Yival

Yival is an AI-driven experimentation and evaluation platform designed to help teams systematically test, compare, and improve AI prompts, models, and workflows. It provides a structured environment for running controlled experiments on LLM-based applications, enabling data-driven decisions instead of ad-hoc trial and error. The primary purpose of Yival is to make AI evaluation reproducible, scalable, and aligned with real-world performance criteria.

The platform allows users to define evaluation scenarios, input variations, and success metrics, then automatically run experiments across different prompts, model versions, or configurations. It supports multi-metric evaluation, including accuracy, relevance, consistency, and custom business-specific metrics, helping teams understand trade-offs between options. Yival also offers tooling for dataset management, experiment tracking, and result visualization, so teams can compare outcomes over time and across experiments. Its modular design makes it suitable for integrating with existing MLOps workflows, CI/CD pipelines, or internal tooling.