Plurai

Plurai builds evaluations and guardrails tailored to an AI agent from a description. A user explains what the agent should and should not do, and Plurai generates the training data, validates it, and deploys a custom model in minutes, no labelled data, annotation pipeline, or prompt engineering.

Under the hood it uses small language models with sub-100-millisecond latency, roughly eight times lower cost than using GPT as a judge, and reports over 43% fewer failures, running continuously rather than on samples. It is built on published research.

It is aimed at teams running agents in production who need reliable, always-on evaluation and safety checks without building an evaluation system themselves.