What is generative engine optimization?

Why the term exists

For twenty-five years, being findable meant being ranked. A search engine returned ten links, you competed for one of them, and the click carried the visitor to your site, where you told your own story. Search engine optimisation is the whole industry built on that arrangement.

Generated answers break the arrangement in two places. First, the assistant answers in place. It reads the sources, synthesises them, and gives the user a paragraph. Often no click happens at all. Second, the answer names a handful of businesses, not ten. There is no page two to be on.

So a new name was needed for a different competition. You are no longer competing for a rank in a list. You are competing to be inside the sentence the assistant writes. That is what GEO means, and it is why the practice needed its own word. Related labels you will see include answer engine optimisation (AEO) and AI search optimisation. They describe roughly the same work.

One caution about the acronym. In older marketing writing, GEO meant geographic targeting. If a vendor pitches you "GEO services", ask which one they mean.

How do AI assistants build an answer?

The mechanism matters, because everything you can actually change sits inside it. Assistants differ in the details and change their systems often, so treat the following as the general shape rather than a fixed spec. Four stages, in order.

1. Retrieval.When a question needs current or specific information, the assistant does not answer from memory alone. It issues web searches behind the scenes. One user prompt typically becomes several queries, phrased differently from what the user typed. These are usually called fan-out queries. This is the single most important fact in GEO: you are not competing for the user's prompt, you are competing for the searches the machine runs behind it.
2. Sources. The assistant fetches a set of pages from those results and reads them. What it can read is what it can use. A page that needs JavaScript to render its content, or that keeps its key facts inside an image or a PDF, is often functionally invisible at this stage even when it ranks well.
3. Synthesis. The model writes an answer from the retrieved text plus what it already learned in training. Where sources agree, it states things confidently. Where they conflict, it hedges, picks the better-corroborated version, or omits the claim. Consistency across independent sources is therefore doing real work here.
4. Citation. The answer carries links back to some of the sources used. Not every source is cited, and citation is not the same as influence. A page can shape the wording of an answer without appearing in the footnotes. Both matter, and the visible citation is the one you can measure most easily.

We take this apart in detail on how ChatGPT, Claude and Perplexity choose which businesses to recommend, including why an assistant will name your competitor and not you.

GEO vs SEO difference

The short version: SEO is the substrate, GEO sits on top of it. A page an AI assistant cannot find, fetch or parse cannot be cited, so crawlability, speed, clean HTML and genuine topical authority all still matter. Anyone telling you the two are unrelated is selling something.

What changes is the target and the unit of victory:

• You compete for a mention, not a rank. Position ten is worth something in search. It is worth nothing in an answer that names four businesses.
• Much of the surface is not yours.Reviews, directories, forum threads and "best of" roundups are read alongside your own site, and often trusted more, because they are independent.
• Format is leverage. Models lift short declarative sentences, tables and clear headings far more readily than they lift atmosphere. Copy written to persuade a human reader is frequently unusable as source text.
• Measurement is sampled, not ranked. Answers vary between runs, so you measure appearance rates over a fixed prompt set, not a single position.

The full side-by-side, including what carries over unchanged, is on GEO vs SEO: what changes, what carries over.

What actually influences whether you get cited

Nobody outside the labs can rank these factors precisely, and any list that claims to is guessing. What follows is opinionated, based on what we see move answers, and ordered by how often it turns out to be the problem.

1. Being described consistently everywhere. If your site calls you one thing, your Google profile another, and a directory a third, the model has no coherent picture of you and hedges. Freeze one sentence describing what you are and use it verbatim in every profile.
2. Third-party corroboration. Independent pages that mention you carry more weight than your own claims about yourself. Reviews, listicles, local guides, forum threads, integration directories, press. This is the part most businesses have never worked on deliberately.
3. Existing in the exact phrasing people ask.Conversational questions are longer and more specific than keywords. If nothing on the open web pairs your name with "quiet restaurant for a business lunch in Dubai Marina", you will not surface for it.
4. Extractable answer-shaped content. Headings that are real questions, a direct answer in the first paragraph under each, short sentences, tables for comparisons, plain claims a model can lift without interpretation.
5. Technical accessibility to AI crawlers. A separate question from Googlebot access. Check that GPTBot, ClaudeBot, PerplexityBot and OAI-SearchBot are not blocked in robots.txt, and that your content exists in the served HTML rather than being assembled by JavaScript.
6. Structured data and entity markup. Schema.org markup does not force a citation. It does make your facts unambiguous, which helps a model state them confidently rather than avoid them.
7. Freshness and dates. Assistants often bake the current date into their searches. Visibly dated, genuinely maintained pages tend to be favoured for questions where recency matters.

Notice what is not on this list: keyword density, word count targets, and anything resembling a trick. There is no known input that reliably forces a model to name you. There are many inputs that change what it reads.

How do you measure generative engine optimization?

Honestly, and with a stated margin. AI answers are probabilistic. Ask the same question twice and you can get two different shortlists. That is not a flaw in your measurement, it is the nature of the surface, and any tool that reports a single tidy "rank" is hiding it from you.

The method that works:

• Freeze a prompt set. Thirty to fifty real questions your customers would ask, written the way a person actually asks. Freeze it, so month-on-month numbers are comparable.
• Run it repeatedly, across assistants. ChatGPT, Gemini, Perplexity, Claude and Google AI Overviews answer differently. Sample each more than once.
• Record appearance rate, not position. In what share of runs are you named at all? That is the headline number.
• Record share against a named competitor set.Who is absorbing your category's answers, and by how much.
• Record how you are described. Being named incorrectly is its own problem. Log every factual error about your prices, hours, locations or features.
• Record the cited sources. The list of pages driving the answer is the actual work list. It tells you which third-party properties to go and fix.

Crawler hits are a leading indicator, not an outcome. Rising GPTBot and ClaudeBot traffic on a new page means it is being read. Presence in the answers is what counts.

How to start today, for free

You do not need an agency to begin, and we would rather you checked before you talked to anyone, including us. Half an hour, no budget:

1. Write down the five questions a customer would ask an assistant to find a business like yours. Full sentences, with the constraints they would actually include.
2. Ask each one in ChatGPT, Perplexity and Gemini. Do not mention your own name. Screenshot every answer, with the date visible.
3. Count. How many times are you named? How many times is each competitor?
4. Ask a follow-up: "why those?" The sources it lists are the pages currently deciding your category.
5. Ask an assistant directly what your business does, then check the answer for errors. That description is what your prospects are reading.
6. Fix the cheapest thing first. Usually that is a wrong primary category on a Google Business Profile, a menu or price list trapped in an image, or a blocked AI crawler.

If the result is uncomfortable, that is the normal outcome, and it is the reason this page exists. For the mechanism behind what you just saw, read how AI assistants choose businesses. For where this fits with the SEO work you may already be paying for, read GEO vs SEO. For everything else people ask us, there is the GEO FAQ.