We ran our audit on 5 AI-visibility vendors. One passed.

On July 12, 2026 we pointed our 5-signal checker at five companies whose product is AI-visibility tracking. The scores came back 100, 70, 55, 55 and 40 out of 100. One vendor passes cleanly; four fail parts of the audit they'd flag on a client's site. We're not naming them — the point isn't a scoreboard, it's what the failures teach.

For calibration: geoplaybooks.com itself holds 5/5 PASS on the same signals — verified signal-by-signal the same day (200 + ~20,000 chars of server-rendered text, explicit AI-bot allowances in robots.txt, a spec-valid llms.txt with 100% described links, an Organization/Person/WebSite entity graph, and answer-first structure under every H2). We eat the medicine we prescribe.

What the five probes found

The anonymized breakdown:

• Vendor A — 100/100. The exception that proves it's doable: a spec-valid llms.txt with 180 described links, clean schema, answer-first pages.
• Vendor B — 70/100. Ships an llms.txt, but it's a junk export: 86 links, 2% with descriptions. A file that exists and says nothing is a map with no street names.
• Vendors C and D — 55/100 each. No llms.txt, no JSON-LD schema. Engines can fetch their pages but can't classify who they are.
• Vendor E — 40/100. A client-rendered marketing site: almost no server-side text, no H1/H2 structure a bot can segment, and /llms.txt answers with an HTML page instead of a file.

Every result is reproducible — run any domain through the free checker and you'll see the same five probes we used (they're also callable by AI agents via our MCP server ).

Why do visibility vendors fail visibility audits?

The pattern matches what we see across all logged runs, not just vendors. In Checker Finds #1 , 13 of 13 domains passed robots.txt — and more than half failed schema. The gap is organizational, not conceptual: robots.txt is a one-line decision, while entity schema and server-side rendering are engineering work that marketing sites — often single-page apps shipped by growth teams — rarely get budgeted for. Knowing the playbook and shipping the playbook are different jobs.

There's a useful lesson in Vendor B, too: an llms.txt is not a checkbox. The spec's one-line descriptions are the whole point — they're what lets a crawler decide whether to fetch a page without fetching it. An export with 2% coverage technically "has llms.txt" and practically has nothing.

The 5-minute version for your own site

• Fetch your homepage with curl — if the response has almost no text, retrieval bots see the same emptiness. (Fix: server-side rendering; our GEO playbook covers it.)
• Check robots.txt and your WAF — the block that matters usually lives at the CDN layer, invisible in robots.txt ( AI crawler playbook ).
• Lint your llms.txt with the validator — aim for 100% described links, not mere existence. No file yet? The generator builds one in the browser.
• View source and search for application/ld+json — no Organization/Article schema means engines can't classify you.
• Read the first paragraph under each H2 aloud: if it isn't a self-contained 40–80-word answer, it won't be extracted.

Or let the machine do it: the checker runs all five in about 20 seconds, and the audit ladder starts at $49 when you want the page-by-page version.