What GEO and AEO mean for a business website

SEO, GEO and AEO describe three different goals for the same website. SEO, search engine optimisation, works to rank a page in traditional search results such as Google. GEO, generative engine optimisation, works to get a page referenced inside an AI-generated summary, such as an AI Overview. AEO, answer engine optimisation, works to get a sentence from your page quoted directly by an assistant such as ChatGPT, Claude or Perplexity. A page built well for one of these gets most of the way to the other two.

The practical difference shows up in how someone finds you. A person searching "web app development company Nigeria" in Google might click your services page from a list of ten blue links. The same question typed into an AI assistant might return a short written answer that names two or three companies, with a link to each. If your page is not written and structured for that second scenario, you can rank well in search and still be left out of the answer.

What is the difference between SEO, GEO and AEO

SEO decides whether your page appears, and where, on a results page a person scrolls through. It rewards pages that match search intent, load quickly and earn links from other sites. GEO and AEO decide whether an AI system reads your page at all, and if it does, whether it quotes you by name or folds your information into an answer with no attribution.

In practice the three overlap. A page that already ranks well for its topic is a page an AI system is more likely to find and trust. GEO and AEO add a layer on top: writing and structuring that page so a language model can extract a clear, accurate answer from it, rather than a generic industry summary pulled from elsewhere. Neither replaces the other; a page with no organic visibility rarely gets cited either, because most AI systems still lean on search results to decide what to read.

What do AI assistants actually cite

Before any of this matters, an AI system has to be able to read your site at all. Many robots.txt files were last edited years before AI crawlers existed, and quietly block them by omission or by an old, over-broad rule. GPTBot, ClaudeBot and PerplexityBot are the three worth checking for by name: PerplexityBot drives citations directly, and GPTBot and ClaudeBot feed the assistants people are increasingly asking instead of typing a search query. Blocking them by default, without a specific reason, is the single most common and most avoidable mistake in this area.

Once a page can be reached, assistants that browse the web, rather than relying only on training data, tend to favour pages that answer a specific question in the first few sentences, use headings phrased the way people ask questions, and carry structured data that removes ambiguity, such as FAQ or Article schema. They struggle with pages where the answer is buried under a long introduction, hidden behind content that has not finished rendering, or spread thinly across several vague paragraphs.

• A clear, declarative first paragraph that answers the likely question
• Headings phrased as questions, matching how people ask them
• FAQ or Article schema marking up the same content in machine-readable form
• Clean HTML that does not depend on client-side rendering to show the text
• A stated author, date and source for factual claims

Why answer-first content matters

An answer-first paragraph states the conclusion before the explanation: what a service is, who it is for, and what someone receives, in plain declarative sentences. This matters to a person skimming a page on a phone, and it matters just as much to an AI system deciding which sentence to lift into its answer. If the key fact sits five paragraphs deep, most systems will summarise around it instead of quoting it directly, and your business becomes background information rather than the named source.

This is also a length discipline as much as a writing style. Every model reads within a fixed context, and a page padded with introductions, stock phrases and repeated calls to action pushes the useful sentence further from the top, where it is less likely to be read in full before an answer is generated.

Publishing an llms.txt file and using schema

llms.txt is a short, plain-text file published at the root of a website, similar in spirit to robots.txt, that lists a site's key pages in plain language for AI systems to read. It is a community convention rather than an official standard, but a growing number of sites publish one because it costs little and gives an AI crawler a map of what matters on the site. Alongside it, FAQPage and Article schema give a machine-readable version of the same content a person reads, reducing the chance of a wrong summary reaching a user.

• Publish a plain-text llms.txt file listing your key pages and what they cover
• Mark up FAQ sections with FAQPage schema
• Mark up articles with Article schema, including a published and updated date
• Keep the file and the schema current as pages change, rather than publishing it once

What to expect realistically

GEO and AEO are young disciplines built on systems that change without notice. There is no assured citation, no fixed ranking to track, and no single tool that proves an assistant has read your page. Treat the work as a foundation: an answer-first page, clean structure and accurate schema make you easier to cite, but whether a specific AI product cites you on a specific day is outside anyone's control. Measure the effect over months, through direct or referral traffic from AI systems where your analytics can see it, rather than day to day.

None of this is a reason to skip the basics. A page that already answers a real question clearly, loads without friction and is honest about what it offers tends to do well across search and AI answers at the same time, because both are ultimately trying to serve the same reader.