diversity-scoring

Automated quality control for generative AI design applies a 5-axis scoring rubric across color, typography, layout, spacing, and shape. It generates a quantitative scorecard with actionable feedback and a clear pass or reject verdict for each asset.

Style drift occurs when generated designs lack differentiation from established baselines. A multi-axis scoring framework identifies this drift by quantitatively analyzing visual features, preventing generic or off-brand designs from reaching production.

Auditing design packs requires evaluating them against a baseline using a multi-axis rubric. This automated quality assurance process generates a scorecard, identifies which outputs fail to differentiate, and provides actionable feedback on prompt sections to refine.

Scoring design outputs utilizes a 5-axis rubric evaluating color, typography, layout, spacing, and shape or motion. This quantitative analysis measures visual features against established baselines to ensure high-fidelity and distinct design outputs.

Automated style validation applies directly to design system maintenance by running style consistency checks. It ensures generated design assets maintain stylistic diversity and adhere to established baselines through quantitative visual feature analysis.