SEO and AI Visibility → AI Visibility & GEO

AI Visibility and GEO

We measure how AI systems find, describe and recommend your brand: prompts, mentions, citations, sources, entity signals and business metrics.

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Overview

AI Visibility: what it is

More and more buyers start their research with a ready-made AI answer. GEO doesn't replace SEO: AI systems still need accessible, consistent and verified sources. We measure where you stand today and identify which facts and surfaces need strengthening.

Scope: brand and category
Measurement: prompts · mentions · citations
Foundation: verifiable sources
Output: baseline and backlog

In plain words

What it is and why it matters

What it is: We check what ChatGPT, Gemini and other AI assistants say about your category and brand, and strengthen the sources they draw inaccurate facts from.
Who it is for: A SaaS product or IT studio whose buyers choose solutions through AI assistants, while the product is missing from answers, described inaccurately or confused with another.
What you get: Real buyer questions, a first measurement of AI answers, a map of the sources they cite, a list of fixes on and off your site, and a re-measurement.
Where to start: Send us your product name and the questions clients ask before buying. We start with an audit of AI answers and suggest what to fix first.

Solution architecture

How the work is structured with the 5D methodology

We start with a set of questions and record a baseline so changes can be compared over time.

Discover

Build the prompt set

We capture questions about the category, comparisons, vendor choice, limitations and local context.

Design

Define the metrics

We set metrics for discoverability, mentions, accuracy, citations, stability and the target action.

Deploy

Check AI answers and sources

We capture answers, citations and wording and compare them with your website, profiles and external sources.

Drive

Fix facts and surfaces

We strengthen entity signals, sales pages, expert content and data consistency.

Dominate

Measure again

We compare against the baseline, track stability and link changes to search and business metrics.

Unit economics and ROI

How GEO impacts business

Visibility only matters if it helps a user move from an answer to checking out the brand, visiting the website or getting in touch. That's why we don't stop at counting mentions.

The work reduces the risk of outdated or inaccurate descriptions and strengthens sources that serve search, AI and customers at the same time.

What it protects: accurate brand description
What we measure: presence and actions
What we strengthen: sources and entity signals
Cadence: baseline and re-measurement

Result

What you get

You get measurement, a source map and growth priorities.

Prompt set for commercial scenarios
AI Visibility baseline
Mention and accuracy analysis
Map of cited sources
Review of the brand's entity signals
Backlog of pages and external proof
Re-measurement metrics
Recommendations for linking with SEO and analytics

Comparison of approaches

GEO or classic SEO

The channels overlap, but they show your brand in different interfaces.

Approach	Focus	Limitation	Output
SEO	Pages in search results	Doesn't show ready-made AI answers	Organic visibility
Brand monitoring	Mentions in sources	Doesn't check AI answers	Reputation landscape
AI Visibility & GEO	Answers, sources and accuracy	No guaranteed placement	Baseline and priorities

Limitations and risks

What we don't promise

AI answers change and depend on the system, the model, the user and the sources available.

Guaranteed mention

You can't control a third-party AI system's output the way you control an ad placement.

One universal prompt

You need a set of real scenarios, not a test of one convenient question.

A technical file instead of sources

llms.txt alone doesn't replace an accessible website, facts and trust.

A good fit

When this service fits

Customers research the category through AI
The brand is described inaccurately or inconsistently
You need to understand where recommendations come from
SEO and content are already at a basic level

Not a fit

When to choose another approach

The website has no clear facts about the product
The brand has almost no presence in open sources
You need a guaranteed mention
The audience's commercial questions are not defined

Terms in plain words

GEO: work on brand visibility in generative AI answers
Prompt set: a set of questions for repeatable measurement
Citation: a source an AI answer refers to
Entity signal: a consistent fact linking the brand, product and market
Baseline: the initial measurement for future comparison
AI Visibility: How often and how accurately AI assistants mention a brand when answering buyer questions about a category, comparisons or vendor choice.
Discoverability: A metric showing whether the brand appears in AI answers to questions about a task or category that do not name it.
llms.txt: A file on the website with a short description for AI systems. It can help, but does not replace real pages and external proof.

For IT products

GEO optimization and AI visibility for IT products in Kazakhstan

For SaaS, web services and IT studios, GEO optimization means working on how ChatGPT, Gemini, Perplexity and other AI assistants describe the product, whom they compare it with and which sources they cite when a buyer chooses a solution.

SaaS in comparison answers

SaaS buyers increasingly ask an AI assistant to pick a service for a task or compare several options. We check whether the product appears in such answers, whether its features, integrations, markets and pricing logic are described correctly, and which competitors it is placed next to. We also look at local availability questions: does the service work in Kazakhstan, does it have a Kazakh interface, how does it accept payment.

Web services and marketplaces

For web services, an AI answer is often built on external reviews, directories and forums rather than on the service's own site. We map the sources cited in answers and find where facts about the service are outdated or contradict each other. Then we strengthen the official pages and profiles so the AI system has an unambiguous and current source of data about the service.

Mobile apps

AI assistants answer questions about mobile apps based on store listings, reviews and the product website. We check whether the name, category, key features and platforms match across all these places, and whether the app is mentioned in answers about the task, not only about the brand. Inconsistent names and descriptions are a common reason why an app is not recognized or is confused with another.

IT studios and integrators

When a client asks an AI who develops products or implements CRM in Kazakhstan, the answer depends on how clearly the company is described in open sources. We check whether the brand, services, stack and city are linked into consistent entity signals, whether there is confirmation on third-party platforms, and whether the studio is confused with companies with similar names.

How it works

How GEO optimization and ChatGPT promotion work in practice

Promotion in ChatGPT and other AI assistants is not ad placement but work with sources: we measure what AI says about the category and the brand, find the causes of gaps and inaccuracies, and strengthen the facts the answers rely on.

A prompt set from real buyer questions

We start not with the brand but with the questions a buyer asks before they know it: how to solve the task, what services exist, how one differs from another, what suits Kazakhstan. We collect them from search demand, sales and support requests and conversations with clients. The prompt set covers questions about the category, comparison, vendor choice, limitations and local context, in Russian, Kazakh and English. A typical mistake is testing one convenient question containing the brand name: it almost always gets a good answer and says nothing about real visibility. For B2B products we add questions a business owner would ask and those a technical specialist would ask, since they lead to different sources.

Baseline: what we record and how

We run the prompt set across several AI systems and record for each question whether the brand is mentioned, in what position, whether it is described accurately, which competitors appear alongside and which sources are cited. AI answers change from run to run, so each question is checked several times and we assess stability rather than a single result. The artifact is a baseline table with discoverability, mention, accuracy, citation and stability metrics. All later measurements are compared with it, so question wording and test conditions are kept unchanged. We also record whether the AI system searches the web when answering or relies only on the model, as source work affects these modes differently.

Source map and entity signals

For each answer we find where the AI gets its information: the company website, directories, reviews, company profiles, documentation, media publications. We compare brand facts (name, category, features, markets, city, team) across all sources and identify discrepancies. These are the most common cause of inaccurate descriptions or confusion with other companies. For Kazakhstan brands we also check that data match across the Kazakh and Russian versions, map listings and 2GIS, and IT company directories. The result is a source map with prioritized fixes. We also note which website pages AI cites most often, as these are updated first.

Strengthening the site and external proof

Based on the analysis we build a backlog: which pages to create or rewrite, which facts to make unambiguous, where external confirmation is needed. On the website, these are product and solution pages with clear definitions, integration pages, pricing logic, FAQ answers, Schema.org markup and consistent organization data. Off the website, they are up-to-date profiles, directories, documentation and expert publications. An llms.txt file can complement this work but does not replace accessible pages and proof. Everything we do for AI also strengthens SEO and helps the client who is checking the brand. Fixes that remove inaccuracies in answers to commercial questions get priority.

Re-measurement and business impact

After changes we rerun the same prompt set and compare the result with the baseline: where the brand appeared, where the description became more accurate, which sources started being cited. AI systems update models and indexes unevenly, so we judge changes by trend, not by a single snapshot. We link visibility to business metrics: branded search, direct visits and inquiries in which the client mentions an AI assistant. In Kazakhstan's small B2B market, where the business owner often makes the decision, an accurate description in an AI answer matters even with a small number of queries. We test the method on our own products finspot.kz, salemdom.kz and developers.kz. If the changes have no effect, we go back to the source map and check which confirmations are still missing.

Pages AI can cite

AI assistants are more likely to cite pages where the needed fact is stated directly and sits next to the question. So we restructure key pages to open each section with a short answer: what the product is, who it is for, how it differs, where it works. Definitions, feature and integration lists, comparison tables and FAQ answers are written as self-contained fragments that make sense without the rest of the text. We check that content is accessible without JavaScript and not hidden behind forms. On bilingual sites the Kazakh and Russian versions must contain the same facts. A typical mistake is marketing copy without specifics: it is hard to quote, so AI takes the product description from third-party reviews.

How we work

How AI visibility work is done: step by step

AI visibility work follows five practical steps, from collecting audience questions to re-measurement. At each step it is clear who takes part on the company side and what deliverable it leaves behind.

Kick-off meeting and product facts

We meet the business owner or marketer and the product owner. We record the official facts: the correct product name, its category, who it is for, which markets it serves, what it integrates with. We collect the list of competitors and alternatives clients compare you with. The output is a reference brand profile against which AI answers will be checked. If facts already differ between the website and profiles, we note it right away and add it to the fix list.

Collecting questions from sales and support

Together with sales and support we collect the questions clients ask before buying and add search demand data. Questions are grouped by scenario: category, comparison, vendor choice, limitations, local context. We agree on the languages and AI systems to test. The output is an approved prompt set used unchanged in all later measurements. For B2B products we add separate questions from a business owner and a technical specialist.

Measuring and reviewing answers

We collect answers to the prompt set from the selected AI systems with several repeats and compile them into a table. For each answer we note the mention, accuracy, competitors and cited sources. The results are reviewed with the client team to separate real errors from debatable wording. The output is a baseline, a source map and a prioritized list of inaccuracies. Answers are saved in full so wording can be revisited later.

Fixes on and off the site

The backlog goes into work: pages and markup are edited by the client team or our specialists, profiles and directories are updated by the person responsible on the company side, expert publications are prepared with the experts. Tasks are tracked in a shared task tracker such as Plane or Jira. The output is fixed pages, updated profiles and a dated change log for later comparison. Changes to the Kazakh and Russian site versions are made at the same time.

Re-measurement and next-step decision

After an agreed interval we repeat the measurement on the same prompt set and compare the result with the baseline, taking the change log into account. We show where descriptions became more accurate, which sources started being cited and where gaps remain. Together with the business owner we decide whether regular monitoring is needed or one-off work is enough. The output is a trend report and a plan for the next cycle.

Engagement formats

AI visibility and GEO optimization formats

AI visibility work starts with an audit and a baseline, after which we decide whether source fixes and regular monitoring are needed. The scope depends on the number of commercial scenarios, languages and AI systems.

Format	What is included	Timeline	Outcome
AI Visibility audit	Prompt set, baseline of AI answers, mention and accuracy analysis, map of cited sources, entity signal check	First stage of work	A clear picture of how AI systems see the brand today and a list of the main gaps
Fixing facts and sources	Backlog of pages and confirmations, site improvements, markup, profile and directory updates	After the audit	Accurate and consistent brand facts on the site and in external sources
Regular monitoring	Repeat measurements on a stable prompt set, change report, SEO and content recommendations	Ongoing cycle	Visibility trends against the baseline and early signals of inaccuracies
GEO together with SEO	A single demand map, commercial pages and expert content useful for both search and AI answers	After the diagnostic	Pages and sources that work both in search results and in AI answers

We choose the right format during a free basic diagnostic, once scenarios, languages and the state of sources are clear.

Frequently asked questions

AI Visibility & GEO: questions

Can you guarantee that a brand will appear in ChatGPT?

No. We can improve how accessible, accurate and well-supported your sources are, but we can't control a third-party system's answer.

Do we need an llms.txt file?

It can be a technical add-on, but it doesn't replace quality pages, structure and external proof.

How often should we measure?

After significant changes to sources, and regularly on a stable prompt set to see trends.

Is GEO only for big brands?

No. A local or niche company can also be a relevant answer if its facts are accessible and verified.

How is GEO different from SEO?

SEO works with pages in search results, while GEO deals with how a brand appears in ready-made answers from AI systems: whether it is mentioned, how accurately it is described and which sources the answer cites. The channels overlap, because AI systems rely on the same accessible and verifiable pages. That is why GEO does not replace SEO but builds on it.

How can a SaaS product get into ChatGPT and other AI assistant answers?

Facts about the product — category, features, integrations, markets and pricing logic — must be described clearly on the website and confirmed by external sources such as directories, reviews, profiles and documentation. We check which category questions users ask, what AI systems currently answer and which confirmations are missing. A mention cannot be guaranteed, but the reasons a product is left out or described inaccurately can be removed.

How do you measure brand visibility in AI answers?

We define a set of real audience questions — about the category, comparisons, vendor choice and constraints — and regularly capture the answers to them. We check whether the brand is mentioned, how accurately it is described, which sources are cited and how stable the result is. The first measurement becomes the baseline for all later comparisons.

What should we do if an AI assistant describes our product incorrectly?

First we find the source of the inaccuracy: an outdated page, conflicting facts between the website and profiles, old reviews or the lack of an official description. Then we fix and strengthen the sources — commercial pages, documentation, profiles and external confirmations. After the changes we measure again using the same set of questions.

What does an AI Visibility audit include?

A set of questions for commercial scenarios, a baseline of AI answers, analysis of mentions and accuracy, a map of cited sources and a review of the brand's entity signals. The output is a backlog of pages and confirmations, metrics for repeat measurement and recommendations on linking the work to SEO and analytics.