fixa is an open-source voice agent observability platform and testing package, designed to help developers monitor, debug, and improve the performance of AI-powered voice agents. fixa offers developers tools to identify and address issues such as latency, interruptions, and correctness in production voice agent calls.
The core product, 'fixa observe', enables users to measure key metrics like response times (including TTFW, p50/p90/p95 latency), detect when voice agents interrupt users, and run custom evaluations to ensure accurate responses. In addition, fixa integrates with Slack to provide custom alerts whenever a voice agent malfunction is detected, allowing teams to respond quickly to issues as they arise.
The platform has also introduced an open-source voice agent testing package, available on GitHub, which supports automated testing and quality assurance for voice AI systems.
Who Uses fixa?
fixa primarily serves teams and developers building, deploying, or maintaining AI voice agents—particularly those seeking greater visibility into system behavior and reliability. Early customers include organizations like 11x, echowin, and telli, who leverage fixa to monitor and test their production voice interactions.
How Was fixa Started?
fixa was founded in 2024 by Oliver Wendell-Braly, with Luan Nguyen also part of the founding team. The company is based in San Francisco, California. The founders bring experience in building developer tools and AI infrastructure, focusing fixa's mission on making voice agent quality assurance accessible and actionable.
What Makes fixa Different?
fixa's key differentiators include its open-source approach, specialized focus on voice agent observability, and real-time operational insights like interruption detection and latency analysis. The seamless Slack integration for alerting and the customizable evaluation framework set fixa apart from more generic monitoring solutions.
Who Are fixa's Competitors?
fixa operates within the emerging space of voice agent monitoring and testing. While there are broader observability platforms and testing tools for AI systems, few focus specifically on the unique challenges of production voice agents, such as conversational latency and user interruption tracking.
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