GEO: generative engine optimisation, and why it is just AEO
Generative engine optimisation is the American name for answer engine optimisation. Same entity work, same structured data, same goal of being the cited source. Do not let anyone sell you both.
The short version: GEO is AEO with an American accent
If you have been pitched generative engine optimisation as the next thing after answer engine optimisation, here is the part nobody selling it wants to lead with: they are the same discipline. GEO is the label that took hold in the United States, largely off the back of a 2023 research paper that studied how large language models decide which sources to weave into a generated answer. AEO is the term that settled in more often over here. Both describe the work of making your business the source an AI engine cites when it answers a question in your category. The underlying craft does not change because the acronym did. I am labouring this because the naming has become a selling opportunity. A handful of agencies now offer AEO and GEO as if they were two products with two price tags. They are not. When you strip away the branding, both come down to the same entity work, the same structured data, the same content written to be extracted and quoted. If a proposal lists them separately, that is a commercial decision, not a technical one, and it is worth asking why.
Where the two terms actually came from
Answer engine optimisation grew out of the SEO community as search results started answering questions directly, first with featured snippets and People Also Ask, then with AI Overviews. The instinct was familiar: if the engine is going to answer without a click, be the source it answers with. Generative engine optimisation arrived from the academic and American side, framed specifically around generative models such as ChatGPT, Perplexity and Gemini that synthesise a written response rather than rank a list of links. You can draw a fine distinction if you want to. You could say GEO leans towards engines that generate prose, while AEO covers any answer surface including the older snippet-style boxes. In day-to-day practice that distinction does not survive contact with the work. The same clean HTML, the same schema, the same clearly stated answer near the top of a page, and the same corroboration from trusted third parties serve every one of those surfaces at once. We treat them as one programme because splitting them would mean doing the same job twice and charging for it.
How a model actually picks who to cite
This is the question worth your attention, because it is where the real work lives. A generative engine does not rank ten blue links and step back. It reads the sources it considers strongest, writes a single synthesised answer, and credits a few of them. Getting into that handful comes down to three things a model can assess. First, can it parse you at all. If your key facts only appear after heavy JavaScript renders, or your page is a soup of unlabelled divs, the model may skip what it cannot read cleanly. Legibility is unglamorous and decisive. Second, is there a clean passage to lift. Models favour content that states the answer plainly, defines terms, and presents primary data or clear specifics rather than making the reader infer them. If the quotable sentence is easy to extract and hard to argue with, it gets used. Third, is the claim corroborated. A model is more confident naming you when credible third parties in your sector repeat the same thing. That is why digital PR and earned citations matter here as much as anything on your own site. Consistency of your entity across your site, Companies House, and the references models already lean on ties it together, so the engine knows who you are and does not confuse you with a competitor.
Why you should not buy it twice
Here is the practical warning. Because GEO and AEO share a foundation, and because that foundation is the same entity and structured-data work that underpins good SEO, the moment you see them itemised as three separate retainers you are almost certainly being asked to pay several times for one job. The technical legibility pass is done once. The schema is written once. The reference content is built once. The corroboration is earned once. That single body of work then serves your rankings, your AI Overviews presence, and your citations in ChatGPT and Perplexity together. So when you compare proposals, look past the vocabulary. Ask what actually ships each month and who does it. A firm that quotes you for SEO, then AEO, then GEO as if each unlocks a different mechanism is either confused about the discipline or hoping you are. The honest structure is one programme, priced once, with the AI-answer work sitting on top of the SEO foundation rather than beside it as a separate purchase.
When GEO is premature, and we will say so
There is a version of this pitch that sells you generative engine optimisation before you have anything worth citing, and it is worth being wary of. GEO only earns its place once there is source material a model can synthesise. If your site has no structured data, thin content and no authority, there is nothing for an engine to quote, and pointing AI tools at an empty cupboard changes nothing. In that situation the right advice is to start with SEO: fix the crawlability, build the reference content, earn the coverage. The AI-answer gains follow from that foundation, they do not substitute for it. This is also why we rarely sell GEO or AEO on their own. Not because the demand is not real, but because doing so in isolation would usually be premature and we would rather tell you that than take the retainer. The engines reward businesses that are genuinely a good answer to a buyer's question. The work is to become one, then make sure the machines can read it, quote it and attribute it to you.
How to measure it honestly
Because there are no rankings in the classic sense, the temptation is to measure GEO with vanity numbers or, worse, to promise placements. Neither is honest. Nobody controls what a model outputs, so any guarantee of a citation is a guess dressed up as a commitment. What can be measured is share of answer. You define the set of questions your buyers actually put to these engines, run them across the major surfaces, and record for each one whether you are cited, a competitor is, or nobody is. Tracked over time and tied to assisted pipeline, that is a real measure, and it moves up or down like anything else worth reporting. That is the standard we hold this work to, whatever you call it. Baseline first so movement is measured against a real starting point. Move the inputs the engines demonstrably use. Report the outcome against your own buying questions rather than a dashboard of impressions. GEO, AEO, the naming genuinely does not matter. What matters is that the discipline underneath is sound, done once, and measured in a way you can trust.