Generative Engine Optimization is the practice of structuring a brand’s entities, proof, and pages so answer engines — ChatGPT, Gemini, Claude, Perplexity, and AI Overviews — can retrieve, quote, and attribute you on high-intent questions. It is useful when buyers start in AI and never reach a classic search results page unless the model already trusts your source.
Whilst GEO is often sold as “SEO for ChatGPT,” in practice engines do not rank a keyword list — they prefer clear entities, claim-dense answers, and consistent public facts. For example, a company invests in blog volume and still loses the citation to a competitor with one sharper answer page and cleaner Organization markup. We often recommend a fixed prompt set, measurable answer share, and closing citation gaps with answer pages plus entity clarity — before scaling content production.
GEO sits beside SEO and AEO but optimizes for a different surface: generative answers that name, recommend, or link a source. Models build a working picture of who you are from your site, bios, schema, and third-party mentions; conflicts invent, consistency cites. The operating loop is practical — define the buyer prompts that matter, publish pages that answer them in the first screen, keep people/company/offer relationships consistent, ship machine-readable maps where they help parsers, and re-measure named vs linked vs absent across engines. Citation assets (specs, methods, cases) matter because generative systems lift passages they can verify, not adjectives. Treat GEO as findability engineering for answer time, not a one-shot campaign or a replacement for crawlable technical SEO.