Evidently AI

Evidently AI develops open-source software and commercial infrastructure for evaluating, testing, monitoring, and debugging machine learning models and generative AI applications. Founded in 2020, the company builds tools that help engineering teams examine data quality, detect changes in production data, and assess whether AI systems continue to perform as expected after deployment.

Its work spans traditional predictive models, large language models, retrieval-augmented generation systems, and AI agents. The software supports evaluation during development as well as ongoing monitoring in production, connecting experimental testing with the practical maintenance of deployed applications.

Evaluation and monitoring tools

The Evidently open-source Python library is distributed under the Apache 2.0 licence. It combines more than 100 built-in metrics and evaluators with declarative test suites and interactive visual reports. Teams can investigate data drift, concept drift, data integrity, embedding changes, and text quality, using statistical checks and evaluation results to understand potential performance problems.

For generative AI applications, Evidently provides LLM-as-a-judge workflows, hallucination evaluation, RAG benchmarking, and testing for agents and multi-step workflows. Synthetic test dataset generation helps teams assemble evaluation material for complex LLM systems. These capabilities extend the framework beyond tabular and numerical prediction into applications whose outputs require more contextual assessment.

Commercial platforms

Evidently Cloud provides a managed collaborative workspace for datasets, application traces, evaluation runs, and dashboards. It gives AI product teams a shared environment for examining results and maintaining testing workflows.

The Evidently Platform and Enterprise Edition support self-hosted, on-premise, and VPC deployments. Their capabilities include persistent telemetry storage, multi-tenant monitoring, automated alerts, and REST API access. Commercial support includes service-level agreements, architectural consultation, and custom integrations for enterprise machine learning systems.

The company serves AI and data engineering teams across enterprise software, financial services, retail, manufacturing, telecommunications, and digital health. Its product range combines an openly available evaluation framework with managed and enterprise infrastructure for teams operating AI systems at production scale.