Roark

Product Introduction

1. Roark is a voice AI observability platform designed to monitor, evaluate, and stress-test conversational agents in real-world scenarios. It provides tools to track call performance metrics, simulate diverse user interactions, and convert failed calls into automated tests for continuous improvement.
2. The core value of Roark lies in its ability to ensure reliability and compliance for voice agents by offering granular visibility into interactions, proactive testing across accents and languages, and automated failure remediation workflows.

Main Features

1. Roark monitors live calls using 40+ built-in metrics, including latency, repetition detection, sentiment analysis, and custom-defined events such as payment verification or appointment scheduling. It supports multi-speaker analysis for conference calls with up to 15 participants and provides automated speaker identification.
2. The platform enables graph-based simulation scenarios where users define conversation flows, configure personas with specific accents (e.g., British), speech patterns, and background noise, and test agents across edge cases. Simulations run at scale with configurable success/failure thresholds and auto-generated test cases from production failures.
3. Roark integrates natively with voice platforms like VAPI, Retell, LiveKit, and Pipecat Cloud via one-click setups or SDKs (Node/Python), offering real-time dashboards, automated reports, and webhook alerts. Integrations include automatic call capture, HIPAA-compliant data handling, and SOC2-certified workflows.

Problems Solved

1. Roark addresses the lack of visibility and reliability in voice AI deployments, where untested edge cases, latency spikes, or script deviations lead to failed customer interactions. It eliminates manual testing bottlenecks by automating scenario generation and compliance checks.
2. The product targets engineering and product teams building voice agents for healthcare, customer service, or sales, particularly those using platforms like VAPI or Retell that require HIPAA-compliant monitoring and detailed conversation analytics.
3. Typical use cases include pre-deployment stress-testing of agents handling payment collections, post-call HIPAA compliance audits for healthcare applications, and real-time sentiment analysis to detect frustrated users during support calls.

Unique Advantages

1. Unlike generic monitoring tools, Roark specializes in voice AI, offering fine-tuned ASR models, emotion detection through vocal cues, and graph-based simulation editors that replicate complex conversational branching.
2. The platform automatically converts failed production calls into repeatable test cases, reducing manual effort by 85% while ensuring fixes are validated against historical failures. It supports 15+ languages and configurable accents for global deployment testing.
3. Competitive advantages include HIPAA-ready infrastructure, native integrations with leading voice platforms requiring under 60 seconds of setup, and granular pricing based on call-minute credits rather than rigid tiered plans.

Frequently Asked Questions (FAQ)

1. What does Roark do? Roark provides QA and observability tools for voice AI agents, including live call monitoring with 40+ metrics, automated testing via simulated personas, and compliance checks for HIPAA or SOC2 requirements. It converts failed interactions into automated regression tests.
2. How difficult is it to integrate Roark? Integration requires less than 60 seconds for platforms like VAPI or Retell using pre-built connectors, while SDKs (Node/Python) enable custom deployments with full access to metrics, evaluators, and simulation APIs.
3. How do simulations work? Users design conversation flows in a graph editor, configure personas with accents and speech patterns, and run bulk tests. Roark measures success rates, detects script deviations, and auto-generates edge cases from historical failures.
4. What are metrics and evaluators? Metrics include technical indicators like latency (230ms avg) and business-specific events (e.g., payment verification). Evaluators are automated checks for compliance, script adherence, or tool-call accuracy, triggered via UI or API after each call.
5. What kind of sentiment analysis does Roark offer? The platform detects user sentiment (positive/negative) using vocal tone analysis, supports custom emotion models, and correlates sentiment shifts with conversation events like payment failures or escalations.