A six-step AI visibility audit
Set the scope
Define the business unit, market, audience, language, competitors, and decision you need the audit to inform. A useful audit starts with a clear question, not a generic score.
Map buyer prompts
Build a small library across discovery, evaluation, comparison, use-case fit, and brand-accuracy questions. Include the natural phrases buyers use in each target language.
Capture answer evidence
Run the same prompts across relevant AI engines and record the answer, sources, date, prompt, engine, and context. Preserve evidence before interpreting it.
Score the signals separately
Track mentions, citations, recommendations, accuracy, sentiment, competitor presence, and source domains as separate measures. One blended score can hide the reason for a change.
Audit the evidence behind the answer
Review your site, public profiles, reviews, documentation, case studies, directories, and competitor sources to find missing, weak, stale, or contradictory evidence.
Prioritise the next 30 days
Turn the findings into a limited roadmap of technical fixes, source corrections, content improvements, and off-site evidence work. Assign an owner, expected signal, and review date to every action.
What an AI visibility audit is and is not
An AI visibility audit examines whether and how answer systems surface a brand for relevant buyer questions. It gathers the evidence behind mentions, citations, recommendations, source selection, and competitor comparison. The purpose is to identify what a prospective customer is likely to hear before visiting your site and what information may be missing or unclear.
It is not a certification, a promise of placement, or a deterministic rank report. AI responses vary by model, prompt, location, product configuration, and time. A credible audit uses repeated sampling and keeps the underlying answers available for review instead of treating one result as a permanent position.
Start with the buyer question, not the brand name
Begin with the decisions that create commercial value: choosing a category, comparing providers, checking fit for a specific use case, verifying requirements, and deciding whom to contact. Brand-name prompts are useful for accuracy checks, but they do not reveal whether you appear when a buyer has not heard of you yet.
Group prompts by persona, intent, and language. For example, an agency buyer may ask for an AI visibility tool for client reporting, while a brand marketer may ask how to measure mentions in ChatGPT. Document why each question matters, which market it represents, and what a useful answer should clarify.
Capture the full answer and separate the metrics
For every run, retain the exact prompt, response, citations, model or engine, date, and any notable context. Then code the result: was the brand mentioned, cited, recommended, accurately described, positive or negative in sentiment, or absent? Which competitors were present? Which domains were cited?
Mentions indicate inclusion. Citations identify evidence and potential referral opportunities. Recommendations indicate perceived fit. Accuracy and sentiment show whether visibility helps or harms. Keeping these signals separate prevents a superficial improvement from masking an inaccurate description or a competitor’s stronger recommendation.
Audit the sources and content gaps behind the result
Inspect the pages and domains that appear in answers. For your own site, check accessibility, indexability, entity clarity, service or product definitions, proof, author expertise, updates, and internal links. For third-party sources, check whether listings, reviews, partner pages, editorial coverage, and community discussion accurately represent the brand.
A content gap is not simply a missing keyword. It is a missing piece of evidence needed to answer a buyer question: a clear comparison, a qualification criterion, a case study, an integration explanation, a location page, a current pricing detail, or credible proof of expertise. Prioritise gaps that repeatedly appear in high-value prompts and that competitors already answer well.
Turn the audit into a 30-day action plan
Rank actions by buyer impact, evidence gap, effort, and confidence. Start with foundational problems that affect many prompts: blocked or thin source pages, unclear positioning, outdated profiles, missing proof, or contradictory claims. Then create the few high-value pages and third-party corrections that answer the questions most associated with pipeline or revenue.
Every action should name the owner, the source or prompt it addresses, the expected outcome, and the date to re-test. Examples include correcting a public profile, publishing a comparison page, adding a current case study, clarifying an integration, or improving a poorly defined service page. Re-run the same prompt set after changes and compare the underlying responses, not only a summary score.
Where the free checker fits
A free checker is a useful first diagnostic. It can establish an initial snapshot across its supported engines and identify areas worth investigating. It does not replace a complete audit that uses a deliberate prompt universe, captures cited-source evidence, checks brand accuracy, and ties findings to business priorities.
Use the checker to begin the conversation, then deepen the work when the business needs a repeatable baseline, competitor comparison, a content roadmap, or client-ready evidence. The right level of audit depends on the decision at stake, not on how impressive a single visibility score looks.
Frequently asked questions
How long does an AI visibility audit take?
A focused first audit can begin with a small prompt set and a limited competitor group. The time depends on the number of markets, languages, engines, pages, and sources that need review. Start small enough to establish a trustworthy baseline, then expand deliberately.
Can an audit guarantee that a brand will be recommended?
No. An audit can identify weak or missing evidence and measure how answers change over time. It cannot control proprietary model behaviour or guarantee a recommendation.
What is the difference between a citation and a mention?
A mention names the brand in an answer. A citation links or attributes information to a source. Both matter, but citations reveal the evidence and page types that may influence the answer.
Should agencies run separate audits for every client?
Yes, each client needs its own prompt universe, competitors, facts, and market context. Agencies can standardise the workflow, but not the evidence or recommendations.