Plurai

Plurai is an AI agent trust platform that pairs scenario simulation with custom small-model evals and real-time guardrails, aimed at teams moving agents from prototype to production.

Plurai is a trust platform for teams that build AI agents and need evidence those agents will hold up with real users. It combines three jobs that are usually handled by separate tools: simulating realistic conversations, scoring agent behavior with custom evaluators, and enforcing policies at runtime through guardrails. The pitch is that hand-written test cases and generic LLM-as-a-judge scoring leave gaps that customers end up finding first. The simulation side generates multi-turn scenarios tailored to a specific product and its policies, including voice and document-based interactions. The aim is wider coverage of edge cases than a manual test suite offers, and the runs can be automated inside CI/CD workflows so regressions surface before a release. The homepage cites roughly 15x more edge-case coverage and 7x faster deployment as headline results. On the evaluation and protection side, Plurai turns a plain-language policy prompt into a small language model that acts as a judge or a guardrail. The company positions these models against GPT-5-mini, claiming more than 43% fewer failures, more than 8x lower cost and enforcement in under 100 ms. A Claude plugin lets developers build these judges from inside Claude, and the blog describes serving many LoRA-based guardrails on a single GPU. The audience is engineering and AI quality teams shipping customer-facing agents, particularly in enterprises where policy violations and hallucinations carry real cost. Logos on the homepage include Microsoft, Google, NVIDIA, IBM and Red Hat, though the page does not say how deep each relationship goes. A research section with papers and technical posts backs the approach. Among alternatives, Plurai sits between general observability and evaluation suites on one side and standalone guardrail libraries on the other. Its distinguishing angle is training compact, use-case-specific models rather than relying on a large general model to grade itself, and tying that to simulated test worlds rather than static datasets.