Elixir

Free AI Ops & QA platform for testing, monitoring, and debugging voice agents
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Elixir is an AI Ops and QA platform built for multimodal, audio-first products—especially voice agents that need to work reliably in real-world conversations. Instead of waiting for issues to surface in production, Elixir helps teams proactively test, monitor, and debug their agents with workflows designed specifically for spoken interactions.

At the core of Elixir is the ability to simulate realistic test calls that reflect how users actually speak: interruptions, ambiguity, edge cases, and varied intents. After each call, Elixir automatically analyzes the full interaction to spot mistakes and quality regressions, helping you understand not only what failed, but why it failed. When something goes wrong, you can drill into the exact moment of failure using a unified debugging view that brings together audio snippets, call transcripts, and LLM traces in one place—making it easier to trace errors across speech, reasoning, and tool execution.

Elixir also supports streamlined review workflows. Teams can manually review calls when needed, use scoring and auto-grading to reduce time spent on repetitive QA, and track performance over time with monitoring and analytics. With dataset management, you can build and maintain a comprehensive library of scenarios to consistently evaluate agent behavior, compare changes across versions, and validate improvements before shipping. more

Review summary

Features

  • Automated testing with realistic simulated calls
  • Automated call analysis and mistake detection
  • Debugging with audio snippets, transcripts, and LLM traces
  • Monitoring and analytics for call quality at scale
  • Tracing for end-to-end visibility across the agent stack
  • Scoring, auto-grading, and manual review workflows
  • Dataset and scenario management for repeatable evaluation
  • Integrations with LLM providers, vector DBs, agent frameworks, telephony/WebRTC, and transcription/voice services

How It’s Used

  • Simulate realistic test calls to validate voice agent reliability before release
  • Automatically analyze conversations to identify errors and regressions
  • Debug failures quickly using aligned audio, transcripts, and LLM traces
  • Monitor call metrics and detect issues at scale in production
  • Reduce QA workload with scoring and auto-grading, while enabling targeted manual review
  • Evaluate agent performance against a maintained dataset of scenarios and edge cases

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