How to run an AI visibility audit

You rank nicely in Google, your traffic is stable, and yet you lose deals to players you barely know. Increasingly, that is because the buyer no longer started in Google, but in ChatGPT or Perplexity. That is where a shortlist was put together, and you were not on it. So the question is no longer only “do I rank?”, but “does AI see me?”. An AI visibility audit answers exactly that question.

In this article I treat the audit as a format in its own right, not as an extra line at the bottom of your classic SEO report. Because the signals that determine whether an AI model cites you partly overlap with SEO, but certainly not entirely. You need a structured approach that examines four layers separately: crawlability, entities, citations and schema. Do you first want to understand the broader foundation? Then read what SEO exactly is and how it works. This audit zooms in on one thing: how AI processes your brand today.

Why a separate audit is needed

A classic SEO audit measures whether Google can find, index and rank your pages. That remains the basis. But an AI search engine does something else with your content: it reads, summarizes, combines sources and formulates an answer in which it names one or two brands. You do not need to be number one in the classic results for that, but your content does have to be readable, understandable, credible and machine-interpretable.

That is why an AI visibility audit works with different metrics than positions and clicks. You measure whether a model reaches your content, whether it grasps who you are and what you have authority on, whether you are already mentioned in AI answers, and whether you give technical signals that machines process easily. Four layers, each with its own check.

Layer 1: crawlability for AI bots

Everything starts with the question of whether an AI system can reach your content at all. Many teams only discover during an audit that they are unintentionally blocking AI crawlers. So start with your robots.txt and look at which user agents you allow or refuse. Bots like GPTBot, ClaudeBot, PerplexityBot and Google-Extended each have their own rules. A block there simply means your content is not included in training or retrieval processes.

Then check whether your most important pages render server-side. Content that only appears after heavy JavaScript execution is, for many retrieval systems, invisible or incomplete. Also look at loading speed, redirect chains and orphan pages. A handy cross-check can be found in your crawl statistics in Search Console: if you see there that even Google struggles to fetch everything, then AI bots certainly have a harder time. The result of this layer is a list of pages that are technically unreachable or hard to read for machines.

Layer 2: entities and who you are

Being crawlable is not enough. An AI model also has to understand who you are, what you do and why it would trust you. That revolves around entities: your brand, your people, your services and the topics on which you claim authority. Models build a kind of knowledge picture from all the sources where you appear, not only from your own site.

In this layer you check whether your entity is consistent and recognizable. Do you describe your company everywhere in the same way? Is it clear which problems you solve and for which audience? Do you have substantive depth around your core themes, or does everything stay superficial? Also look at external signals: mentions, profiles and references in places that models often consult. A strong entity emerges from coherence between your site, your website architecture and the way others talk about you. The gap you find here is almost always the same: the brand is indeed active, but nowhere sharply and consistently defined, which makes a model guess or simply skip you.

Layer 3: citations and factual visibility

Now comes the core: are you already being mentioned today? This layer is your real baseline measurement. You put together a set of realistic questions that your ideal customer would ask, and for each question you record which brands the AI names and which sources it cites. Do that per platform, because ChatGPT, Google AI and Perplexity behave differently and draw on different sources.

Note for each question whether your brand appears, at which position, and whether you are cited directly or only carried along as a source behind an answer. Look just as closely at the competitors who are mentioned: which of their pages are cited, and what do they have that you are missing? This makes it visible whether the problem lies in missing content, in a lack of authority, or in a format that is hard to cite. For a deeper, structured development of this measurement you can turn to the complete GEO audit, which further deepens this citation work. The result here is an honest picture: a selection rate and a citation share per platform, instead of a feeling.

Layer 4: schema and machine-readable context

The last layer makes it as easy as possible for an AI to interpret your content correctly. Structured data via schema.org tells machines explicitly what something is: an organization, a product, an article, an author, a frequently asked question. That reduces guesswork and increases the chance that your content is summarized and attributed correctly.

Check that you have the basics in order: organization and website markup, author data, and where relevant FAQ or product markup. Look at whether your titles and summaries are unambiguous, because a good title and meta helps not only Google but also every model that has to summarize your page in one sentence. Pay attention to clear headings, short and citable paragraphs, and factual statements that hold up independently of their context. The gap in this layer is usually technical and quick to close, which makes it an ideal starting point after the audit.

From audit to a prioritized action list

The four layers together deliver a list of gaps. The pitfall is wanting to solve everything at once. Instead, prioritize by impact and effort: a blocked crawler or missing basic schema is fixed quickly, while building a strong entity and citable content takes months. In addition, tie every gap to a commercial goal. A mention on a question that has nothing to do with purchase intent weighs less than visibility on the questions your best leads ask.

That is exactly the difference between chasing visibility and building pipeline. At Customer Impact we treat SEO and AI visibility as one acquisition layer of the same growth engine: we make sure you are found and cited, and always toward revenue instead of vanity numbers. Do you want to spar with an seo consultant who runs this audit for you and ties it to your growth goals? Then feel free to schedule a call.

Start with your baseline measurement

An AI visibility audit does not have to be a months-long project. You start with an honest snapshot of where you stand today, in four layers, and you translate that into a short action list. The most important thing is to fix a baseline, so that you can later prove real progress.

Do you want us to run the audit together and tie it directly to your pipeline? Get in touch and we will make your AI visibility concretely measurable.