Most generative models default to “safe” outputs: fluent, on-brand, and… indistinguishable. In performance marketing, that safety often lands you in the bland middle - materials that look plausible but fail to win attention or drive outcomes. In this talk, I’ll walk through how we engineered an AI tooling stack for marketing that goes beyond generation: a closed-loop system that connects creative production to measurable ad performance. We’ll cover the architecture behind creative generation and variation, multimodal feature extraction, correlation analysis between creative signals and results, and a suggestion engine that proposes concrete improvements (hooks, structure, visuals, claims, and angles).