What does the AI visibility audit analyse?

It shows whether your brand appears in AI answers, in what context and how it compares with competitors. We study a set of queries linked to your offer, your customers' problems and the buying process, and then analyse the answers, the recommended brands, the citations and the sources.

Which AI systems does the audit cover?

The usual set is ChatGPT, Gemini, Google AI Overviews, Perplexity and Microsoft Copilot. The final scope depends on your market and industry. A brand can be visible in one system and almost invisible in another, so each is analysed separately.

How do you choose the questions and prompts?

We start from your business model, offer and customers' decision process, not from a list of keywords turned into questions. We consider your main products and services, the problems they solve, audience groups and their knowledge, buying stages, questions from sales and support, how competitors present their offers and the business value of each topic.

Is a single ChatGPT answer enough to judge visibility?

No. One answer is an observation, not a measurement. The same brand can appear and disappear between queries, depending on wording, model, context, language, region and sources. That is why we use a fixed protocol and report the share of answers that contain your brand.

Does the audit include competitors and cited sources?

Yes, both. We compare you with the competitors you name and with brands the AI starts naming on its own, and we analyse the domains and pages the models cite where the system shows them. This shows which companies are recommended most, for which questions, and which external sources could support your growth.

What do I get when the audit is finished?

A report with the methodology, the analysed queries, your share and your competitors' share, the context of your presentation, cited domains and pages, inaccuracies, a map of topic and source gaps, and priorities with recommendations. It ends with a roadmap and a way to measure the effect.

Is it a one-off study, and what if my brand does not appear?

The first audit is your starting point. The next measurement makes sense after you put the recommendations in place or as part of monitoring, and how often depends on how fast your industry, publications, competitors and offer change. Keeping the method constant is what makes results comparable. If your brand does not appear, the audit points to the content and source gaps to close first.

How much does an AI visibility audit cost?

There is no single rate. The price depends on the number of AI systems, prompts and intent categories, the number of competitors, markets and languages, and whether you want a one-off audit or recurring measurement. Contact us with your market and goals for a quote.

AI Search

AI visibility audit: how AI answers present your brand

An AI visibility audit shows whether your brand appears in answers from ChatGPT, Gemini, Perplexity and Google AI Overviews, in what context, and how that compares with your competitors. It also shows which sources the models cite and the queries where you lose visibility.

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What is an AI visibility audit?

An AI visibility audit is a study of whether your brand is named in answers from AI systems, for which questions it appears and how it compares with competitors. We do not just check a URL. We assess whether the brand is recognised, recommended, compared, cited or passed over, and we set the results against your Google and paid-campaign data.

People increasingly discover products and ask for recommendations in generative systems such as ChatGPT, Gemini, Perplexity, Microsoft Copilot and Google AI Overviews. Your brand can be the category leader, one option among many, or missing altogether. The audit answers a set of plain questions. Does AI recognise your brand and offer? For which questions do you appear? Which competitors are named more often? How is your brand described? Which sources shape the answers? Where are the biggest gaps? It is the first step of our generative engine optimisation work.

From the right questions to a map of your brand's visibility

Instead of a single prompt, we build a representative set of queries, test the answers of different AI systems and show where your brand is present, who wins the recommendations and what could help.

Scope: questions, topics and competitors
We analyse your offer, category and audiences, set the key user intents and build a representative set of prompts split by buying stage. We choose direct and indirect competitors and agree which AI systems the study covers.
Measurement: answers, recommendations and sources
We count the answers that name your brand and your position in recommendation lists. We check the context in which your offer is presented, your share of visibility against competitors, the domains and pages the models cite, and any incorrect or outdated information.
Recommendations: visibility gaps and an action plan
We show the queries where competitors win, the missing content and pages, the updates your brand information needs, and the external sources that could raise your visibility. You get priorities by business potential, a roadmap and a way to measure results.

Why one prompt proves nothing

A single conversation with ChatGPT or Gemini is an observation, not a measurement. The same brand can appear and disappear between two queries. The wording, the model, the context, the language, the region and the sources all play a part. People also ask in different ways: for a list of companies, for a comparison, or by describing a problem. Three similar questions can return different answers and different sources.

In classic SEO, a page has one position. An AI answer is closer to a distribution, so the first step of an audit is to set the measurement method. A screenshot from one conversation is not proof of visibility.

How we make the results comparable

The measurement protocol is fixed before the start and does not change between audits. Two rules stay constant: the same set of questions returns in every measurement, and the result is the share of answers that contain your brand, not a single yes or no. The protocol defines:

the AI systems and their versions
the list of prompts, split by intent
how many times each question is repeated
the language, market and region
the rules for counting a brand as mentioned and as recommended
a fixed list of competitors
how answers and sources are recorded
the interval between measurements

Four dimensions we read together

Presence alone is not enough to decide what to do. We look at four dimensions side by side.

Dimension	The question	Why it is not enough alone
Presence	How often does your brand appear?	It does not say how the brand is presented.
Context	How is it described: as a leader, a cheap option, a niche choice, a weaker alternative?	It does not show the source of that image.
Sources	Which sites and profiles does the model base its answer on?	It does not say whether your brand wins.
Competition	Which competitors take your place?	Without the rest, it does not point to actions.

The types of questions we test

The aim is to find where on the decision path your brand drops out of view. We choose questions from your business model, offer and customers' decision process, not by turning keywords into prompts.

Problem

The user describes a need and does not yet know the solution.

Educational

A request to explain a technology or a category.

Recommendation

A search for a recommended company or tool.

Comparison

A side-by-side of brands, methods or suppliers.

Purchase

Price, implementation, availability, choosing a solution.

Brand

Questions about a specific brand, product or expert.

Not only whether, but how AI describes your brand

A mention alone is not a good result. A brand can be described as niche, expensive, a poor fit for a given customer or a random example at the end of a list. For every mention we check whether the description of your offer is accurate, whether the model understands your main specialisation, which customers it assigns you to, which competitors it sets you against and on which side, what arguments it gives for choosing you, and whether the answer rests on current data.

Models draw on scattered, sometimes outdated or contradictory data. Typical errors include an old description of the offer, an outdated price list, a wrong location, services you do not provide, or confusion with a similarly named company. The audit points out the errors and their likely source. Clean-up starts with your own website, then profiles and directories, and finally sources outside your control. Changes on your site will not alter answers immediately. AI systems have their own cycles, so this is months of work, and the first effect is removing contradictions between sources.

What the audit report contains

A good report shows the situation, the gaps and the actions of highest value, not a pile of screenshots.

the methodology and scope
the list of analysed queries
the share of answers naming your brand and your competitors
the most frequently recommended companies
an assessment of the context in which your brand is presented
a summary of cited domains and pages
the inaccuracies we found
a map of topic and source gaps
priorities and recommendations

How to read the result

A high share of mentions does not always mean a good business result. Your brand may appear for general questions rather than for choosing a supplier. A rarer but positive presence on precise purchase questions can be worth more. We judge results by the business value of the query, the buying stage, the type and quality of the recommendation, the visibility of direct competitors, the accuracy of the description and your ability to influence the sources.

After the audit: what happens next

The audit is a diagnosis, not the end of the process. We turn the findings into a plan for your website, content, brand information and external sources. That can include missing service and product pages, updated company information, content that answers important questions, expert and author pages, better technical accessibility, valuable publications and cleaner external profiles.

Where the audit shows content gaps and a brand missing from sources, the next step is AI search optimisation. Where visibility is already built and needs to be kept, the same protocol runs on a cycle as AI visibility monitoring.

The audit does not replace classic SEO. Models rely on content that has to exist, be accessible and be cited, and that is still SEO work. Our SEO audit asks a different question: why a page does not rank.