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 Bias Detection

Patronus AI includes bias evaluation as part of its LLM assessment suite, testing model outputs for demographic biases, stereotyping, and unfair treatment across different population groups. Its automated evaluation framework helps organizations identify and quantify bias in AI-generated content before deployment.

AI Content Moderation

Patronus AI evaluates LLM outputs for toxic content, policy violations, and inappropriate responses, providing automated content safety assessment at scale. Organizations use its evaluation tools to verify that their AI applications generate outputs that comply with content policies and community guidelines.

AI MLOps Tools

Patronus AI integrates into MLOps workflows through its API and CI/CD pipeline support, enabling continuous evaluation of LLM applications throughout their lifecycle. Its monitoring dashboards track model quality over time, compare configurations, and alert on quality degradation, providing the observability layer needed for production LLM operations.

AI Safety Tools

Patronus AI specializes in AI safety evaluation, providing automated testing that identifies hallucinations, toxic outputs, PII leakage, and other failure modes in LLM applications. Its red-teaming capabilities automatically generate adversarial prompts to probe for vulnerabilities, helping organizations ensure their AI deployments meet safety standards before reaching users.

AI Testing Tools

Patronus AI provides comprehensive automated testing for LLM applications, evaluating outputs across factual accuracy, relevance, coherence, toxicity, and custom criteria. Its evaluation framework scales to thousands of test cases, integrates into CI/CD pipelines, and provides quantitative scoring that enables systematic quality assurance for generative AI systems.