Also called: GEO, generative engine optimisation
GEO is the practice of improving how generative AI engines represent and recommend a brand when they answer users' questions — optimizing to be cited inside the answer, not merely ranked as a link.
Generative Engine Optimization (GEO) is the discipline of getting a brand fairly represented and recommended by generative AI engines. It is the answer-engine successor to SEO: SEO optimizes for a ranked position in a list of links; GEO optimizes for inclusion inside the AI-written answer that increasingly sits above (or replaces) that list.
The mechanics differ from classic SEO in an important way. AI engines synthesize answers from sources they can extract and trust: clean, machine-readable content, explicit structured data, corroborating third-party mentions, and unambiguous entity signals. GEO work therefore concentrates on being extractable, being corroborated across the web, and being an unambiguous entity the engine can resolve — not on keyword density or backlink volume alone.
Because engine outputs are probabilistic, GEO cannot be verified by a single check. The only honest way to know whether a GEO change worked is to measure the engines' answers before and after, at enough sample size to tell a real change from run-to-run noise. That before/after measurement loop is what separates GEO practice from GEO guesswork.