AI Search
A growing share of buying decisions starts inside an AI answer, not a list of blue links. When ChatGPT, Gemini, Perplexity, or Google AI Overviews summarize your market, they name a handful of brands — and being one of them is a different discipline than classic ranking. It is usually called GEO (generative engine optimization) or AEO (answer engine optimization).
The articles in this category cover that discipline in practice: how AI assistants pick their sources, how to structure pages so models can quote them, what llms.txt and schema markup change, and how to measure whether AI engines actually mention you. This is also the problem Seonix automates — its content engine writes and structures every article for both Google and AI search from the start.
Start with the ChatGPT-visibility guides if you need a diagnosis: they include a reproducible 25-prompt audit you can run against your own brand today. Then move to the structured-data and llms.txt material to make every future page machine-quotable by default. The pattern repeats across the whole category — measure where you stand, fix the layer that blocks citations, then keep publishing pages that answer one question per section, because that is the format AI engines quote most readily.