Error discovery & evaluation for AI Agents
The single platform to analyze, test, observe, evaluate and fine-tune new AI features
Okareo automatically generates synthetic scenarios with variations (rephrasing, misspellings, conditionals) and pairs them with baseline metrics. The platform's fine-tuning co-pilot automates dataset generation, evaluation, and deployment of fine-tuned models across multiple LLM vendors, enabling customization and predictability without manual dataset creation.
Okareo runs scheduled synthetic callers against production agents and pairs that with live voice observability on real calls. Behavioral alerts detect regressions in tone, policy, tool use, or routing, and failed real calls feed back into development as new test scenarios, enabling continuous proof that agents remain stable post-launch.
Okareo creates synthetic users (Drivers) that simulate real customer interactions across voice, text, and headless channels in 120+ languages. These synthetic personas automatically explore edge cases, multi-turn conversations, and real-world conditions (background noise, language switching, emotional volatility) that manual QA scripts miss, exposing failures before customers encounter them.
Okareo enables evaluation on every PR by running synthetic simulations and applying LLM judges, symbolic checks, and audio-native evaluations on the same scorecard. Failed scenarios block merges, and production failures automatically become new test cases, creating a closed-loop system where your test library strengthens weekly without manual test writing.
Okareo synchronizes simulation conversation transcripts with observability traces on a single timeline, allowing conversation designers and engineers to see the same call from different perspectives. Audio-native evaluation catches latency, prosody, and mispronunciation that transcripts silently drop, and drill-down from phrase to span reveals exactly where in the stack failures occur.