AI visibility audit: a one-time measurement, not a subscription
By Vladimir Boldyrev · LLMBrandScan · Published October 2, 2026 · Reviewed October 2, 2026
Before you decide what to fix, you need to know whether AI assistants recommend you at all. An audit answers that with sampled, dated evidence: a set of buyer questions asked live, several times each, on several engines.
Live engine status (2026-10-18): ChatGPT (API with web search), Gemini (grounded), Claude (API with web search) and Google AI answers (AI Overviews) are answering. Perplexity Sonar is temporarily unavailable at the provider — scans run on the engines that answer, and an engine that does not answer is not charged. This note updates by itself when an engine comes back.
An AI visibility audit measures how often AI assistants name your brand when a buyer asks for options in your category, compared with your competitors. It works by asking a fixed set of unaided questions, none of which contains your brand name, and collecting each answer several times because the same question can return different brands on different runs. The output is a share of voice with an observed range, the questions where a competitor took your slot, and the sources the models cited. At LLMBrandScan a $19 Quick check collects 90 answers, a $149 Audit collects 625, and a $249 Deep Audit collects 1,000. All are one-time purchases that do not renew. Because an audit is a dated sample rather than a live feed, the honest way to use it is to run it before you change anything, make one deliberate change, and run it again, so that the difference between two reports tells you whether the change worked.
What you get in the report.
| Section | What it tells you |
|---|---|
| Share of voice, as a range | Your brand's share of the answers that name any tracked brand, labelled Strong, Emerging or Noisy according to how much the repeated samples agree. |
| Per-engine breakdown | Which engines name you and which do not, so a gap in one assistant is not averaged away. |
| Lost slots | The questions where an answer named a competitor and left you out, with the competitor that took the place. |
| Cited sources | The domains the models leaned on when they answered, which is where a content or PR fix has to land. |
| The raw answers | Every sampled answer, exportable to CSV, XLSX and PDF, so you can re-check any number yourself. |
This is a real report, untouched: open the Fathom Analytics sample or the Notion sample.
How the audit is run.
1. You name the brand, the category and the competitors.
The questions are built from your category and your buyers' language, and they never contain your own brand name — otherwise the model is handed the answer.
2. We ask each question live, several times, on each engine.
A Quick check samples every question 3 times; an Audit or Deep Audit 5 times. 4 engines live right now on an Audit (2 on a Quick check).
3. We read every answer and tally who was named.
An engine that fails to answer is reported as unavailable, not as a miss. You pay only for answers that come back — a failed or empty answer costs nothing, and its credits return to your balance automatically when the scan finishes.
What it costs.
Quick check
$19
90 answers, paid once
Audit
$149
625 answers, paid once
Deep Audit
$249
1,000 answers, paid once
Credits are prepaid and nothing renews. For how this compares with monthly tools, see the dated price comparison.
Questions.
What is an AI visibility audit?
A measurement of how often AI assistants name your brand, and in what position, when asked the questions your buyers ask. It reports your share of voice against named competitors, the questions you lost, and the sources the models cited.
How much does an AI visibility audit cost?
Here: $19 for a Quick check, $149 for a full Audit and $249 for a Deep Audit, each paid once. Monitoring tools bill monthly instead; the comparison page lists their entry prices with the date they were read.
How long does it take?
The report is ready when the sampled answers are collected and analysed. A Quick check finishes in minutes, a full Audit takes longer because it asks more questions on more engines.
Can I try it before I pay?
Yes: the free AI visibility checker asks three questions on the engines that are answering today and shows whether each one named your brand. It is limited to one run per person per day.
Keep reading.
- Free AI Visibility CheckerOne brand, three unaided buyer questions, the engines that answer right now. Mentioned or not, per engine, in under a minute.
- LLM SEO ToolA measuring tool for LLM SEO: where AI answers name you, who they name instead, and which sources they lean on.
- Best AI Visibility Tools ComparedOtterly, Peec AI, Profound, Ahrefs Brand Radar and Semrush next to a one-off scan — prices checked on the vendors' own pages.
- LLMBrandScan vs. ProfoundProfound's published pricing and scale as of mid-2026, next to what a one-time scan costs and covers. Every number sourced and dated.
- LLMBrandScan vs. Peec AIWhat Peec AI costs and covers as of mid-2026, and the honest decision rule for continuous monitoring versus a single dated audit.
- What Generative Engine Optimization (GEO) Actually MeansThe definition, how it differs from SEO, and why 'optimizing for AI answers' is a different job than optimizing for a ranked list.
- Answer Engine Optimization (AEO): Myths vs. FactsAEO, GEO, and LLM SEO get used interchangeably online. Here's what's actually true about each — and where the myths come from.
- How to Create an llms.txt File (Step by Step)A working llms.txt template, what belongs in it, and an honest read on what it does and doesn't do for AI visibility.
- AEO vs. GEO: A Head-to-Head ComparisonThe two acronyms compared axis by axis — origin, scope, mechanics, what each one measures — and a decision rule for which one your work actually is.
- GEO vs. AEO vs. LLM SEO: What's the Actual Difference?Three overlapping terms for three overlapping-but-distinct jobs. A disambiguation guide so you stop guessing which one you need.
- What Is AI Visibility, and How Is It Actually Measured?The metrics the category has converged on — Visibility Score, Share of Voice, first-mention — explained from first principles.
- How to Improve Your Odds of Being Recommended by ChatGPTSeven concrete, testable levers — and three things that don't work no matter how confidently they're sold to you.
- How Perplexity Chooses Sources to CiteThe mechanics of Perplexity's citation model, what makes a page citable, and how to track whether you're being cited at all.
- Optimizing for Google AI OverviewsWhat triggers an AI Overview, how it differs from classic SGE and organic ranking, and a practical checklist to work through.
- AI Brand Monitoring: Why a One-Time Check Isn't EnoughAI answers drift month to month. What ongoing monitoring actually requires, and the DIY-vs-tool tradeoffs honestly laid out.
- AI Share of Voice: The Formula and a Worked ExampleThe exact formula, a worked numeric example with real arithmetic, and how it maps onto the classic marketing metric it's borrowed from.