LLM evaluation and  observability
Catch hallucinations, safety risks, and quality issues before they impact users.
AI TESTING
Built for teams who can’t afford to guess
LLM products go beyond prompts — they’re complex systems with models, data flows, and business logic. We provide a complete testing platform to ensure reliability and safety across entire workflows.
Test any AI system
From RAG chatbots to multi-agent workflows.
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Customize evaluations
Configure metrics to match your risks.
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Test every AI component
Validate single prompts or full interactions.
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Move beyond spot-checks
Run experiments with repeatable tests.
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Work as a team
Centralize evaluations, share findings, and collaborate on quality.Â
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Prove readiness
Actionable insights and audit-ready reports.
features
End-to-end AI evaluation
Use Evidently as a Python library for ad hoc checks and experiments, then scale to a self-hosted platform when you're ready.
EVALS
Run automated evaluations
Measure what matters, with structure and scale.
Built-in and custom metrics. Factuality, helpfulness, relevance, and more.
Automate grading. Scale manual labels with LLM-as-a-judge.
Catch issues before users do. Detect hallucinations, correctness gaps, and safety risks.
SYNTHETIC DATA
Generate realistic test cases
Ensure broad test coverage across real-world scenarios.
Simulate interactions. From expected inputs to complete user sessions.
Test edge cases and attacks. Probe AI resilience under stress.
Adapt to new risks. Update with evolving user behavior and threats.
TEST
Manage test suites
Keep tests up to date and ship with confidence.
Curate and version datasets. Maintain structured, reliable evaluation.
Expand test coverage. Add new scenarios, edge cases, and failure modes as your AI system evolves.
Catch regressions. Prevent quality drops before they hit production.
Reports
Get clear insights
Find out where your AI breaks and how to fix it.
Compare side-by-side. Spot changes between models and prompts.
Drill into failures. Understand specific incorrect responses.
Debug faster. Identify patterns and prioritize fixes.
MONITORING
Track AI performance
AI testing doesn’t stop at launch — stay ahead of failures.Â
Run continuous tests. Validate new releases and prompt updates.
Identify new risks. Spot emerging failure patterns.
Evaluate live data. Get full production observability.
use cases
Start testing where it counts
Focus on the most critical risks and workflows for your AI system.
Adversarial testing
Jailbreaks, PII leaks, harmful content.
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AI agent testing
Multi-step workflows and tool use.
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RAG evaluation
Hallucinations and retrieval failures.
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ML system monitoring
Drift, classifier or recommender performance.
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Evals
Define AI quality on
‍your terms
Tailor tests to your risks, standards, and performance goals.
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Safety
Ensure responses align with policies.
Toxicity
Detect offensive or discriminatory language.
Hallucinations
Catch outputs that are factually wrong or out of context.
Retrieval quality
Verify if the retrieved content is relevant.
PII Detection
Identify personal data in outputs.
Answer relevancy
Measure response accuracy to user intent.
Format compliance
Ensure outputs follow the expected structure.
Intent classification
Understand the purpose behind user queries.
Prompt injection
Catch attempts to manipulate the model.
Correctness
Compare outputs against references.
Tone
Align AI responses with brand guidelines.
Robustness
Test consistency across runs.
Get started with Evidently
Open-source AI evaluation and observability for your systems.
© 2026, Evidently AI. All rights reserved
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