Problem: Product teams often struggle to translate ideas into well-structured A/B tests with clear hypotheses, success metrics, and decision criteria, leading to slow experimentation cycles and ambiguous outcomes.

Insight: By guiding PMs through a structured experimentation framework, covering hypothesis formulation, control vs. variant definition, metric selection, and statistical validity, AI can reduce ambiguity and improve the quality of product decisions without replacing human judgment.

Outcome: Built an AI A/B Testing Hypothesis Builder that helps PMs turn product ideas into experiment-ready hypotheses with defined control and variant setups, focused metrics, and clear outcome logic, enabling faster, more confident, and more disciplined experimentation.

Key Metrics: Hypothesis Adoption Rate, Time to Experiment Readiness and Experiment Win / Decision Rate