What Is GEO?

GEO (Generative Engine Optimization) is the optimization discipline that makes a brand visible, and cited as a source, in generative AI answer engines such as ChatGPT, Gemini, Perplexity and Google AI Overviews. The word "generative" is the point: these systems do not hand you a list of links. They write the answer themselves and pick a few sources while they write it.

In classic SEO the goal was a ranking, and even the tenth result collected some traffic. In GEO the goal is a place inside the written answer. Most answers hold two or three sources; the ones that make it in are visible, the ones that do not are simply absent. That is why GEO is not an extension of ranking tricks. It is the discipline of being answerable.

A research team from Princeton and IIT Delhi coined it in a paper posted to arXiv in November 2023 and presented at KDD 2024, the ACM's data mining conference. The paper measured how changes to a page, such as adding citations, statistics and quotations, changed how often generative engines used it. The name stuck within a year.

The practice is older than the name. Agencies were already figuring out how to get brands into AI answers in 2023, and we were one of them; we called all of that work AIO and still do. Google's own guidance now names GEO and AEO together and says the work is still SEO, which is a fair description of where the field settled.

SEO gets you ranked in a list of results. GEO gets you into the answer an AI writes. The difference shows up in three places. The metric changes: rank and clicks in SEO, mentions and citations in GEO. The unit changes: SEO judges the whole page against a keyword, GEO judges a single passage, sometimes a single sentence, on whether it can be lifted into an answer.

The arena changes too: a results page has ten spots, an answer has two or three. So the bar is higher in GEO and the payoff is bigger. But the two are layers, not rivals: a site that cannot be crawled and indexed cannot be cited either. The six-point table above walks through the rest.

They overlap more than they differ, and most of the time they describe the same work. AIO is our umbrella: strategy, measurement, test cycles and authority building in one frame. GEO is the field under it that faces generative engines: being a source in what ChatGPT, Gemini, Perplexity and AI Overviews write. AEO is the industry's name for optimizing for answer engines, and Google's guidance treats AEO and GEO the same way and calls both of them SEO.

The confusion has a specific cause: Google's AI Overviews feature is also shortened to "AIO" in the industry. Our AIO is the name of a methodology; Google's is a feature. We counted how often each term gets used, with Google Trends data, in a separate article linked in the sidebar.

No. GEO is built on top of SEO. Before an answer engine can cite a page it has to find it, crawl it and read it. Google's AI Overviews draw directly on Google's index, and ChatGPT runs a web search when it needs current information. A page that is not indexed reaches neither of them.

Getting this right means running two layers at once: SEO for the Google ranking, GEO for the citation in an answer engine. Google's own guidance says the same thing in one line: optimizing for generative AI search is still SEO. That is also why we do not sell GEO as a separate service; it runs inside the SEO work.

In order, without skipping the foundation. First, the technical foundation: content readable in raw HTML and an open door for AI crawlers. Then answerable content: every important page gives the answer in its first paragraph and covers the customer's sub-questions (time, cost, conditions, exceptions) under their own subheadings.

Then identity: Organization and Person schema, and the same brand facts everywhere. Then authority: being named in the right context by the publications your industry trusts, through real mentions rather than paid lists. Measurement comes last: the generative AI report in Search Console and a regular test of your own target questions. The part that belongs to you as the owner is the seven questions in the section above.

On two levels. First, technical readiness: raw HTML access, bot permissions, answerability, structured data. These are yes-or-no questions and take a week to check. Second, real visibility: is your brand mentioned in AI answers, is it described correctly, and for which questions?

For the second level there is finally an official tool. The generative AI report in Search Console shows your impressions in AI Overviews and AI Mode, and it has been open to every property since August 31, 2026. It does not show clicks or queries; you cover the query side by testing your own target questions the same way every month. Fill out the form on this page and our team will check where your brand stands today.

Google's guidance says plainly that you do not. There is no special file and no special format for appearing in AI Overviews; the two requirements are that the page is indexed and eligible for a snippet. We have no evidence that other engines read llms.txt either, and we have not seen a study that shows it.

It does no harm. Publish one if you like, but keep it at the bottom of the list and do not let it take time away from content. The real signal is the content itself: a page that answers in the first paragraph, covers the sub-questions and stays consistent. A site without llms.txt but with the answer gets cited. The reverse does not happen.

First, the wrong sentence is not invented; the system read it somewhere. Find where. Does the description on your site agree with your Google Business Profile, your directory listings and your press coverage? Is an old page still live? Is a closed location still listed somewhere? Most errors about a brand come from those mismatches.

Then fix the source: update your Organization schema, redirect stale pages, close or correct the directory entries. On Google, use the feedback link under the summary; it does not act immediately, but it is recorded. A consistent source is the only way the wrong sentence gets corrected the next time the answer is generated. The system does not correct itself.

ChatGPT knows you from two places. The first is its training data: it knows your brand the way the texts it learned from described it. For that you need consistent mentions in authoritative publications, trade media and trusted directories; it does not happen overnight, it accumulates. The second is live web search: when a question needs current information, ChatGPT searches at that moment and cites the pages it finds.

The second path has two conditions: GPTBot has to be able to reach your site, and your page has to give the answer in the first paragraph. A closed door cancels both, so check robots.txt first. If you run behind Cloudflare, know that its default setting blocks AI crawlers, and check it.

A fixed timeline would be dishonest, and you should be careful with any offer that gives you one. But you can break it into levels. Technical readiness improves fast: opening the door, adding schema and rewriting a first paragraph are a week's work, and the effect can show up in Google's AI Overviews within a few weeks.

It takes longer for a model to really learn your brand and get in the habit of citing it. That builds over months, with your content and your authority. Getting into ChatGPT's training data waits for the next model version. That is why you take the first measurement early: six months from now you can only see what changed if you know where you started.

Yes, and that is exactly where the window of opportunity sits. Answer engines do not look for the biggest brand, they look for the best answer. The measurements bear this out: roughly a third of the domains cited by AI Overviews do not even appear in the first-page results for the same query. A site that cannot reach page one can still be cited if it gives the right answer to the right question.

The small site's advantage is focus: a niche topic, a question customers actually ask, an answer written from experience. With clean structured data, consistent details everywhere and the answer in the first paragraph, a small site can be cited ahead of competitors many times its size. Most large sites are not doing this work yet, which is why the window is open.

No, and nobody can. The system writing the answer is not ours, and it decides which sources to use every time it runs. It can show you one day and your competitor the next for the same question. That uncertainty is the nature of the system, not a gap in our work.

What we commit to is specific and measurable: checking off the signals one by one, measuring visibility on your target questions every month with the same method, and reporting what we find, as is, in good months and bad. Treat any offer that guarantees AI rankings with caution: either they are not measuring, or they are not showing you what they measured.

The base is the same, with two differences. First, data: when an answer engine describes your product it also states the price, the stock status and the delivery time, and it will quote a stale figure with the same confidence as a current one. Keep price and stock current, mark them up with Product schema, and let category pages answer "which one is right for whom." A product page alone rarely gets cited; a buying guide does.

Second, the horizon: agent shopping protocols have been arriving since late 2025, and the agent does not just find and compare the product, it places the order. There is no single standard yet, but your product feed should already be in a form a machine can read. The details are in our ecommerce SEO guide.