What is Generative Engine Optimization (GEO)? A practical guide

When someone asks ChatGPT "who builds computer vision systems in Dubai?", the answer is a short paragraph with two or three names in it. There is no page two. Either you are in that paragraph or you are not.

Generative Engine Optimization (GEO) is the practice of making your business one of the names an AI engine gives. It overlaps with SEO, but it is not the same discipline, and treating it as "SEO with extra steps" is the most common way to waste a budget on it.

What an AI engine actually does before it answers

Most of the engines that matter today, ChatGPT with search, Perplexity, Google AI Overviews and Gemini, work in roughly the same way:

1. Retrieve. The engine runs one or more web searches for the question, often rewriting it several ways. It pulls a handful of pages, usually fewer than ten.
2. Read. It extracts passages from those pages. It does not read the page the way a person does. It looks for self-contained chunks that directly answer the question.
3. Synthesize. The language model writes an answer from those passages and attaches citations to the sources it leaned on.

Every GEO tactic that works maps onto one of those three steps. Anything that doesn't is theatre.

Step 1: Being retrievable

You cannot be cited if you are never retrieved. This is where classic SEO still matters: a crawlable site, a sitemap, fast pages, a clear title on every page. But two things are specific to AI engines.

Let the AI crawlers in

OpenAI, Anthropic, Perplexity and Google each run their own crawlers (GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot, Google-Extended). A surprising number of sites block them by default, either through a strict robots.txt or a firewall rule that treats them as bots. If you block them, you have opted out of the answer.

Publish an llms.txt

llms.txt is a plain-text file at the root of your domain that summarises who you are, what you do and which pages matter. It is a proposed convention rather than a formal standard, and not every engine reads it yet. It costs twenty minutes, it never hurts, and it gives you one place to state the facts you want repeated. Ours is here if you want a template.

Step 2: Being extractable

This is the part most sites get wrong. AI engines cite passages, not pages. A page that is beautiful but vague loses to a page that states a plain fact in one sentence.

• Answer the question in the first sentence under the heading. "PPE detection uses a camera and an object-detection model to check that workers are wearing helmets, vests and gloves" is citable. "Safety is our passion" is not.
• Use real numbers. "Trained on 118,950 Dubai building permits from 1998 to 2025" gets quoted. "Trained on a large dataset" does not. Engines prefer specifics because specifics are what make an answer look trustworthy.
• Name the entity. Say "Software Stem, an AI development company in Dubai" on the page, in words, not just in a logo. The model needs the string it will later type.
• Keep sections short and self-contained. A 90-word block under a descriptive H2 is the ideal unit. If a passage only makes sense with the paragraph above it, it will be extracted without that paragraph and lose.

Step 3: Being trusted enough to cite

Given five retrieved pages that all answer the question, the engine picks the ones that look most authoritative. Three signals matter here.

Structured data

JSON-LD markup (Organization, ProfessionalService, FAQPage, Article) tells the engine what the page is and who is behind it without guesswork. FAQ markup in particular maps almost one-to-one onto the question-and-answer shape of an AI response.

Entity consistency

Your name, address, services and founder should be described the same way on your site, LinkedIn, Google Business Profile and any directory listing. Contradictions make the model hedge, and a hedged answer usually drops the citation.

Third-party mentions

An engine that sees you mentioned on pages you don't control (a client's case study, a directory, a news piece, a GitHub README) treats you as a real entity rather than a self-description. This is the slowest lever and the strongest one.

What GEO is not

• It is not keyword stuffing for robots. Language models are better at detecting filler than Google ever was.
• It is not a one-off audit. Engines re-retrieve on every query. Visibility drifts as competitors publish, so it needs measuring monthly.
• It is not a replacement for SEO. Retrieval still runs on a search index. A site that doesn't rank on Google will rarely be retrieved by ChatGPT either.

How to measure it

Pick the 15 to 20 questions a buyer would actually ask an assistant ("best AI development company in Dubai", "who can build a face-recognition attendance system", "computer vision company UAE"). Ask each engine every month. Record whether you are mentioned, whether you are cited with a link, and who else appears. That table is your GEO scorecard. We built AIGEO to automate the site-side half of that audit.

Where to start this week

1. Check your robots.txt for AI crawlers and unblock them.
2. Add Organization and FAQPage JSON-LD to your homepage.
3. Rewrite the first sentence under every service heading so it answers "what is this?" in plain words.
4. Put one real number on every project or case-study page.
5. Publish an llms.txt.

Those five changes take a day and cover the majority of what we see missing when we audit a site for the first time. Everything after that is content, consistency and patience.