
Evaluation and observability for AI systems
Evidently AI offers a comprehensive AI observability platform that bridges the gap between open-source accessibility and enterprise-grade AI testing needs. The platform distinguishes itself through its strong open-source foundation and specialized focus on the unique failure modes of non-deterministic AI systems, from hallucination detection to adversarial security testing.

Evidently AI is a collaborative AI observability platform specializing in the evaluation, testing, and monitoring of AI-powered products, including Large Language Models (LLMs) and traditional Machine Learning (ML) systems. Built on top of a trusted open-source Python library with over 6,500 GitHub stars and 35 million downloads, the platform provides teams with comprehensive tools to ensure their AI systems are safe, reliable, and production-ready. The company offers automated evaluation capabilities, synthetic data generation for testing edge cases and adversarial scenarios, and continuous monitoring dashboards to track performance across updates. The platform addresses the unique challenges of non-deterministic AI systems, including hallucinations, data leaks, jailbreaks, and cascading errors in multi-step workflows. Evidently AI serves a wide range of use cases including RAG pipeline testing, AI agent validation, adversarial security testing, and predictive ML model monitoring. With over 100 built-in metrics and the flexibility to create custom evaluations, the platform enables teams to design quality systems tailored to their specific AI applications. The company also offers enterprise solutions with private cloud deployment, role-based access control, and dedicated support for organizations building AI at scale.