Generative engine optimization

What GEO is, and what six AI models actually do

Generative engine optimization (GEO) is the practice of getting a brand accurately described on the sources AI assistants read, so that when someone asks about a category the generated answer has a reason to name it. It optimises toward systems that write one answer instead of returning a list of links.

Almost everything written about GEO cites the same two or three third-party studies. This page does not. We run live checks: real buyer questions, put to six assistants with the web open, every answer and every source kept. What follows comes from 363 recorded answers carrying 5,148 citations, and you can open any of them.

GEO, AEO, AI SEO, LLM SEO: one job, several names

The vocabulary is unsettled because the field is new. These all describe the same work, and each name emphasises a different part of it.

AI SEO: The broad one. Any work aimed at getting a brand into the answers assistants give.
AI search engine optimization: The long form of the same thing, used when people mean search specifically rather than chat.
Generative engine optimizationGEO: The term the industry settled on for optimising toward systems that generate an answer instead of returning links.
Answer engine optimizationAEO: Emphasises the output: there is one answer, and the question is whether you are in it.
LLM SEO: Names the thing doing the reading. Same work, framed around the model rather than the search box.
AI visibility: The measurable side: whether, and how often, a brand actually appears.

One distinction is worth keeping straight, because it splits the search results in two: “AI SEO” is also used for using AI to do your SEO work, which is a different subject. This page is about the other meaning, getting into the answers.

How we measured this

Stated plainly so you can judge it, because most numbers in this field arrive without a method attached.

The questions are not ours. They are built from terms Google Keyword Planner reports real monthly volume for in each category, so they are things buyers type rather than things we would like to be asked.
Asked live, with the web open, to 6 assistants: ChatGPT, Claude, Gemini, Perplexity, DeepSeek and Grok. Not a cached prompt database.
Every answer is kept whole, with the sources the model actually read attached, and published. Nothing here is a summary you have to take on trust.
The numbers recompute as more checks run, so this page cannot drift away from the transcripts behind it. Last recalculated 28 September 2026.

The finding that should change what you do

The industry talks about “optimising for ChatGPT” as though winning there carries you elsewhere. Across 62 questions that more than one model answered, it does not.

22.5

brands named per question, counting every model together.

0.9

of those are named by every model that answered. Fewer than one.

23.3%

average overlap between any two models’ lists of brands.

Ask six assistants the same buying question and they will name roughly 22.5 companies between them, of which under one is common to all of them. Being the answer in ChatGPT tells you almost nothing about Claude, and a tool that checks a single model is measuring a fraction of your exposure.

The engines do not read alike either

How many sources each one consults before it answers, and how many companies it tends to name. Per-answer averages across our corpus.

Engine	Answers	Sources read	Brands named
Grok	48	54.2	7.5
Perplexity	63	21.8	6.6
ChatGPT	63	6.9	5.9
Claude	63	6.8	7.8
DeepSeek	63	3	4.9
Gemini	63	1.9	7.6

And 14% of all answers cited nothing at all. In those the model is working from what it already believes about your category, which is the hardest state to influence and the strongest argument for being described widely rather than perfectly in one place.

What actually moves a brand into an answer

Be described on the sources that category already cites

Citations in our corpus spread across 1,952 distinct domains, and the 20 most-cited hold only 9% of them. There is no short list to buy onto. What works is being accurately described in many places your category is already being read from.

Get into the comparison pages, not just your own site

Roundups and "best X for Y" pages are what an assistant reaches for when asked to name a few companies. Your homepage is the one source in the world that cannot vouch for you.

Say what you sell in the words buyers use

Assistants match a question to a description. A category label that only exists inside your company gives the model nothing to match, however well written it is.

Be reachable by the crawlers that matter

Several assistants use separate agents for training and for live retrieval, and a blanket block at the CDN can quietly remove you from the second. Worth checking before spending anything on content.

Publish something only you can publish

Original figures get quoted, restated ones do not. This page is an example of the tactic it is describing.

Three things this industry keeps repeating

That you can buy a position in a model

There is no ranking, no submission, no placement product inside any of these assistants. Anyone quoting you a guaranteed citation is quoting you something that does not exist.

That llms.txt is a ranking factor

No major assistant has said it reads one, and none of the citations we have collected trace back to a file like this. Publishing it costs nothing. Believing in it costs you the budget you should have spent elsewhere.

That optimising for one model covers the rest

The measured overlap between any two models on the same question is 23.3%. That is the whole rebuttal.

See where you stand, in about a minute

The same check that produced every number on this page, run for your domain. We find the questions your buyers search, ask them live, and show you the unedited answers with your name highlighted if it is in them.

Run the free check

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FAQ

Frequently asked questions

What is generative engine optimization?▾

Generative engine optimization, or GEO, is the practice of getting a brand accurately described on the sources AI assistants read, so that when someone asks about a category the generated answer has a reason to name it. It optimises toward systems that write an answer rather than return a list of links.

Is GEO different from SEO?▾

The work overlaps, the target does not. SEO competes for a position in a list where being tenth still earns a click. A generative engine returns one answer naming a handful of companies, so there is no tenth place. And the signal sits elsewhere: a search engine reads your site to rank your site, while an assistant reads other people’s pages to decide who to mention.

Can anyone guarantee a position in ChatGPT?▾

No. There is no ranking to buy, no submission form, and no placement product inside any of these models. Anyone selling a guaranteed citation is selling something they cannot deliver. What can be changed is the raw material: what the web says about you, on the sources these systems already read.

Does optimising for ChatGPT cover the other models?▾

Our data says no, and this is the most actionable thing we have measured. Asked the same buying question, any two models overlap on only a fraction of the brands they name, and the set of brands every model agrees on is close to empty. A win in one assistant tells you very little about the others.

Does llms.txt help?▾

There is no published evidence that any major assistant reads it, and none of the citations we have collected trace back to one. It costs almost nothing to publish, so it is not harmful, but treating it as a GEO strategy is mistaking a convention nobody has adopted for a ranking factor.

How long does GEO take to show up?▾

Publication takes days. The models reflecting it is slower and depends on how often they recrawl the sources in your category, which is why the only honest approach is to measure, change the inputs, and measure again rather than quote a timeline.