The short version: GEO (generative engine optimization) and AEO (answer engine optimization) are how a brand gets named inside AI-generated answers. SEO earns a place in a list of links; GEO and AEO earn a place in the answer itself. Buyers increasingly act on the answer without ever seeing the list, which is why this now deserves its own discipline, budget and measurement.
What is GEO (generative engine optimization)?
GEO is the practice of structuring a brand's content and wider web presence so that generative engines, ChatGPT, Gemini, Perplexity, Claude, Copilot, retrieve, cite and recommend that brand when composing answers. It treats the brand as an entity the model must understand clearly: who you are, what you sell, where, and what evidence supports trusting you.
What is AEO (answer engine optimization)?
AEO focuses one level down, on extractability: can an answer engine lift a clean, correct answer from your page in one move? Its tools are question-shaped headings, a 40–60 word direct answer immediately beneath them, definitions, comparison tables and FAQ structure with matching schema.
How do GEO, AEO and SEO differ?
| SEO | AEO | GEO | |
|---|---|---|---|
| Goal | Rank in results | Be quoted in the answer | Be recommended by the answer |
| Metric | Position, clicks | Extraction rate | Share of answer across tracked prompts |
| Core work | Links, keywords, technical health | Direct answers, FAQ schema, structure | Entity consistency, citations, evidence, freshness |
The practical conclusion: you don't replace SEO. You build on it, and aim at the layer where the recommendation now happens.
How do AI engines choose which brands to cite?
Mechanics differ by engine, but four patterns recur. First, extractable beats eloquent: a self-contained answer sentence gets quoted; flowing prose gets paraphrased or passed over. Second, third-party agreement compounds: when several trusted sources name the same brands for a category, models repeat those names, one reason round-ups, directories and reviews matter so much. Third, consistency is identity: conflicting names, services or locations across the web make a model hedge toward a clearer competitor. Fourth, freshness is weighted unevenly: retrieval-led engines such as Perplexity reward recently updated, well-sourced pages quickly, while training-data presence compounds over months.
What should a business do in the first 90 days?
- Weeks 1–2, Audit. Fix a set of 50–100 real buyer prompts (“best…”, “top… near me”, “… vs …”), run them in ChatGPT, Perplexity, Gemini and Claude, and record who is named. This is your baseline share of answer.
- Weeks 2–4, Entity. Publish one canonical description of the business and align it everywhere: website, Google profile, directories, socials, structured data.
- Months 2–3, Content and citations. Rebuild key pages answer-first, add the comparisons and FAQs buyers actually ask, and earn mentions in the sources AI already cites in your category. Re-run the prompt set weekly and keep what moves.
That sequence is deliberately unglamorous. GEO rewards the same virtues as good publishing: be clear, be consistent, be cited by people other than yourself, and keep it current.
Key takeaways
- GEO = being recommended; AEO = being quotable; SEO = being listed. You need all three, in that order of novelty.
- Measure share of answer across a fixed prompt set, per engine, weekly.
- Entity clarity and third-party citations move the needle before content volume does.