Comprehensive testing framework for AI chatbots, RAG systems, and autonomous agents. Measure quality, performance, and reliability with LLM-as-a-Judge evaluation.
Comprehensive evaluation metrics designed for modern AI systems
Evaluate if your AI agent successfully completes the intended objective with LLM-as-a-Judge scoring.
Measure how pertinent and useful responses are to user inputs with semantic analysis.
Ensure AI responses are grounded in context without hallucinating facts or inventing information.
Track retrieval accuracy with contextual recall metrics for your RAG system.
Validate that agents call the right tools with correct parameters in the right order.
Monitor pass rates, latency p95, costs, and trends across all your test suites.
Compare different models, versions, and configurations against baselines.
Track latency, token usage, and cost metrics across test runs.
Schedule automated test runs and get alerted when quality degrades.
Test every type of AI system with confidence
Validate conversational flows, response quality, and user experience across multiple scenarios.
Test retrieval accuracy, context usage, and faithfulness of your RAG system responses.
Verify autonomous agents correctly use tools, call APIs, and execute multi-step plans.
Simple workflow, powerful results
Define test scenarios, expected outcomes, and associate them with your AI agents. Set up context, personas, and test data.
Execute tests manually or schedule them to run automatically. Our system simulates real conversations and tracks every interaction.
Multiple LLM judges evaluate responses across 8+ metrics including task completion, relevancy, faithfulness, and toxicity.
View comprehensive dashboards with pass rates, latency p95, costs, and trends. Compare versions and track improvements over time.
Track what matters for production AI systems
Did the agent achieve the scenario objective?
Is the response pertinent and useful?
Response grounded in context without hallucinations?
Relevant context retrieved in top-k results?
Correct tools called with proper parameters?
Response time meets performance targets?
Token usage and costs within budget?
No harmful or inappropriate content detected?
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