What is GEO (generative engine optimisation)?

Generative engine optimisation (GEO) is the practice of making your product likely to be named, described accurately, and recommended inside the answers that generative AI systems produce. A generative engine is any tool that reads a question and writes a synthesised answer rather than returning a list of links: ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews are the common examples. GEO is the work of understanding how those systems assemble an answer, then arranging your content, your presence across the web, and the facts about your product so that you are part of the answer when someone asks what software solves their problem.

If search engine optimisation (SEO) is about ranking a page so a person clicks it, GEO is about being included in an answer the person never clicks away from. The destination changed, so the optimisation target changed with it. This page defines GEO precisely, explains the mechanism that makes it possible, and shows where it sits next to SEO and AEO so you know what to actually do.

How generative engines build an answer

To optimise for a generative engine you have to know how it decides what to say. There are two mechanisms at work, and most modern systems use both at once.

Parametric recall: what the model already learned

A large language model is trained on a very large body of text. During training it absorbs patterns, associations, and facts into its parameters, the internal weights that make up the model. When you ask a question, the model can answer partly from this stored knowledge without looking anything up. This is parametric recall. If your product was discussed widely and consistently across the web at the time the model was trained, the model may already associate your name with a category and recall it when relevant.

Parametric recall has two limits worth understanding. It is frozen at the training cut-off, so a model will not know about a product that launched after it was trained. And it reflects volume and consistency: a product mentioned often, in the same terms, in places the model trained on is more likely to surface than one mentioned rarely or described inconsistently. You cannot edit a model's parameters, but you can influence what the next version of it learns by being present and consistent in the text it will train on.

Retrieval: what the model fetches at answer time

The second mechanism is retrieval. Many generative engines run a search at the moment you ask, pull back a set of current sources, and write the answer grounded in what they just fetched. Perplexity and Google AI Overviews lean heavily on retrieval. ChatGPT and others retrieve when the question looks like it needs fresh or specific information. This is why a newly launched product can appear in an AI answer even though no model was trained on it: the engine found a page about it at answer time and cited it.

Retrieval is the part of the system you can influence quickly. The engine fetches from the live web, so a clear page, an accurate third-party mention, or a relevant community thread can be retrieved and pulled into an answer within days rather than waiting for the next training run. GEO works on both mechanisms, but retrieval is where most of the near-term progress is made.

What generative engine optimisation actually targets

Because the answer is assembled from recall and retrieval, GEO is not one tactic. It is a set of conditions you arrange so that, whichever path the engine takes, it arrives at an accurate picture of your product. Three things matter most.

• Retrievable, unambiguous content. Pages that state plainly what your product is, who it is for, what category it belongs to, and what it does. The engine has to be able to fetch a page and extract a clean claim from it. Vague or purely promotional copy gives the engine nothing concrete to repeat.
• Corroboration across independent sources. Engines weight a fact more heavily when several sources agree on it. A claim that only appears on your own site is weaker than the same claim echoed in a review site, a directory, a comparison article, and a community thread. GEO cares about your presence across the wider web, not just your domain.
• Consistent facts and framing. If your homepage, your directory listings, and third-party write-ups describe you differently, the engine has conflicting inputs and may pick the wrong one or omit you. Saying the same true thing the same way everywhere is a quiet but real advantage.

Notice what is absent from that list: keyword stuffing and link-count games. Those targeted an older ranking system. GEO targets a comprehension system. The question is not whether a crawler counts a keyword, it is whether a model can read your material and correctly summarise what you do.

GEO, SEO, and AEO: how they relate

These three terms describe overlapping work aimed at different surfaces, and the distinctions matter because they change what you measure.

SEO optimises for traditional search engines that return ranked links. The unit of success is a ranking position and the click it earns. The reader leaves the engine and lands on your page.

AEO, answer engine optimisation, optimises to be the direct answer to a question: the featured snippet, the voice-assistant response, the boxed answer at the top of a results page. The unit of success is being the single chosen answer, often with no click at all.

GEO optimises to be included in a generated, synthesised answer that may weave together several sources and several products. The unit of success is being named, described accurately, and recommended inside that answer. GEO inherits much of the technical groundwork of SEO, because a page that retrieval engines can fetch and parse cleanly is the same page that traditional crawlers handle well. The difference is the audience being optimised for: a model assembling prose, not a crawler ranking links. In practice the three reinforce each other, and a healthy discoverability programme does all three at once rather than treating them as rivals.

Why this matters for a post-launch founder

A growing share of people researching software now ask a generative engine before they ever open a search results page. They describe their problem in plain language and read the answer they get back. If your product is not in that answer, you are invisible at the exact moment someone is deciding what to use, and you have no link, no impression, and no way to know it happened. The traditional analytics that show you a ranking drop do not show you an answer you were left out of.

For a solo or small-team founder the encouraging part is that GEO rewards clarity and consistency more than budget. A product described precisely, corroborated across a few independent sources, and present in the communities where its users already talk has a genuine chance of being pulled into answers, regardless of company size. The harder part is that it is steady, distributed work across many surfaces, and it is easy to lose track of where you stand.

What to do next

Start by finding out what the engines actually say about you. Ask ChatGPT, Claude, Gemini, and Perplexity the questions your customers would ask, in the words they would use, and read the answers honestly. Note whether you appear, whether the description is accurate, and which competitors are named instead. That baseline tells you whether your problem is recall, retrieval, or simply that the facts about your product are scattered and inconsistent. From there the work is concrete: publish clear pages that state plainly what you do, earn accurate mentions in the directories and communities your users read, and keep the facts consistent everywhere they appear.

If you would rather start from a measured baseline than a manual audit, AfterLaunch runs a free Growth Snapshot that scores your discoverability across seven dimensions, including how you currently show up in AI answers, so you can see where you stand before you decide what to fix first.