End-to-end AI security and governance platform to discover, monitor, red-team and prove enterprise AI
EU AI ActNIST AI RMFISO/IEC 42001
Patronus AI
Automated evaluation, guardrails, and judges for LLM and agent reliability
What Patronus AI does
Patronus AI is an automated evaluation, observability, and guardrails platform for large language models and AI agents, built to detect model mistakes at scale. Founded in 2023 in San Francisco by former Meta machine-learning researchers Anand Kannappan and Rebecca Qian, the company helps enterprises measure and improve AI quality by generating adversarial test cases, benchmarking models, scoring outputs, and catching hallucinations, unsafe content, PII leakage, and copyright or compliance violations before and during deployment. Its offerings include managed evaluators and 'judge' models (such as its Lynx hallucination-detection model and the GLIDER family), the Percival agent-debugging capability, and runtime guardrails that can be embedded in production pipelines, including via gateways like Bifrost. Patronus emphasizes rigorous, research-driven evaluation and continuous testing rather than only a static safety filter, positioning itself at the intersection of AI evaluation and security. The company raised a $17M Series A in 2024 led by Notable Capital with Lightspeed and Datadog, reaching about $20M total, and has since announced substantial follow-on funding. Patronus AI is aimed at ML and product engineering teams, and the risk and compliance stakeholders they support, that need trustworthy, automated evaluation and guardrails to ship LLM applications with measurable reliability.
Key capabilities
- Automated LLM evaluation and benchmarking
- Adversarial test-case generation
- Lynx hallucination detection and judge models
- Percival agent debugging
- Runtime guardrails
- PII, safety and compliance checks
Best for
ML and product teams that need automated, research-grade evaluation plus guardrails to ship reliable LLM apps
Limitations
More an evaluation/observability platform than a hardened security firewall; deepest value requires building evaluation into workflows; younger company still expanding enterprise features
Framework coverage
| Framework | Type | Supported |
|---|---|---|
| NIST AI RMF | Voluntary framework | Yes |
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