What is generative engine optimisation (GEO)?

What is generative engine optimisation (GEO)?

Generative engine optimisation is the practice of getting a company named and recommended inside the answers AI tools such as ChatGPT, Gemini and Perplexity give to buyers. Where search engine optimisation aims for a ranking in a list of links, GEO aims to be the name the model recommends.

The term covers everything that influences whether an AI system mentions you. That includes the content the models read and the structured signals describing each provider. It also includes the references across the wider web that establish you as a credible source. The goal is a single measurable outcome. When a buyer asks AI for a recommendation in your category, your name is in the answer.

That outcome matters because of where the buying decision now starts. A founder evaluating compliance software, or a head of operations comparing platforms, increasingly opens an AI chat before they open a search engine. According to G2's 2026 buyer study, more than half of B2B buyers now begin supplier research with AI chatbots rather than Google. The shortlist forms inside that conversation.

How GEO works: the in-chat shortlist

A buyer opens an AI tool and asks which provider is best for their situation. The model returns a name, then two or three more. That short list becomes the buyer's starting point, and it formed without a single visit to anyone's website. If your company was not in the answer, the comparison happened in a conversation you had no part in.

This sits outside conventional analytics. Nobody landed on your site to be counted, so the introduction your competitor just received never shows up in a traffic report. The same category question gets asked many times a day, each one quietly handing the first introduction to whoever the model decided to name.

AI tools name the companies they have seen referenced most often and trusted most consistently. They weigh the content available about a category alongside the structured data describing each provider, then factor in how that provider is referenced across the wider web. A company that appears clearly and credibly across those signals becomes the name the model reaches for. A company that is absent from them does not get mentioned, regardless of how well its own website ranks in classic search.

This is why ranking and being recommended are separate jobs. You can hold the top organic position and still be missing from the answer that sits above the results, because the work that earns a ranking is not the same work that earns a citation in a generated answer.

GEO and AEO: the naming explained

Two terms describe closely related work, and the overlap causes confusion. Generative engine optimisation and answer engine optimisation are best understood as one discipline with two emphases rather than two separate practices.

Term	What it focuses on	The buyer outcome
Generative engine optimisation (GEO)	Being the recommended name in generative AI output	AI names your company when asked who to use
Answer engine optimisation (AEO)	Being the source an answer engine extracts and cites	AI pulls your content as the answer to a question
Search engine optimisation (SEO)	Ranking in a list of links	Your page appears high on the results page

In practice, the same foundations support both GEO and AEO: content that answers real buyer questions, clean structured data, and credible references. The difference is what you are optimising toward. GEO targets the recommendation. AEO targets the extracted answer. For most B2B teams the distinction is academic, because the programme that earns one tends to earn the other. You can read a fuller treatment in our guide to answer engine optimisation and how it differs from GEO.

How to do GEO: the four-step method

Getting named in AI answers follows a repeatable sequence. Each step builds on the last, and the whole thing runs as a programme rather than a one-off project, because the questions buyers ask and the answers models give both keep moving.

Step one: audit

Ask AI the questions your buyers ask, and record exactly where you stand. Which questions name you, which name a competitor, and which sit open with no clear leader. This produces a baseline score for your named-in-answer rate across the questions that matter to your category. It also reveals the open questions, the ones where no provider yet owns the answer and the position is there to claim.

Step two: content

Build the content the models draw on. This means material that answers real buyer questions directly, written in the format AI tools pull from when they form a recommendation. A clear answer near the top, content that stands on its own when lifted out of context, and structure the models can read reliably. This is the half of the work that gives the model something credible to cite. For the detail on what makes content citable, see our guide to getting cited in Google AI Overviews and AI answers.

Step three: citation building

Earn the references and structured signals that teach models to trust your name. AI tools weigh how often and how credibly a company is referenced across the web, so the work here is establishing your company as a recognised source rather than an unverified claim on a single page. Done well, you start to appear as a cited source rather than an afterthought.

Step four: re-scoring

Re-run the question set on a monthly cycle and score the movement question by question. Models update, buyers ask new questions, and a competitor can surface in an answer you used to own. Monthly re-scoring catches the change while it is still small and shows you the position climbing, with a clear record of where you started and where you stand now.

Who needs GEO?

GEO matters most to B2B companies whose buyers research suppliers before talking to sales, which describes most considered purchases in software, professional services and regulated industries. If your category is one where a buyer would reasonably ask AI for a recommendation, the answer is forming whether or not you are in it.

The case is strongest in categories where few competitors have started. The discipline is new enough that most markets still have open questions, and the company that gets named first tends to stay named, because the model settles harder on whoever it sees referenced most often. Being early is the advantage, and it narrows every month.

A worked example shows the shape of it. A UK digital identity platform that we worked with grew its monthly reach in AI tools from roughly 39,000 to 2.9 million over six months, and won a seven-figure contract directly through AI search. The category had been wide open. The programme claimed it.

Where Forge fits

Forge Together runs generative engine optimisation as a managed programme for regulated B2B companies. We make AI recommend you, using the four steps above: we audit where you stand today, build the content the models draw on, earn the citations that teach them to trust your name, and re-score your position every month so you can watch it climb.

You can see how the whole programme fits together in our B2B GEO playbook, which walks through the operating model end to end.

Frequently asked questions

What does GEO mean in marketing?

In marketing, GEO means generative engine optimisation: the practice of getting a company named and recommended inside the answers AI tools give to buyers. It targets the recommendation an AI returns when someone asks who to use, rather than a position in a list of search results.

Is GEO the same as SEO?

No. SEO aims to rank a page high in a list of links so a buyer clicks through. GEO aims to get your company named inside the AI answer that often sits above those links. They share foundations such as clear, structured content, but the job and the metric differ. We cover the distinction in detail in is GEO the same as SEO.

What is the difference between GEO and AEO?

Generative engine optimisation and answer engine optimisation describe one discipline with two emphases. GEO focuses on being the recommended name in generative output. AEO focuses on being the source an answer engine extracts and cites. The same content and citation work usually supports both.

Is GEO real, or just hype?

GEO addresses a measurable shift in how buyers research. G2's 2026 buyer study found that most B2B buyers now open an AI chatbot before Google for supplier research, and that a large share changed their choice of vendor on the strength of AI guidance. The discipline is new, but the behaviour driving it is documented.

Who needs generative engine optimisation?

B2B companies whose buyers research suppliers before contacting sales benefit most, particularly in software and other regulated, considered-purchase markets. If buyers in your category would plausibly ask AI for a recommendation, GEO determines whether your name is in that answer.

See where you stand

Book a discovery call and free AI-visibility audit at forgetogether.agency/GEO. 30 minutes. No pitch deck. We'll run your category live and show you exactly what AI says about you today.