
Why you need AI evals
Building production AI systems without proper AI evals is risky. Teams often discover issues in production because they lack visibility into AI behavior across different scenarios. Manual evals don’t scale, and without systematic evaluation, it’s impossible to know if changes improve or degrade performance. Scorecard provides the infrastructure to run AI evals systematically, validate improvements, and prevent regressions.Who uses Scorecard
AI Engineers run evals systematically instead of manually checking outputs Product Teams validate that AI behavior matches user expectations QA Teams build comprehensive test suites for AI systems Leadership gets visibility into AI reliability and performanceWhat Scorecard provides
Testset management — Convert real production scenarios into reusable test cases. When your AI fails in production, capture that case and add it to your regression suite. Playground evaluation — Test prompts and models side-by-side without writing code. Compare different approaches across providers (OpenAI, Anthropic, Google Gemini) to find what works best. Domain-specific metrics — Choose from pre-validated metrics for your industry or create custom evaluators. Available for legal, financial services, healthcare, customer support, and general quality evaluation. Automated workflows — Integrate AI evals into your CI/CD pipeline. Get alerts when performance drops and prevent regressions before they reach users.How it works
- Create testsets from your real use cases and edge cases
- Run evaluations across different prompts, models, and configurations
- Compare results to identify the best performing approaches
- Deploy with confidence knowing your AI system meets quality standards