What is Generative Engine Optimisation (GEO)?
Search is no longer a list of ten blue links. A growing share of questions are now answered directly (by ChatGPT, by Google’s AI Overviews, by Gemini, Perplexity and Copilot) in a single synthesised paragraph that may never send the reader to a website at all.
That shift has created a new discipline. Generative Engine Optimisation (GEO) is the practice of making a brand, person or organisation visible, accurately represented and cited inside those AI-generated answers. It is sometimes called Answer Engine Optimisation (AEO) or, loosely, “LLM SEO”. The names differ; the problem is the same.
Why GEO is not just SEO with a new name
Classic search engine optimisation is about ranking a page. GEO is about being retrieved, trusted and quoted by a model that is composing an answer from many sources at once.
The two overlap (clean, well-structured, authoritative content helps with both) but they reward different things:
- SEO optimises a page to win a position in a ranked list a human then scans.
- GEO optimises the wider information environment so that when a model assembles an answer about your category, it reaches for your facts and attributes them to you.
A page can rank well and still be invisible inside AI answers. Conversely, a brand can be cited confidently by an answer engine without owning the top organic result.
What answer engines actually reward
There is no published ranking formula, but the behaviour of the major engines is consistent enough to plan around. In practice, the content that gets retrieved and cited tends to share a few traits.
- Direct answers, stated early. A clear, self-contained answer near the top of a page is far easier for a model to lift and attribute than the same point buried in paragraph nine.
- Structure a machine can parse. Descriptive headings, short definitional sentences, lists and tables, and clean semantic HTML all make a page easier to extract.
- Corroboration across sources. Models are more confident about a claim that appears, consistently, in several independent places: your own site, third-party coverage, reference sources and structured data.
- Evident expertise and provenance. Named authors, citations, dates and a credible publisher signal trustworthiness, and trust is what an engine is implicitly grading when it decides whose version of a fact to repeat.
The accuracy problem
GEO is not only about presence. It is about correctness.
Because answer engines synthesise rather than quote, they can state things about a company that are outdated, conflated with a competitor, or simply wrong, and present them with complete confidence. For a regulated business, a listed company or anyone in a contested situation, an authoritative-sounding but inaccurate AI answer is a reputational exposure in its own right.
Managing that means treating the model’s view of you as something to be measured and corrected: understanding what the engines currently say, identifying where the wrong inputs are coming from, and improving the accuracy of the sources the models rely on. We cover the practical side of this in What to do when ChatGPT gets your company wrong.
Where GEO sits alongside SEO and PR
GEO does not replace search engine optimisation or public relations. It sits between them and depends on both. Traditional SEO keeps your own pages discoverable and well-structured, which gives answer engines clean material to retrieve. PR earns the third-party coverage that models treat as corroboration. GEO is the layer that ties them together and points them at a new target: not a ranking position, but a citation inside a synthesised answer.
In practice the three disciplines share inputs but optimise for different outcomes. A single press feature can be a PR win, an SEO signal and a GEO citation source at once, but only if it states the facts an engine needs in a form it can extract. The teams that do this well stop treating the three as separate budgets and start treating them as one information environment.
Common GEO mistakes to avoid
A few patterns reliably waste effort:
- Writing for the ranking, not the answer. Keyword-stuffed pages that once ranked can still be ignored by an engine that simply wants a clean, quotable statement of fact.
- Ignoring the source layer. Optimising only your own site, while the reference databases and third-party profiles an engine trusts stay wrong, leaves the inaccurate version of you in place.
- Treating it as one-and-done. Answers drift as models update and competitors publish. Without standing re-measurement, early gains quietly erode.
- Chasing every engine equally. Effort should follow your audience (the engines your buyers, candidates and stakeholders actually use) rather than spreading thin across all of them.
How to start
A workable GEO programme usually moves through three stages:
- Measure. Establish what the major engines currently say about you and your category, and where those answers come from. You cannot improve a position you have not baselined.
- Build. Create and restructure content so the answers you want are stated plainly, supported by evidence, and easy for a model to extract and attribute.
- Reinforce. Strengthen the surrounding signals — third-party coverage, reference-source accuracy, structured data and knowledge-graph integrity — so the model’s confidence in your version compounds over time.
None of this is a one-off. The engines change, the answers drift, and competitors are working the same surface. GEO is closer to an ongoing build-and-optimise cycle than a project with an end date.
How Morris McLane runs this
For us, GEO is a measurable, repeatable build — not a content guess. It is the core of our AI search visibility work, and it runs as a standing cycle rather than a one-off project.
We start by measuring. We run a fixed set of priority questions through ChatGPT, Google’s AI Overviews, Gemini, Perplexity and Copilot on a regular cadence, capture the answers and citations, and baseline where you appear, where you are missing, and where the engines are simply wrong about you.
From there the work splits across the layers that actually move an answer. On owned content, we restructure pages so the facts you want are stated plainly and are easy for a model to extract and attribute. At the source layer, we correct the third-party profiles, reference databases and structured data the engines lean on, and we build the corroborating citation network that makes a model confident in your version. Then we re-measure against the baseline, so gains are visible and drift gets caught before it erodes.
The short version
If SEO was about being found, GEO is about being quoted correctly. As more decisions begin inside an AI answer rather than a search results page, the brands that win are the ones an engine trusts enough to cite — and accurate enough to cite well.
That is the work: making sure that when an answer engine speaks about you, it speaks accurately, and credits you for it.
Frequently asked questions
What is the difference between GEO and SEO?
SEO optimises a page to rank in a list of results a person then scans. GEO optimises the wider information environment so that when an AI engine composes an answer, it retrieves your facts and attributes them to you. They overlap (clean, authoritative content helps both) but they reward different things.
Is GEO the same as Answer Engine Optimisation (AEO)?
In practice, yes. GEO, AEO and the looser term "LLM SEO" all describe making a brand visible and accurately represented inside AI-generated answers. The names differ; the discipline is the same.
Which AI engines does GEO cover?
The major answer engines: ChatGPT, Google's AI Overviews, Gemini, Perplexity and Copilot. Each retrieves and cites sources slightly differently, so a sound programme weights effort towards the engines whose answers matter most to your audience.
How do you measure GEO when the engines don't publish rankings?
By observation. You run a fixed set of priority questions through each engine on a regular cadence, capture the answers and citations, and track presence, accuracy and source coverage over time against a baseline.
Does GEO work compound or reset when models update?
It compounds. Models retrain by re-ingesting the web, so structural work — accurate reference sources, structured data, citation networks — carries forward into each cycle rather than resetting.
