Evidently AI provides an open-source platform for monitoring and evaluating machine learning models in production, utilizing over 100 built-in metrics for data quality, model performance, and data drift detection. The tool enables teams to conduct systematic tests, generate reports, and maintain AI product integrity throughout the machine learning lifecycle.
Funding
$125K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
DVFVFounders
Product
Problem
Machine learning models deployed in production are susceptible to data drift, data quality issues, and performance degradation, leading to unreliable predictions and business impact. Traditional software testing methods are inadequate for addressing the unique challenges of AI systems, including hallucinations, edge cases, data leaks, and adversarial attacks.
Solution
Evidently AI provides a platform for evaluating, testing, and monitoring AI-powered products, including LLMs and traditional ML models. The platform offers automated evaluation, synthetic data generation, and continuous testing to ensure AI systems are safe, reliable, and ready for deployment. It helps teams measure output accuracy, safety, and quality, generate realistic test cases, and track performance across every update with a live dashboard. Evidently AI enables users to design custom AI quality systems, combining built-in metrics, rules, classifiers, and LLM-based evaluations to address specific use cases.
Target Audience
The platform is designed for AI product teams, including machine learning engineers, data scientists, and MLOps engineers, who need to ensure the quality, safety, and reliability of their AI systems in production.
Features
- Automated evaluation of LLM output accuracy, safety, and quality with shareable reports
- Synthetic data generation for creating realistic, edge-case, and adversarial inputs
- Continuous testing to track performance across updates and detect drift, regressions, and emerging risks
- Library of 100+ built-in metrics for data quality, model performance, and data drift detection
- Customizable dashboards for visualizing AI product performance and sharing results
- Support for adversarial testing to probe for PII leaks, jailbreaks, and harmful content
- RAG evaluation to prevent hallucinations and test retrieval accuracy
- AI agent testing to validate multi-step workflows, reasoning, and tool use
- Open-source Python library for transparent and extensible AI monitoring
- Integration with CI/CD pipelines for continuous validation