Patronus AI is an AI safety evaluation and testing platform that helps organizations systematically assess the reliability, safety, and accuracy of large language model applications before and during production deployment. Founded in 2023 by Anand Kannappan, Rebecca Qian, and Neel Guha, and headquartered in San Francisco, California, the company focuses on automated evaluation of LLM outputs to identify hallucinations, toxic content, personally identifiable information leakage, and other failure modes specific to generative AI systems. The platform's core capabilities center on automated evaluation at scale. Patronus AI provides a suite of evaluators that assess LLM outputs across multiple dimensions including factual accuracy, relevance, coherence, toxicity, bias, and compliance with custom policies. These evaluators can be run on thousands of test cases automatically, providing quantitative scores and detailed reports on model behavior. A key product is the hallucination detection system, which evaluates whether LLM-generated responses are grounded in provided source material or contain fabricated information, a critical capability for organizations deploying AI in high-stakes domains like finance, healthcare, and legal. Patronus AI also provides red-teaming capabilities that automatically generate adversarial prompts to probe LLM applications for vulnerabilities, including prompt injection susceptibility, jailbreaking, and policy violations. The platform supports custom evaluation criteria, allowing organizations to define their own quality and safety standards and test against them continuously. Patronus AI integrates into development workflows through its API, enabling evaluation to run as part of CI/CD pipelines and production monitoring systems. The platform provides dashboards for tracking model quality over time, comparing different models or configurations, and alerting on quality degradation. Pricing follows an enterprise model with custom contracts based on evaluation volume and features required.

AI偏差检测

Patronus AI 在其 LLM 评估套件中包括偏差评估，测试模型输出中的人口统计学偏差、刻板印象和不同人口群体之间的不公平对待。其自动化评估框架帮助组织在部署前识别并量化 AI 生成内容中的偏差。

AI内容审核

Patronus AI 评估 LLM 输出中的有毒内容、策略违规和不当回复，大规模提供自动化内容安全评估。组织使用其评估工具来验证其 AI 应用生成的输出符合内容政策和社区指南。

AI MLOps工具

Patronus AI 通过其 API 和 CI/CD 管道支持集成到 MLOps 工作流中，在 LLM 应用的整个生命周期中实现持续评估。其监控仪表板跟踪模型质量随时间的变化，比较配置，并在质量下降时发出警报，为生产 LLM 运营提供所需的可观测性层。

AI安全工具

Patronus AI 专门从事 AI 安全评估，提供自动化测试来识别 LLM 应用中的幻觉、有毒输出、PII 泄露和其他故障模式。其红队能力自动生成对抗性提示来探查漏洞，帮助组织确保其 AI 部署在到达用户前符合安全标准。

AI 测试工具

Patronus AI 为 LLM 应用提供全面的自动化测试，评估输出的事实准确性、相关性、连贯性、有毒性和自定义标准。其评估框架可扩展到数千个测试用例，集成到 CI/CD 管道中，并提供定量评分，使生成式 AI 系统能够进行系统的质量保证。