Validation Experiments

Discovery is moving from search results to AI answers. When buyers ask ChatGPT, Gemini, or Perplexity, the brands the model names win the consideration set, and the pages it cites are often not the ones that rank in search. Most brands can’t see whether they show up, or change it. GASP is a new MMA research lab that helps brands measure their visibility in AI answers and prove what actually moves it, quantifying how much is in their control.

The Agentic AI Advertising (A³) Lab is a collaboration between MMA's AI Leadership Think Tank and Monks to assess the creative and commercial impact of video advertisements fully produced by agentic AI. As the advertising industry evolves from generative to agentic AI, this initiative provides insights into practical capabilities, true impact on effectiveness, and the evolution of workflows, tools, and processes. A³ represents shared learning to future-proof the marketing industry.

AI Creative Effectiveness (ACE) uses artificial intelligence to analyze creative performance patterns and provide pre-launch scoring and recommendations. By learning from your past campaigns, ACE extracts brand-specific success drivers and applies them to new assets—checking brand/platform compliance, predicting performance, and suggesting edits before you go live. This pre-optimization approach helps teams prioritize high-potential assets and fix issues early, reducing costly revisions and improving campaign outcomes.

Semantic & Intent Fusion Targeting (SIFT) represents the next generation of contextual advertising. Instead of relying solely on keywords or legacy segments, SIFT uses pre-computed LLM-derived embeddings to understand page content and user intent at programmatic speed. By querying billions of URLs and anonymized behavioral signals through fast vector similarity search, SIFT adds high-propensity semantic signals to standard RTB inputs—without the latency of in-auction LLM inference.

AI Uncovering Responsive Audiences (AURA) is a breakthrough AI-first audience discovery and optimization program. Using advanced pattern recognition across behavioral, demographic, and transactional data, AURA continuously identifies and upweights high-response micro-segments that traditional targeting methods miss. Early pilots demonstrate an average 22% lift with no creative changes and minimal workflow impact—just split your audience lines and let AI optimize weekly.