How to Run an AI Visibility Audit for Your Brand in One Afternoon

Most teams have no idea how they look inside AI answers. They rank fine on Google, assume that carries over, and never check. It does not carry over. You can find out exactly where you stand in an afternoon with nothing but a spreadsheet and access to the major AI engines. Here is the process we use before starting any program.

Step 1: Build a buyer-question prompt set

Do not test vanity prompts like "what is [your brand]." Test the messy, unbranded questions a real buyer types when they do not know you exist yet. Aim for 20 to 30 prompts across the topics you want to win.

Pull them from three places: the questions your sales team hears on calls, the searches in your existing search console data phrased as full questions, and the category comparisons buyers make. A good prompt sounds like a person, not a keyword. For example, "what tools help a Series B SaaS company show up in AI search results" beats "AEO tools."

Group the prompts by topic and by the persona asking. That grouping is what turns a pile of results into a map of where you are strong and weak.

Step 2: Run each prompt across the engines

Take every prompt and run it, unbranded and in a fresh session, through ChatGPT, Claude, Gemini, and Perplexity. Use a clean session each time so prior context does not bias the answer. For each run, record three things:

• Were you mentioned in the answer at all?
• Were you cited with a source link?
• Which competitors and which domains were named or cited instead?

That third column matters as much as the first two. It tells you which sources the models trust in your space, which is your target list for earned coverage later.

Step 3: Score what you find

Turn the raw notes into numbers so you can compare topics and track movement over time. A simple scoring frame:

Add up points per topic and per persona. Divide by the maximum possible and you have a citation share percentage you can bring to a leadership review. For the deeper version of this metric and how to make it board-ready, see our guide on how to measure your brand's AI citation share across LLMs.

Step 4: Find the gaps that matter

Now read the map. You are looking for three patterns:

1. The zero-visibility topic. A whole subject area where you are absent everywhere. This is usually the fastest win because you are starting from nothing and any citation is progress.
2. The blind-spot persona. A buyer type whose questions you never answer well. If demand gen directors ask five questions and you appear in none, that persona is a hole in your content.
3. The competitor-owns-it prompt. A question where one rival gets cited every time. Study their page. It almost always has a cleaner answer-first structure than yours.

Step 5: Fix the pages, do not just note the gaps

An audit that ends in a spreadsheet is wasted. Turn the top gaps into a short content list. For each gap, either the page does not exist or the page exists but is not structured to be quoted.

If the page is missing, write one built to be cited: a direct answer up top, question-shaped headings, a table, and real FAQs. Our walkthrough on how to get your brand cited by ChatGPT, Claude, and Perplexity covers the exact structure.

If the page exists but is invisible, the problem is usually one of two things. Either AI crawlers cannot read it cleanly, which you can check and address with the free llms.txt generator, or the content buries its answer under three paragraphs of preamble so the model has nothing quotable to lift.

Step 6: Set a cadence

One audit is a snapshot. Visibility moves as models update and competitors publish, so rerun the same prompt set monthly and watch the trend. This is the part teams skip and the part that actually builds a program. If running a 30-prompt sweep by hand every month is not realistic, that continuous measurement and the content engine that acts on it is what the AI Feed Engine and how OnlyAEO works automate, and you can see how that is scoped on the pricing page.

Proof that closing these gaps works: FastTrackr ran this kind of audit, found it was absent across its category, and moved to consistent citations, documented in the FastTrackr AI case study.

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