Generative Engine Optimization (GEO) is the umbrella term for improving how generative AI systems describe and recommend your brand.
GEO emerged from academic work measuring how content changes propagate into LLM answers. In practice it covers the same territory as AEO: getting cited, getting recommended, and getting described accurately by ChatGPT, Gemini, Claude, and Perplexity.
Most GEO platforms are telemetry. They run category prompts on a schedule, log which brands each model mentions, and chart your share of voice over time. That answers “what is AI saying about us?” It does not answer “how do we change it?”
A dashboard that says you are losing is a starting point, not a strategy. The gap in most GEO stacks: no map of the upstream moments that create demand, no engine that ships corrective content, and no attribution tying any of it to revenue. You get a score and a to-do list you have to figure out yourself.
We treat GEO-style visibility tracking as one of four tools, not the product. Moment Map finds where demand is born, Visibility Tracking measures the answer, the Action Engine ships the signal that changes it, and attribution proves what it earned. Context, not keywords.