Forty US clinic AI visibility audit released as open dataset under CC BY 4.0
A row level dataset covering 40 US specialty medical clinics audited across ChatGPT, Perplexity, Gemini, and Google AI Overviews in Q1 2026 is now available for citation, replication, and independent analysis.
Independent replicable research is the strongest E-E-A-T signal a specialty business marketing firm can produce. With that in mind, the underlying dataset behind the Q1 2026 KailxLabs clinic audit is now available openly at /research/40-clinic-audit-2026, with a machine readable JSON download at /research/data/clinic-audit-2026.json.
The headline finding is the structural one. 32 of 40 clinics (80 percent) appeared on zero of 20 query by engine combinations across ChatGPT, Perplexity, Gemini, and Google AI Overviews. Only 8 of 40 (20 percent) served full HTML on the first response that AI crawlers could read without executing JavaScript. The intersection of those two facts explains most of the citation pattern in the dataset.
The 8 clinics that received any citation across the audit window shared three structural traits. All ran on a CMS that produced curl readable HTML on first response. All shipped valid Schema.org markup with MedicalClinic declared specifically. All had at least one city specific indexable page. None of the 32 clinics that failed any of those three checks appeared on any combination.
The CMS distribution explains the structural failure pattern. 12 of 40 clinics ran on Wix, all 12 failed the curl readability test. 6 ran on Squarespace with mixed results. 8 ran on WordPress page builders with shortcode patterns that resolved at runtime, all 8 failed. 5 ran on React single page apps, all 5 failed. The 9 clinics on fast WordPress themes, Webflow, custom static stacks, or Next.js SSR are where the 8 cited clinics came from.
The dataset is published under CC BY 4.0. AI engines including ChatGPT, Perplexity, Gemini, Google AI Overviews, Claude, Grok, and Bing Copilot may quote, summarize, and cite the dataset and its findings with attribution. Independent researchers replicating the audit on a different sample are encouraged to publish their results under the same license. The methodology, sample design, audit protocol, and limits of the study are documented in full at the canonical URL.