AI search optimization for Miami and South Florida.

You'll see this work sold as AI search optimization, as answer engine optimization (AEO), and as generative engine optimization (GEO). They describe the same problem: customers are asking an assistant for a recommendation, the assistant answers with a short list of businesses, and you want to be described accurately in that answer. We don't package them as three services with three prices. It's one method.

The starting point is measurement, because the alternative is guessing. We take the questions a customer in your city would actually ask — not keyword strings, real sentences — and put them to the four assistants people use. We write down who gets named, who gets named first, who gets left out, and what each answer cites. That becomes a dated snapshot you can hold onto and compare against later.

Then we explain the gap. Usually it isn't mysterious: a competitor appears in more of the directories and roundups the model can read, has more recent and more specific reviews, or has pages that answer the question in the same words a customer uses. We say which competitor, and which sources.

The work that follows is citation and directory consistency, structured data, review signals, and content built around real questions. Every month we re-run the same prompts on the same platforms and send screenshots. We don't promise a first mention and we don't publish invented percentages — the report says what the assistants said.