What Is GEO (Generative Engine Optimization)?

Generative engine optimization is the work of getting a business named and quoted inside answers that AI assistants generate, rather than inside a ranked list of links. The outcome is different from a search ranking. When someone asks ChatGPT, Google's AI answers, Perplexity or Claude for a recommendation, they get one synthesized paragraph with a few sources attached. You are either inside that paragraph or you are absent for that question.

The same job travels under several names. Answer engine optimization (AEO), AI search optimization and generative engine optimization all describe the same objective: being the source a model uses when it writes an answer. GEO is the term that has stuck, so that is the one used here.

What makes it a separate discipline is that the unit of competition changed from position to inclusion. Ranking fourth on a results page still earns clicks. Being the fourth-best match for an assistant that names two suppliers earns nothing at all. There is no page two of an AI answer.

The audience is already there. Google's AI answers reached Estonia in May 2025 and now sit above the ordinary search results, so the synthesized paragraph is the first thing a searcher reads. Those numbers are Estonian. The behaviour behind them is not: people ask an assistant for a shortlist before they ever open a results page.

GEO sits on top of SEO. Assistants retrieve from indexes that search engines build, so a page that is not indexed cannot be pulled into an answer, no matter how well written it is. Every honest GEO project therefore starts by confirming the basics work: the page is crawlable, canonical URLs resolve to themselves, and the sitemap agrees with them.

That sounds obvious until it happens to you. On aiasemu.ee, the lawn-care business we own and run with AI, Search Console performance sat near zero for a long stretch. The cause was mundane: every canonical URL, the sitemap and robots.txt pointed at the apex domain while the host served www, so each page declared a canonical that redirected to itself. Google barely indexed the site. Aligning every absolute URL fixed it. Before anything clever, check that your canonical resolves to itself without a redirect.

"Is this the same as SEO?" is the first question we get asked, and the accurate answer is: partly. The overlap is retrieval. The divergence is that an AI answer is written, not listed, so your facts have to survive being read by a machine and restated in one sentence. We covered the failure side of this in why ChatGPT does not recommend your business.

For anything current, an assistant runs a fresh search at the moment it answers, then writes from what it retrieves. It is not reciting a static memory of the web from training time. That single mechanism explains most of what is true about GEO: it is why a technical fix can change your visibility within weeks, and why two people asking the same question minutes apart can get different companies named.

Which index gets searched depends on the assistant. ChatGPT search and Copilot read primarily from Bing's index, while Gemini leans on Google's. This is the practical reason we submit every deploy through IndexNow: it matters for AI visibility, not just for Bing traffic. If your site is well indexed by Google and unknown to Bing, you are missing from a large share of assistant answers for reasons that have nothing to do with your content.

After retrieval comes extraction. The model opens a handful of pages and pulls out statements it can assert with a source behind them. A page that says the price in plain text gets that price restated. A page that puts the same number in a JPEG or a PDF brochure contributes nothing to that answer, because the retrieval step never turned it into text the model could quote.

Identity matters as well. Roughly 7.8% of ChatGPT citations come from Wikipedia and Wikidata-style sources, which is the argument for entity work: a Wikidata item, and consistent sameAs references so the various profiles of your business are understood as one company rather than several similar ones.

The work divides into being retrievable, being extractable, and being corroborated. Our readiness checker tests the first two with eight deterministic checks, no model scoring involved, so the same site produces the same result every time: the page opens for an agent, title and description are present, structured data exists, AI crawlers are allowed in robots.txt, llms.txt is present, prices are machine-readable, an agent can make contact, and an MCP interface is exposed.

The failures we find in the field come in a fairly stable order: prices locked in images or PDFs, no structured data, AI crawlers blocked by a privacy plugin, no llms.txt, and contact paths a machine cannot use. The crawler block is the one worth checking first, because it silently removes you from everything. Our own robots.txt names twelve AI crawlers explicitly: GPTBot, OAI-SearchBot, ChatGPT-User, ClaudeBot, Claude-User, Claude-SearchBot, PerplexityBot, Perplexity-User, Google-Extended, Applebot-Extended, meta-externalagent and CCBot. The default that ships with a plugin varies too much to trust.

Publishing concrete numbers is the highest-value content change most businesses can make. On aiasemu.ee, the query muruniitmise hinnad harjumaal converts at 35.7% click-through in ordinary search, which is our evidence that the winning page shape is geography plus a concrete price. That shape works on assistants for the same reason it works on people: it answers the question completely, so it can be quoted whole.

Beyond the page, we run infrastructure an agent can act on and not only read: llms.txt with the full offer and prices, an MCP server exposing a readiness check and a contact tool, and an open lead API whose GET response describes its own schema, so an agent that finds the URL never has to guess the format. None of that comes out of a connector platform, which is the same line that separates what Zapier and Make can cover from what has to be built. That layer is covered in AI back-office automation for SMEs.

Ask blind questions and count mentions. The two-phase method we use in our deep check is to classify the business first, what it sells and where, and only then ask the assistant the question a customer would ask, with no brand name in it. Repeat the question, because answers vary. Record a mention rate, not a single result.

The gap this exposes is larger than most owners expect. We tested a Pärnumaa holiday house whose Google visibility is good enough that strangers email asking who did its SEO. We asked Google's AI three times, blind, for holiday-house recommendations in Pärnumaa. It was named zero times out of three. Competitors were named in all three answers. Nothing was wrong with its rankings; the business simply was not in a form the answer layer could use.

The same method run on aiasemu.ee returned two blind Gemini mentions out of three. That is a number worth acting on, because it moves and can be re-measured after each change. Set it beside the readiness score, which is deterministic and tells you what to fix, and you have both halves of the picture: aiside.ee scores 95/100 on that checker and aiasemu.ee 93/100.

No supplier can guarantee that an assistant will recommend you, and anyone who promises it is selling air. The output is generated per question, varies between users, and changes when a model is updated. What can honestly be sold is the loop: measure how often you are mentioned now, fix the things that raise the odds, measure again. If a proposal you are reading contains the words guaranteed and ChatGPT in the same sentence, that tells you what you need to know about the supplier.

Timelines split in two. Technical fixes, crawler access, structured data, prices as text, an llms.txt, tend to show up within weeks, because assistants search fresh each time they answer. The other half, being mentioned and reviewed on sources the models trust, is slower and takes months, because it depends on other people publishing about you.

Attribution is the weakest part of the whole field, and we say so. A visitor who arrives with no referrer and works through several pages in one session did not come from organic search, and that pattern is consistent with a link out of an AI answer or a chat app. Consistent with is not the same as proof. We report it in exactly those terms, and we would treat any agency reporting AI-sourced revenue to the euro with suspicion.

One more admission, because it is the kind of thing a vendor normally hides: our own price check currently warns on aiside.ee, caused by how the framework encodes the euro sign in its server-rendered output. We publish the score with the warning in it. A checker that always says everything is fine is not a checker.

Start by measuring the site, because the fixes are cheap and the diagnosis is free. Run the free readiness check against your domain and read the eight results in order. A typical Estonian business site scores under 50. Most of the missing points come from changes that need no redesign: a robots.txt that stops blocking AI crawlers, prices moved out of images into text, structured data that matches what the page actually says.

Then run the blind test yourself before hiring anyone. Ask an assistant for a recommendation in your category and location three times, without your name in the question, and write down how often you appear and who appears instead. It costs nothing and gives you a baseline no supplier can massage.

From there, the paid ladder is short and the prices are published: 290€ for an audit that goes through the site point by point, with the fee credited against follow-up work; 990€ to implement the fixes on an existing site, about a week of work; from 3 999€ for a new build that people and agents can both read; and 590€ a month for ongoing work with a monthly report on how often AI recommends you.

Publishing those numbers is itself part of the method, and it is why the automation side of the business carries a published answer to what an AI automation project costs and what drives the number. In our own market, most agencies answer the cost question with a contact form. That is exactly why they are absent when an assistant is asked to compare suppliers: there is no number on the page for a model to quote. Whatever your business sells, the fact a customer would ask about first should exist as plain text on a page a crawler can reach.