AEO (Answer Engine Optimization) is the practice of getting your brand chosen as the direct answer in search. GEO (Generative Engine Optimization) is the practice of getting your brand cited and recommended inside AI-generated responses like ChatGPT, Google AI Overviews, and Perplexity. Together they replace the old goal of ranking a link with a new goal: being the answer.
Why the old playbook is fading
Traditional SEO was built on a simple loop. You ranked for a keyword, the user clicked your link, and you captured the traffic. That loop is breaking. People increasingly read a synthesized answer on the results screen and never click through. This is the zero-click reality, and it means a high ranking can now produce very little of what it used to: visits.
The question has changed. It is no longer “how do I rank?” It is “when an answer engine speaks, does it speak my brand’s name?”
What an answer engine actually is
An answer engine does not hand you ten links and let you decide. It reads many sources, decides what is true and relevant, and returns one consolidated response. Google AI Overviews, Perplexity, ChatGPT, and Naver’s answer surfaces all work this way. They act as a curator standing between your brand and the customer.
That curator makes two decisions you now have to win:
- AEO: will my content be selected as the answer shown at the top of a search surface?
- GEO: will a generative model cite or recommend my brand when it writes its reply?
Think of GEO as one level above AEO. AEO is about being picked for the answer box. GEO is about being trusted enough to be quoted in a model’s own words.
How answer engines pick and cite sources
No one outside these companies knows the exact weighting, so treat the following as the durable pattern rather than a formula. Engines tend to favor sources that are:
- Clearly structured, so the relevant fact is easy to extract
- Direct, answering the question in the first sentence rather than burying it
- Specific, with concrete facts a model can lift without ambiguity
- Corroborated, saying things other credible sources also say
- Machine-readable, with clean markup and schema that label what each piece of content is
The through-line is extractability. A model cites what it can confidently pull out and trust. Prose written to impress a human reader is often the prose a machine cannot safely quote.
The first moves to become citable
You do not need a rebuild to start. You need to make a few pages genuinely answerable.
- Pick your real questions. List the actual questions a buyer asks before choosing a vendor like you. These, not keywords, are your targets.
- Answer up front. Open each page or section with a direct, one- to two-sentence answer (BLUF), then expand. The model should be able to quote your first sentence and be correct.
- Structure for extraction. Use clear headings, short paragraphs, and lists. Put one idea per block so a fact can be lifted cleanly.
- Add the facts a model needs. State concrete specifics: what you do, who it is for, how it works, what it costs to engage. Vague pages get skipped.
- Label your content with schema. Mark up FAQs, articles, products, and your organization so engines know what each block is, not just what it says.
- Build corroborated authority. Publish substantive content on your topic consistently. A model trusts a brand that the wider web already treats as a source.
A quick self-check
Before you publish, run the page against this list:
- Does the first sentence answer the question on its own?
- Could a model quote a paragraph without distorting your meaning?
- Is every claim specific enough to be useful?
- Is the page marked up with relevant schema?
- Would another credible source agree with what you wrote?
If you can answer yes to all five, you have moved from writing for rankings to writing to be cited. That is the shift from SEO to AEO and GEO, and it is the work of making a brand machine-readable so AI quotes it.
The brands that win the next phase will not be the loudest. They will be the most structured.