Search is splitting in two
Classic search returns ten links and lets the buyer choose. Generative search — ChatGPT, Perplexity, Google AI Overviews, Gemini, Copilot — returns an answer: two or three named businesses and a short rationale. The buyer often never clicks past it. For most categories, being named in that answer is now worth more than ranking #3 on Google.
Why AI names some businesses and not others
Large language models recommend entities they have seen consistently and credibly across the corpus they were trained on and the live sources they retrieve. In practice that means:
Structured data (schema)
LocalBusiness / Organization / Product + FAQ JSON-LD so engines can extract facts without ambiguity.
Citations in trusted sources
Consistent presence in the directories, review sites and publications an AI leans on for your category — the sources it can point to.
Answer-first, entity-rich content
Pages that state the answer up front with specifics, credentials and clear entities (E-E-A-T) — the text models lift verbatim.
Consistency of the entity
Same name, category, and details everywhere, so the model resolves you to one confident entity rather than a fuzzy maybe.
GEO vs SEO
SEO optimizes for a ranked list of links; GEO/AEO optimizes for inclusion in a synthesized answer. They overlap (both reward authority and clean structure) but the winning tactics differ: schema and citation presence matter more for GEO, and there is no "page two" to catch overflow.
How to measure it
You can't improve what you don't measure. The AI Visibility Checker asks the AI the real buyer questions for your category and scores 0–100 how often it names you, with the competitors it names instead. That's your GEO baseline. Browse categories →