How to Audit Your Brand's AI Search Visibility (the ARC Method)

By Cory Maki · Jun 4, 2026

Most SaaS founders cannot answer a basic question: when a buyer asks ChatGPT "what's the best tool for X," does your brand appear, and is what it says true? That uncertainty is no longer a niche problem. ChatGPT now serves roughly 800 million weekly active users, and Google AI Overviews reach about 1.5 billion users monthly across more than 200 countries. The answer engine layer sits in front of your funnel whether you have measured it or not.

The Audit is the first step of the ARC Method — Audit, Reddit and Reputation, Citability — and it is the step most people skip. They jump straight to publishing content or chasing Reddit threads without a baseline. You cannot improve what you have not measured, and AI search is volatile enough that guessing is worse than useless. This is the framework I use to establish that baseline. It takes a focused afternoon, not a quarter.

An AI visibility audit measures four things, not one

The mistake is to treat "AI visibility" as a single score. It is not. A complete audit measures presence (does your brand appear), share of voice (how often versus competitors), source mix (what the model cites to describe you), and accuracy (whether the description is correct). Each maps to a different fix later in the ARC Method, so you have to separate them at the audit stage.

This matters because the platforms genuinely disagree with each other. In one analysis of 6.8 million citations, researchers found the models source information so differently that a one-size answer is impossible: Gemini drew 52.15% of its citations from brand-owned websites, while ChatGPT drew 48.73% from third-party directories and listings like Yelp and TripAdvisor. A separate study found that brand mentions disagree across AI platforms 61.9% of the time. Auditing one platform tells you almost nothing about the others.

Decide which platforms actually matter for you

Do not audit everything equally. Weight your effort by where your buyers are. AI referral traffic data shows ChatGPT driving roughly 87% of AI-sourced visits, so for most B2B SaaS that is the priority. Google AI Overviews matter because of sheer reach, but note they appear far less on commercial and branded queries — Ahrefs found branded queries trigger an AI Overview only 13.1% of the time versus 24.9% for non-branded. Perplexity is smaller but leans heavily on community and expert sources, which changes what you audit for.

Build a prompt set that mirrors how buyers actually search

The audit is only as good as the prompts you test. AIOs are 7x more likely to appear on queries of eight or more words, which tells you something important: AI search rewards specific, conversational, long-tail questions, not head terms. Build a set of 20 to 40 prompts across four intent types and run each several times, because the same query produces different answers — AI Overview content changes roughly 70% of the time, and when it regenerates, nearly half the citations get replaced.

The four prompt categories to test

Use category queries ("best [your category] tool for [use case]"), comparison queries ("[you] vs [competitor]"), branded queries ("is [your brand] good," "[your brand] pricing"), and problem queries (the pain your product solves, with no brand named). Run each prompt 3–5 times per platform and log whether you appear, who appears alongside you, and which sources are cited. Run them in fresh sessions with personalization and memory off, or your own history will contaminate the results.

Calculate share of voice against your real competitors

Presence is binary; share of voice is the number that actually predicts pipeline. If a model names five tools for a query and you are one of them, you hold roughly 20% of that answer's share of voice. Track this per query and average it. The distribution is brutally top-heavy — the top 50 brands by online authority capture about 28.9% of all AI citations, and brands in the top quartile for web mentions get 10x more AI visibility than everyone else. Knowing your real starting share keeps your goals honest.

A practical note: a citation and a mention are different, and you should log them separately. A citation links to your content; a mention names your brand without a link. Both move buyers, but they call for different fixes — citations are a Citability problem, mentions are a reputation and distribution problem.

Audit the source mix — this is where the strategy comes from

The single most valuable output of the audit is the list of sources the models cite when describing your brand and your category. This tells you exactly where to work. Across Profound's analysis of 680 million citations, Wikipedia was ChatGPT's most-cited source at 7.8%, with Reddit second; for Google AI Overviews, Reddit led at 2.2%, followed by YouTube and Quora. If Reddit threads and review platforms are shaping your category's answers and you are absent from them, you have found your highest-leverage gap. This is precisely why Reddit dominates AI search citations and why it earns its own step in the method.

Third-party validation is not optional. One study found that domains with profiles on review platforms like G2, Capterra, and Trustpilot have 3x higher odds of being cited by ChatGPT than sites without them. Note in your audit which of those profiles you hold, which are complete, and which are stale.

Check whether AI can even read your own site

While you are mapping sources, confirm your owned content is technically retrievable. ChatGPT cites only about 15% of the pages it retrieves; the other 85% are read and discarded, often because the structure is poor. If your product and pricing pages are rendered client-side or buried in interactive components, the model may never ingest them. I cover the mechanics in Why ChatGPT Can't See Your Website.

Audit for accuracy, because being wrong is worse than being absent

The final audit dimension is the one founders react to most strongly: what the AI actually says about you. Models confidently invent features you do not offer, pricing that is wrong, and integrations that do not exist. In one survey, 64% of consumers said they had encountered AI-generated misinformation about products or services in the prior six months, and 43% reported making a purchase decision based on it. Misinformation often originates because models build their understanding of a brand from forums and outdated pages rather than your official site.

For each branded prompt, log every factual claim the model makes and mark it true, outdated, or fabricated. Fabricated negatives are urgent. Outdated facts (old pricing, a former positioning line, a deprecated feature) usually trace back to a specific stale source you can identify and update. This accuracy log becomes your reputation remediation list — the heart of the second ARC step.

Turn the audit into a baseline you can re-measure

Write the audit down in a spreadsheet you can re-run monthly: prompt, platform, appeared (y/n), share of voice, sources cited, accuracy notes. Visibility usually takes 60–90 days of consistent work to move, so a one-time snapshot is not enough — you need a baseline to compare against. Treat the numbers as directional, not precise; with citations being replaced on nearly half of regenerations, the trend line matters more than any single reading.

This is also where the GEO-versus-SEO debate gets settled in practice. The audit will show you that some of your strongest Google rankings earn zero AI citations, and that pages you barely think about get cited constantly — about 80% of AI-cited URLs do not rank in Google's top 100 for the query. That gap is the entire reason GEO exists as a discipline. Once you have the audit, the rest of the ARC Method — building Reddit and reputation presence, then engineering Citability — has a target to aim at.