What a free AI visibility audit looks like
This is a representative example of the audit we deliver, built from the patterns we see across padel club audits: Indoor padel club, 8 courts, large European city, as of May 2026. Every real audit follows this shape: one score, a per-platform breakdown, the findings that explain it, and the fixes that would move it.
The club is a solid, established business that machines can barely see. Assistants find the club inconsistently, describe it vaguely, and recommend a nearby competitor for most high-intent questions.
How each assistant sees the club
ChatGPT
52/100Named for 2 of 12 test questions. Description outdated: quotes the pre-renovation court count from an old directory listing.
Perplexity
61/100Cited via a booking platform profile, never via the club site. The recommendation carries the platform booking link, not the club one.
Gemini
44/100Appears in map-based answers with correct location, but answers hedge on prices and hours because the site states neither as text.
Claude
31/100Rarely named. When asked directly about the club, the answer is generic and admits it has little information to work from.
Five findings from this audit
1. Core facts are not machine readable
Prices, opening hours, and the court count exist only inside images and a downloadable PDF. Every assistant tested either omitted these facts or guessed them, and one guessed wrong.
2. The club is three different entities to a machine
The website, the Google Business Profile, and the booking platform each use a different variant of the club name. Machines cannot confidently merge them, which dilutes every signal the club produces.
3. The booking platform outranks the club for its own name
For 9 of 12 test questions, the strongest retrievable source about the club is its booking platform profile. Recommendations therefore route through the platform, with its commission and its ownership of the customer.
4. No page answers a single real question
The site has no FAQ and no page matching common prompts (beginner friendliness, racket rental, walk-in play). Competing venues with FAQ pages get quoted on exactly these questions.
5. Zero structured data
No JSON-LD of any type is present on any page. Assistants must infer the business type, location, and offer from prose, and the audit shows they infer it incompletely.
The three fixes that matter first
Every audit ends with a prioritised list. These three would resolve most of what the findings describe.
Publish a machine-readable facts page
One page stating address, hours, court count, surfaces, prices, and the booking path as plain text, marked up with SportsActivityLocation JSON-LD. Resolves findings 1 and 5 and gives every assistant one authoritative source.
Unify the entity
Pick the one official club name and apply it identically on the site, the Google Business Profile, and every booking platform and directory, then declare the profiles with sameAs markup. Resolves finding 2 and strengthens every other signal.
Answer the twelve questions in text
A visible FAQ answering the exact prompts from the audit (beginners, rental, walk-ins, group play, parking), mirrored in FAQPage JSON-LD. Directly targets the questions where competitors are currently quoted instead.
Who AI recommends instead
Share of AI recommendation mentions across the twelve test questions in this club's market.
The gap between this club and Competitor A is an information gap. The audit shows exactly which missing signals produce it.
Get this for your business
The audit above is the free one. We run it for padel clubs, coaches, brands, and every other kind of padel business, in any market, from Madrid to Dubai. If you want a faster first signal right now, the AI Readiness Scorecard checks your website's structural readiness in about a minute.
See your own numbers
Tell us your padel business and we will run this exact audit on it, free. You get the score, the findings, and the fix list, no obligation.
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