Relyable

Overview

The rapidly evolving landscape of AI voice agents presents significant challenges in ensuring reliable performance and quality assurance before deployment. Relyable addresses these critical needs by providing a comprehensive testing and monitoring platform specifically designed for AI voice agents. Founded by Brendan Jowett in 2025 following extensive experience at Inflate AI, where the team spent 80% of development time on testing rather than building, Relyable emerged from the recognition that robust testing infrastructure is essential for production-ready voice AI deployments. The platform enables development teams to simulate thousands of AI-generated conversations, create custom personas and edge cases, and monitor live call performance with automated alerting systems.

Key Features

Relyable provides a comprehensive suite of testing and monitoring capabilities specifically engineered for voice AI agent validation:

• Large-scale conversation simulation: Generate and execute thousands of AI-driven test conversations simultaneously, enabling comprehensive stress testing and edge case discovery before production deployment.
• Custom persona and scenario generation: Create detailed user personas with specific characteristics like age, emotional state, or behavioral patterns, combined with targeted conversation scenarios to test agent performance across diverse interaction types.
• Native platform integrations: Seamlessly integrate with leading voice AI platforms including Vapi and Retell AI, allowing teams to import existing agents within minutes and begin testing without extensive setup processes.
• Real-time call monitoring and evaluation: Monitor live conversations with intelligent analytics that automatically detect performance issues, conversation failures, and quality degradation, providing immediate alerts via email when critical thresholds are exceeded.
• Automated scoring and metrics: Apply custom evaluation criteria to measure agent performance across key metrics such as task completion rates, response accuracy, conversation flow adherence, and user satisfaction indicators.
• Regression testing capabilities: Maintain test suite continuity as agents evolve, automatically running established test cases against updated agent versions to catch performance degradations before deployment.

How It Works

Relyable’s workflow integrates directly into existing voice AI development processes through a streamlined approach designed for both technical and business users. Teams begin by connecting their voice agents through native integrations with Vapi or Retell AI platforms. The system then enables users to define test scenarios by creating custom personas that reflect their target audience demographics and behavioral patterns. These personas are paired with specific conversation scenarios generated from user-provided prompts or business requirements. Once configured, Relyable’s automated testing engine executes hundreds or thousands of simulated conversations, applying custom evaluation metrics to assess agent performance. The platform provides comprehensive reporting and analytics, enabling teams to identify failure patterns, optimize conversation flows, and validate improvements before production release.

Use Cases

Relyable serves diverse applications across industries where voice AI reliability is paramount:

• Customer service optimization: Validate customer support agents against diverse query types, emotional states, and complex problem-solving scenarios to ensure consistent, helpful responses across all customer interactions.
• Sales performance validation: Test AI sales agents with various prospect personas, objection handling scenarios, and qualification processes to maximize conversion rates and ensure professional representation of the brand.
• Healthcare and compliance-critical applications: Rigorously test voice agents handling sensitive information, ensuring proper data collection, privacy compliance, and appropriate escalation procedures for complex medical or legal scenarios.
• Multi-language and accent testing: Validate agent performance across diverse linguistic variations, regional accents, and cultural communication styles to ensure inclusive user experiences for global audiences.
• Load testing and scalability validation: Simulate high-volume call scenarios to identify infrastructure bottlenecks, response time degradation, and system reliability under peak usage conditions.

Pros \& Cons

Advantages

• Significantly accelerates development cycles by automating manual testing processes that traditionally consume 60-80% of voice AI development time
• Provides comprehensive risk mitigation through systematic edge case testing and performance validation before production deployment
• Enables data-driven optimization through detailed analytics and performance metrics that identify specific improvement opportunities
• Reduces post-launch support burden by catching critical issues during development rather than after customer interactions

Disadvantages

• Platform focus on voice AI testing means limited applicability for text-only conversational AI or other AI modalities
• Requires initial investment in test scenario design and custom evaluation criteria setup to achieve maximum value
• Dependency on quality of simulated conversations and personas to accurately represent real-world user interactions

How Does It Compare?

Relyable operates in the competitive and rapidly evolving voice AI testing and evaluation market alongside several established platforms, each offering distinct approaches to quality assurance:

• Versus Hamming AI: Both platforms focus on voice AI testing at scale, but Hamming emphasizes enterprise-grade stress testing with capabilities to simulate thousands of concurrent calls and provides specialized compliance reporting for regulated industries. Relyable differentiates through its focus on persona-driven testing and native integrations with popular voice platforms like Vapi and Retell AI.
• Versus Roark (YC W25): Roark takes a production-first approach, allowing teams to replay real customer calls against updated AI logic while preserving original audio characteristics like tone and timing. Relyable focuses on pre-deployment simulation testing, making these platforms complementary rather than directly competitive for comprehensive voice AI quality assurance.
• Versus Coval: Coval applies autonomous vehicle simulation methodologies to conversational AI, emphasizing large-scale scenario testing with CI/CD integration for regression testing. While both platforms provide simulation capabilities, Coval targets enterprise customers with complex multi-agent systems, whereas Relyable focuses on accessibility for teams of all sizes with straightforward voice agent testing needs.
• Versus Cekura (formerly Vocera): Cekura offers comprehensive end-to-end testing from automated scenario generation through production monitoring. Relyable distinguishes itself through specialized persona creation capabilities and streamlined integration processes designed for rapid deployment and testing cycles.

Final Thoughts

Relyable represents a focused solution for development teams seeking to transform their voice AI testing processes from time-intensive manual verification to automated, systematic quality assurance. By addressing the specific pain point that teams spend disproportionate time on testing rather than development, Relyable enables faster iteration cycles while maintaining high quality standards. The platform’s strength lies in its accessibility and rapid setup process, making sophisticated testing capabilities available to teams regardless of size or technical complexity. While the voice AI testing market includes several strong competitors with varying approaches, Relyable’s emphasis on persona-driven testing and seamless platform integration positions it well for teams prioritizing quick deployment and comprehensive pre-launch validation. For organizations building voice AI agents where reliability and user experience are critical, Relyable offers a practical pathway to deploy with confidence while minimizing post-launch surprises.