Scorecard

Scorecard is designed for teams involved in developing AI systems within high-stakes environments. It integrates LLM evaluations, human feedback, and product signals to facilitate the automatic learning and improvement of AI agents. This enables teams to confidently evaluate, optimize, and deploy AI solutions. Scorecard provides a comprehensive AI laboratory for testing ideas and creating experiments, allowing teams to assess agent performance against trusted metrics. By offering a continuous evaluation system, Scorecard helps identify and resolve real-world usage issues quickly, ensuring AI models are reliable and effective.

The platform addresses common challenges in AI development, such as slow feedback cycles and isolated workflows that obscure insights between development and production. By connecting these environments, Scorecard establishes a continuous feedback loop, enabling teams to make faster and more meaningful improvements. Users can observe AI behavior in real-time, monitor interactions and failures, and track performance through a validated metric library. Scorecard also allows for versioning and storing of effective prompts, providing a centralized repository of successful strategies.

Scorecard's approach creates a rapid feedback loop, enhancing the traditional AI development workflow. It empowers teams to test smarter, validate relevant metrics, and continuously improve AI agents, fostering innovation and ensuring high performance before deployment.