EvalGenius automatically generates, optimizes, and maintains evaluation sets from your production data—so you can focus on building better agents, not managing evals.
As AI agents move from rapid prototyping to production, quality has become the main differentiator for success. Yet ensuring that quality is harder than ever.
Your agents generate massive observability data in production, making manual eval creation impossible to scale.
Off-the-shelf evaluation criteria like "bias" and "factuality" miss the nuanced requirements of your specific domain.
Inflated eval sets with repetitive examples and unnecessary dimensions drain your budget with every eval run.
Crafting effective LLM-as-a-judge scorers requires deep domain knowledge that's hard to scale across your team.
EvalGenius transforms your raw observability data into optimized, production-ready evaluation sets—automatically.
Seamlessly integrate with observability platforms like LangSmith, OpenAI Evals, Ragas or any other one your team prefers.
Our AI analyzes your production data to create domain-specific eval dimensions tailored to your agent's actual behavior.
Advanced algorithms identify and eliminate redundant dimensions and repetitive examples, maximizing signal while minimizing eval costs.
Automatically upload your optimized eval set back to your preferred framework, ready to run.
All within your specified budget.
What used to take weeks of manual curation now happens in hours. Fully automated, end-to-end.
Run leaner eval sets without sacrificing coverage. Eliminate redundant dimensions and examples that inflate your evaluation budget.
Domain-specific dimensions generated from real production data catch issues missed by generic evals.
As your agent evolves and usage grows, your evals automatically adapt—no expert intervention required.
Keep your evals synchronized with actual agent behavior as it changes in production.
Integrates seamlessly into your existing pipeline, no data leaves your control.
Connect to LangSmith, OpenAI Evals, Ragas, Langfuse, and more
AI-powered analysis creates relevant, domain-specific evaluation criteria
Algorithms minimize redundancy while maximizing eval coverage
Select the smallest set of examples that provide maximum insight
Operates within your cost constraints from day one
Seamless upload to your existing eval framework
Traditional eval platforms help you run evals. EvalGenius helps you create better evals.
| Traditional Approach | EvalGenius |
|---|---|
| Manual eval curation | Fully automated generation |
| Generic dimensions | Domain-specific criteria |
| Growing costs with scale | Optimized for efficiency |
| Static eval sets | Continuously adapts to production data |
| Requires domain experts | AI-powered intelligence |
EvalGenius can bootstrap your evaluation process from your observability traces alone. We start with proven eval dimensions (e.g. bias, helpfulness, safety) and help you select representative eval examples from your initial agent executions. This means you can start using evals immediately to improve your AI applications.
As your system produces more observability data in production, EvalGenius continuously refines and optimizes your eval dimensions and eval dataset tailored to your use case.
EvalGenius directly integrates with major eval platforms, including LangSmith, OpenAI Evals, Ragas, and Langfuse through their SDKs, through which it can access trace data and upload the optimized eval dimensions and datasets.
Absolutely. You maintain full control and can modify eval dimensions or datasets.
EvalGenius continuously processes new observability data, so your evals evolve alongside your AI application.
Teams typically reduce eval costs by 40-70% by eliminating redundant examples and dimensions while maintaining or improving coverage.
Join the waitlist for early access to EvalGenius and transform how you manage AI agent quality.
Questions? Contact us