What Is GEO and AI Visibility: Getting Into ChatGPT Answers

GEO (generative engine optimization) is the work of getting ChatGPT, Gemini, Perplexity, and the AI answers in Google and Yandex to find a company’s website, describe its product accurately and link to its pages. AI Visibility is a brand’s visibility in those answers and the set of metrics used to measure it. GEO doesn’t replace SEO: AI systems need the same accessible, clear and corroborated sources.

This article is for founders, marketers and product managers at IT companies in Kazakhstan who notice that customers increasingly show up saying “ChatGPT recommended you” and want to understand what to do about it. We’ll cover how GEO differs from SEO, what is known about how sources are chosen, which actions actually help, how to measure results and where to start.

What GEO and AI Visibility are

The term generative engine optimization comes from a 2023 research paper by Aggarwal and co-authors, published at the KDD 2024 conference. The authors described generative systems as a new kind of search engine: they gather information from several sources and write a ready-made answer. In this setup, a website owner has less control than in classic search results, and GEO was proposed as a way to increase content visibility inside those answers.

In practice, several terms coexist: GEO, AEO (answer engine optimization), LLMO, AI SEO. The differences between them are mostly marketing. We use two:

• GEO is the work: site accessibility, page structure, product facts, external corroboration.
• AI Visibility is the result and how it’s measured: whether the brand is mentioned in answers, how accurately it is described, which pages are cited and how stable the picture is.

For an IT product, this matters at the selection stage. A SaaS buyer asks an assistant to find a service for a specific job or to compare several options. If the product isn’t in the answer, or it’s described with errors in features, pricing and markets, the customer may never reach the website.

How GEO differs from SEO

SEO and GEO solve a similar problem in different interfaces. In search results, the user sees a list of pages and chooses where to go. In an AI answer, they get ready-made text in which the brand may be mentioned, cited with a link or not mentioned at all.

	SEO	GEO and AI Visibility
Where results show up	Google and Yandex search results	Answers from ChatGPT, Gemini, Perplexity, Copilot, AI answers in search
Unit of success	Page ranking and click	Brand mention, accuracy of description, page citation
What you work on	Query and landing page	The user’s question and all sources the answer is assembled from
Role of external platforms	Links to the site	Mentions and facts in directories, reviews, profiles, media
Stability	Rankings change gradually	The answer may differ from run to run
Measurement	Rankings, impressions, clicks in search engine tools	Repeatable measurements against a question set, partly webmaster tools
What they share	Accessibility, indexing, content quality, source trust

The key point of the table is in the last row. If a site is poorly indexed, has weak pages and no external corroboration, separate GEO work won’t change much.

How AI assistants choose sources

Vendors don’t publish exact source selection algorithms, and any “secret factors” should be treated with skepticism. But some things are known from official documentation and research.

An answer from the model or from search. An AI assistant can answer from knowledge acquired during training or search the web at the moment the question is asked. In the second case, it relies on a search index and shows links to sources. Work on your site has a greater effect on the second mode.

Google relies on its own search. According to Google’s documentation, AI Overviews and AI Mode use Google’s search systems and index. For a page to appear in these answers, it must be indexed and eligible to be shown in search with a snippet. Google lists no additional technical requirements.

A question is broken into sub-queries. Google describes a technique called query fan-out: the system runs several related searches across subtopics and sources. That’s why a page can make it into an answer not for the main query but for an adjacent subtopic: limitations, comparisons, local context.

Each system has its own bot. At OpenAI, the OAI-SearchBot crawler is responsible for sites appearing in ChatGPT search, while GPTBot collects data for training models. These are independent settings: you can allow the first and block the second. Perplexity has its own search crawler, PerplexityBot.

Answers are unstable. The same question, a rephrasing of it and the date it’s asked can yield different sources. That’s why one manual query with the brand name tells you nothing about real visibility.

Citation doesn’t equal influence. A link to a page in an answer doesn’t mean the answer is built on that page’s facts. Conversely, a brand’s facts can make it into an answer through a third-party review rather than the brand’s own website.

What helps you get into AI answers

Sustainable actions are more boring than promised “hacks,” but they are what works, and they strengthen SEO at the same time.

Site accessibility

Check that important pages are open to search bots and AI crawlers in robots.txt, aren’t blocked by bot protection on your CDN and are indexed in Google and Yandex. Key text should be in the HTML, not appear only after JavaScript runs or sit behind a form.

Direct answers and structure

It’s easier for AI systems to use a page where a fact is stated directly and placed right next to the question. Start each section with a short answer: what the product is, who it’s for, how it’s different, where it works. Definitions, lists of features and integrations, and comparison tables are written as standalone fragments that make sense without the rest of the text. Marketing language without specifics is hard to cite, and then the product description gets pulled from other people’s reviews.

FAQ and schema.org markup

An FAQ block written in customers’ own words covers the sub-queries a user’s question is broken into. Schema.org markup (Organization, Product or SoftwareApplication, FAQPage, Article) helps systems understand what’s on the page. But it’s not a magic factor: Google states plainly that no special markup is needed for AI features, and any markup must match the visible text.

The llms.txt file

llms.txt is a format proposed in 2024: a Markdown file in the site root with a name, a short description and a list of links to the main pages for AI agents. You can add one; it’s cheap. But Google doesn’t treat such files as a requirement for its AI features, and without good pages the file does nothing.

Mentions on external platforms

For many products, the AI answer is built not on the company’s own website but on directories, reviews, company profiles, documentation, industry media coverage and discussions. Independent corroboration carries more weight than repeating the same claim on your own pages. You need up-to-date profiles, a presence in relevant directories and expert publications.

Up-to-date and consistent facts

A common cause of inaccurate answers is inconsistent facts: one set of prices on the site, outdated ones in a directory, a third set in a review. The name, category, features, markets, city and contacts must match everywhere. A dedicated page with prices and pricing logic is useful both to the customer and to AI: we covered the approach to pricing in tenge in our article on SaaS pricing in tenge.

Action	Why	What to expect
Open the site to search and AI crawlers	Without access, a page won’t make it into the assistant’s search	A prerequisite, not growth in itself
Direct answers at the start of sections	A fact is easier to find and cite	A more accurate product description
FAQ and schema.org markup	Cover sub-questions, help systems understand the page	A supporting effect, not a guarantee
llms.txt	Give AI agents a map of the main pages	An addition, not a substitute for content
External mentions and profiles	Independent corroboration of facts	The most impactful and the slowest block
Consistent and current facts	Remove the causes of inaccurate answers	Fewer errors in the brand description

How to measure AI Visibility

You can’t check AI visibility with a single query. You need a repeatable measurement.

1. Prompt set. Collect the questions a buyer asks before they know your brand: how to solve the problem, which services exist, how they differ, what works for Kazakhstan. Sources include search demand data, sales and support inquiries, and customer conversations. For B2B, add separate questions from the business owner and from the technical specialist.
2. Baseline. Capture the answers of several AI systems for this set, asking each question several times. Don’t change the wording or the test conditions after that.
3. Metrics. For each answer, record whether the brand is mentioned, in what position, whether it is described accurately, which competitors appear alongside it and which sources are cited.
4. Repeats. After changes to the site and external sources, repeat the measurement and compare the trend, not a single result.
5. Link to the business. Look at branded search, direct visits, referral traffic from AI services in your analytics, and inquiries where the customer mentions an AI assistant.

Official tools are also emerging. In 2026, Bing Webmaster Tools launched an AI Performance report: the number of times a site is cited in Copilot answers and Bing AI summaries, the cited pages and the queries for which the system found the content. It’s a good benchmark, but it doesn’t replace the picture for other systems.

GEO in Kazakhstan: two languages, Google and Yandex

In Kazakhstan, users search on Google and Yandex, and ask questions in Russian, Kazakh and sometimes English. Each combination of system and language can produce its own visibility picture.

• Two search engines. Google and Yandex have different indexes and their own generative answers, and Yandex also has the Alice assistant. Indexing and page quality matter in both.
• Russian and Kazakh. It’s worth building the prompt set in both languages. There are generally fewer Kazakh-language sources online, and answers may be built on different pages than in Russian. The Kazakh and Russian versions of the site should contain the same facts.
• Local questions. Buyers ask whether the service works in Kazakhstan, whether it has a Kazakh interface and how payment is accepted. If the site has no direct answer, the assistant will take it from a third-party source or won’t answer at all.
• A small market. The number of queries in a B2B category may be small, but the decision is often made by the business owner, and an accurate description in a single answer matters for the deal.

Kazakhstan context. The set of AI features in Google and other systems varies by country and language and changes over time. Check answers the way a user in Kazakhstan sees them, in Russian and Kazakh, rather than relying on reviews from other markets. For Kazakhstan brands, separately verify the data in map profiles, in 2GIS and in IT company directories: inconsistencies in the name, city or line of business lead to confusion with similarly named companies.

We described how AI visibility fits into the overall channel mix of a SaaS product in our article GTM for SaaS in Kazakhstan. For mobile apps, an additional source of facts is the store listings in two languages; more on that in our article on launching a mobile app in Kazakhstan.

Where to start: a plan for the first month

Week	What to do	Outcome
1	Define the reference facts about the product: name, category, who it’s for, markets, integrations, pricing logic. Collect 20–40 customer questions in Russian and Kazakh	Brand fact sheet and prompt set
2	Capture the answers of several AI systems with repeats, write down mentions, errors and cited sources	Baseline and a list of inaccuracies
3	Check robots.txt, crawler access and indexing; rewrite the openings of key pages as direct answers, add an FAQ and markup	Fixed pages
4	Update profiles and directory listings, plan external publications, add llms.txt, keep a dated change log	Backlog of external corroboration and a date for the repeat measurement

If you don’t yet know what questions customers ask, start with search demand analysis: this step is described in detail in our article on validating demand for an IT product.

Typical mistakes at the start: checking one convenient question with the brand name, expecting results within a week, buying “placement in ChatGPT” and creating dozens of thin pages for every phrasing of a question. Google specifically warns that mass-producing pages to manipulate search violates its policies.

How we do it

Gotomarket works with IT products: SaaS, web services, mobile apps and IT agencies. We start AI Visibility work with a reference brand fact sheet and a prompt set built from real buyer questions in Russian, Kazakh and English. We capture a baseline across several AI systems, build a source map and a list of inaccuracies, then hand off the backlog for execution: pages and markup on the site, profiles and directories, expert publications. After an agreed interval, we repeat the measurement with the same question set. We test the method on our own products.

No one can guarantee placement in the answers of third-party systems, and we don’t promise it. We remove the reasons why a product doesn’t appear in answers or is described inaccurately, and we connect visibility to search and inbound leads. Learn more on the SEO and AI Visibility page.

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Sources. Google Search Central: AI features and your website; OpenAI: Overview of OpenAI crawlers; Aggarwal et al. GEO: Generative Engine Optimization (arXiv); llms.txt; Bing Webmaster Blog: AI Performance in Bing Webmaster Tools.

Terms in plain words

GEO (generative engine optimization)

The work of getting generative AI systems to find, accurately describe and cite a company’s website. An extension of SEO for interfaces that give ready-made answers.

AI Visibility

A brand’s visibility in AI assistant answers and how it is measured: mentions, accuracy of the description, cited pages, stability. It shows whether buyers see the company at the selection stage.

Prompt set

A fixed set of questions that the audience asks. AI system answers are captured against it regularly so results can be compared over time.

Baseline

The first visibility measurement that all later ones are compared against. Without it, you can’t tell whether changes to the site had any effect.

Query fan-out

A technique in which AI search breaks a user’s question into several related sub-queries. That is why a page can make it into an answer through an adjacent subtopic.

Schema.org

A shared markup vocabulary that a site uses to tell search engines what is on a page: an organization, a product, an article, questions and answers.

llms.txt

A file proposed in 2024, placed in the site root, with a short description and links to the main pages for AI agents. An addition, not a mandatory standard.

Crawler

A program that crawls websites and collects pages for search or for training models. If a crawler is blocked, the pages won’t make it into the corresponding system.