. For example, Cekura can check if an agent follows instructions properly, uses tools or APIs when required, and measure conversational attributes like interruption rates or latency in responses ((learn how to ensure the reliability of a voice AI agent). These tailored metrics let businesses focus on the criteria that define success for their specific use case (compliance, response time, accuracy, etc.).
  • Actionable Insights & Recommendations: Beyond raw metrics, Cekura provides prompt-level recommendations and insights to improve those metrics. If the AI frequently misses a step or takes too long to respond, the platform might suggest prompt tweaks or logic changes to fix the issue. This guidance accelerates the refinement of the voice agent.
  • Production Monitoring & Alerts: Once the voice agent is live, Cekura’s observability dashboard tracks real call performance in terms of customer sentiment, call success rates, drop-off points, and more. It can automatically alert teams to critical issues like spikes in latency or instances where the AI fails to follow a script. This continuous monitoring ensures that reliability remains high as the voice agent scales up in production.

Cekura’s integrated approach – combining robust pre-launch testing with ongoing post-launch monitoring – is a key strength. It equips teams with both the preventive tools to avoid failures and the analytical tools to continually polish the voice AI experience.

Hamming: Automated Stress-Testing and Analytics for Voice AI

Hamming AI is another Y Combinator alum that focuses on making voice agents more reliable through automated testing at scale. Hamming provides an all-in-one platform for voice AI evaluation, call analytics, and governance to support development through production. A hallmark of Hamming’s solution is its ability to simulate massive call volumes to stress-test voice AI systems under real-world conditions.

  • High-Scale Automated Testing: Hamming’s platform uses AI-driven “voice characters” to place thousands of concurrent test calls to a voice agent, uncovering issues that might only appear under heavy load or with varied inputs. One example cited is a drive-through ordering AI that used Hamming to simulate thousands of simultaneous phone calls and achieved 99.99% order accuracy after rigorous testing. This level of scale in automated QA ensures voicebots can handle peak traffic and complex scenarios without breaking.
  • Comprehensive QA & Analytics: Alongside testing, Hamming provides rich analytics on call outcomes and agent behavior. It monitors key performance indicators (like completion rates, error frequencies, response latency) across all test calls and real calls. The platform also generates trust and safety reports, which likely means it checks conversations for compliance issues or inappropriate responses to ensure the AI aligns with company policies and ethical standards.
  • Prompt Management & Versioning: Recognizing that conversation prompts and scripts evolve, Hamming includes tools to manage and version prompts centrally. Teams can adjust an agent’s prompt for different customers or use cases and keep those changes synchronized with the testing framework. Every time a prompt or underlying model changes, Hamming’s automated suite can re-test the agent to validate that the update hasn’t introduced regressions, given how even small prompt changes can lead to large variations in AI output.

Hamming’s key strength is rigorous automation and scalability. It reduces the manual effort of testing voice AI by replacing it with AI-driven callers and robust analysis. For organizations where reliability at scale is paramount, Hamming offers a way to rapidly iterate on voice agents while maintaining confidence through data-driven testing.

Leaping AI: Self-Improving Voice Agents with Built-in QA

👉 Leaping AI takes a slightly different approach: it not only provides voice AI agents for enterprises, but also integrates an internal evaluation loop that helps those agents improve themselves over time. Billed as “the only self-improving voice AI” (YC W25), Leaping AI’s platform delivers the most human-like voice agents that can automate a large portion of calls and then automatically analyze their own performance to get better with each interaction.

  • Self-Improving Voice Agents: A standout feature of Leaping AI is that the agents perform post-call self-evaluations. After each conversation, the AI agent autonomously reviews the call, identifies what went well and what didn’t, and even adjusts its prompts or behavior for next time. The system runs A/B tests on different prompt variations and gradually fine-tunes the agent’s performance without human intervention, leading to continuous day-to-day improvement in metrics like successful call resolution rate. This closed feedback loop addresses issues proactively and reduces the need for manual retraining.
  • Automated QA and Feedback: Leaping AI’s platform includes an internal QA feature where, at the press of a button, an AI can evaluate call transcripts far faster than a human reviewer. It provides an automated assessment of each voice AI call for quality and reliability, so that companies no longer have to manually listen to calls at scale. For example, users can instantly run QA on all calls handled by the AI agent and get a report on performance – an approach that one client called a “game changer” for monitoring voice AI quality. This automated QA highlights where the AI followed the script or policy and where it deviated, giving actionable feedback for improvement.
  • Customizable Evaluation Metrics: The evaluation system can be tailored to specific business goals. Teams can configure criteria such as faithfulness (ensuring the AI’s answers stay accurate and true to the knowledge base or instructions) and response quality (naturalness, professionalism, customer satisfaction), or even define prompt-based scoring rubrics unique to their use case. In practice, this means Leaping AI can use AI models to score an agent’s responses on any dimension the user cares about. Developers on the platform report using these evals to test for things like reliability and adherence to instructions automatically.
  • Performance Monitoring & Iteration: All calls are recorded and analyzed within Leaping AI’s dashboard. Clients can track metrics over time – for instance, one customer saw measurable improvements and fewer human hand-offs after deploying Leaping AI with its self-improvement and QA features. The platform flags regressions and spots trends, allowing decision-makers to monitor how the AI agents are trending in performance and quickly spot any dip in quality. Because the agents are continuously learning, many issues get resolved by the AI itself, and the oversight can focus on high-level tuning and strategic use cases.

Leaping AI’s strength is in combining automated voice agents with an intelligent feedback system.

You want to modernize and automate your customer service? For businesses looking not just for a testing tool but for a voice AI solution maintains and upgrades its own quality, Leaping AI offers a compelling package of hands-off improvement and customizable QA.

Conclusion: Key Takeaways

All five platforms – Roark, Cekura, Hamming, Coval, and Leaping AI – are addressing the challenge of voice AI quality and reliability, each through a distinct lens. Roark emphasizes real-world call replay and sentiment monitoring to improve production performance. Cekura offers a full QA pipeline from automated test generation to live analytics. Hamming focuses on stress-testing voice agents at scale with compliance and safety checks. Coval unifies simulation-based regression testing, and monitoring in one platform. Leaping AI integrates self-improvement and automated QA directly into the agent’s feedback loop.

For decision-makers evaluating voice AI performance and QA solutions, the good news is that these platforms can significantly reduce the manual effort of testing and increase confidence in voice agents. The choice may come down to the specific needs of your team: whether it’s large-scale automated testing (Coval, Hamming), deep insight from real calls (Roark, Cekura), or an agent that improves itself over time (Leaping AI).

By leveraging any of these modern tools, companies can accelerate development cycles, ensure higher conversational quality, and ultimately deliver better voice AI experiences to their users – all without an overly burdensome QA process. Each platform highlighted here positively contributes to making voice AI deployments more robust and trustworthy in enterprise settings.