How ChatGPT, Gemini, Perplexity, Google and Naver choose what to say and what to cite, measured with real answers, and how to track your brand in them.
We asked ChatGPT and Gemini 15 “near me” questions from 36 spots in New York, Seoul, Austin and Tokyo, twice. Answers 3 km apart shared 4.9% of their places on ChatGPT and 15.1% on Gemini.
We read every source in 479 AI answers to the same 136 buying questions in Korea and the US. 88% of the websites cited for a question appeared on one engine only, Perplexity cited Reddit in 34% of US answers and Naver AI Tab cited Naver Blog in 97%.
Query fan-out is the set of web searches an AI assistant runs to answer one question. Real fan-out queries from ChatGPT and Copilot, what they add to a question, and how to collect them as GEO data.
We sent 10 shopping questions to the OpenAI Responses API with the web_search tool. One answer averaged $0.0579, and the search results the model reads cost more than the search call. The full bill, and what the same answer costs on querying.ai.
GEO gets your brand named and cited in ChatGPT, Gemini, Perplexity and Google AI answers. How engines pick brands, how GEO relates to SEO and how to measure it.
We asked ChatGPT, Gemini, Perplexity and Naver AI Tab 136 buying questions in Korea and the US. 82% of the 479 answers named a brand. Here is who they named.
Measure how often ChatGPT, Gemini, Perplexity and Google AI name and cite your brand: the questions to ask, the engines and markets, the metrics and the API calls.
A mention rate from 30 AI answers carries a margin of about 18 points. How many prompts, engines and runs it takes to measure AI visibility and to compare two periods, with the math.
We asked ChatGPT for an orthopedic clinic near three Seoul stations and three New York neighborhoods, and every place got its own clinics. How to measure local AI visibility, place by place.