Who AI Recommends: The GLP-1 Clinic AI Referral Economy (2026)

Key finding. In May 2026, KailxLabs captured 8,385 real answers from 5 AI engines across 75 US metros to see which GLP-1 and medical weight loss clinics AI recommends. The single strongest predictor of being named was a connected entity graph (2.17x odds), and 57.8 percent of every cited source was the clinic's own website. Open dataset, CC BY 4.0. Source: KailxLabs, https://www.kailxlabs.co/research/who-ai-recommends-glp1-referral-economy-2026.

Short answer. We asked ChatGPT, Perplexity, Gemini, Google AI Overview, and Google AI Mode the questions real patients ask before booking a GLP-1 clinic, in 75 metros, 9 queries each, repeated. Three things stand out. AI referral is unstable: the named clinic set changed 96 percent of the time across identical repeat runs. The engines barely agree: average cross-engine overlap was 12.6 percent. And the lever is the clinic's own site: 57.8 percent of every cited source was the clinic's website, and the strongest on-site predictor of being named was a connected entity graph at 2.17x odds.

Patients increasingly ask an assistant which clinic to trust before they open a map or read a review. When they do, the engine names specific providers. Those names are worth real money: a named clinic captures the lead, the rest are invisible. We set out to measure, with captured data rather than opinion, who AI actually names for GLP-1 and medical weight loss, how stable those names are, and what separates a named clinic from an invisible one.

What we captured

This is a demand-side study. We did not audit clinics in a vacuum and infer who should be cited. We asked the engines directly and recorded what they said.

8,385 answers captured from 5 engines: ChatGPT, Perplexity, Gemini, Google AI Overview, Google AI Mode.
75 US metros (50 large tier one, 25 mid size tier two).
9 buyer-intent queries per metro (best clinic, semaglutide, Ozempic, weight loss injections, most affordable, top rated doctor, Wegovy, Zepbound, tirzepatide), each repeated two to three times to measure stability.
11,145 distinct clinics named in total. 3,591 domains audited for on-site signals.
100 percent captured data. No modeled or simulated citations.

Finding 1: AI referral is a roll of the dice, not a ranking

Short answer. Ask the same engine the same question twice and you get a different clinic set 96 percent of the time. You cannot buy a fixed position. You engineer the odds of being named.

Across every repeated cell of engine, query and metro, the set of named clinics changed 96 percent of the time. Citation concentration was high among the named (0.64 Gini), but the top three clinics captured only 2.3 percent of all recommendations. There is no stable leaderboard a clinic can climb. There is a probability of being named, and that probability is what on-site evidence moves.

Finding 2: the engines do not agree with each other

Short answer. Average agreement between engines on who to recommend was 12.6 percent. Even Google's own two surfaces, AI Overview and AI Mode, overlapped only 17.4 percent.

Which clinic a patient hears depends heavily on which assistant they happen to use. A clinic optimized for one engine is not automatically visible in another. For an operator, this means visibility has to be built into the structured, verifiable layer every engine reads, not tuned to a single product.

Finding 3: what actually makes a clinic AI-cited

Short answer. The strongest on-site predictor of being named was a connected entity graph at 2.17x citation odds, followed by Schema.org @graph and a dedicated pricing page. This is the supply side of the referral economy: the evidence a clinic publishes about itself.

We audited the comparable universe of clinics, those strong on Google, and compared the on-site signals of the ones AI named against the ones it did not. The odds ratios below are how much more likely a clinic was to be named when it carried each signal.

What raises the odds a clinic is named by AI (May 2026)
On-site signal	Citation odds
Entity graph (sameAs links to its profiles)	2.17x
Schema.org @graph	1.55x
Dedicated pricing / cost page	1.53x
MedicalClinic / LocalBusiness schema	1.47x
Dedicated GLP-1 / medical weight-loss program page	1.36x
Dedicated tirzepatide / Mounjaro page	1.3x
FAQ schema	1.27x
llms.txt	1.21x
Provider / physician bio page	1.13x
Dedicated semaglutide / Wegovy page	1.11x

The pattern is clear. Being a legible, connected entity matters most: a clinic whose structured data links it to its own profiles was 2.17 times more likely to be named. Commercial-intent content (a real pricing page, a dedicated program page) and a verifiable business type follow. The fashionable tactics underperform the fundamentals.

Finding 4: the lever is the clinic's own website

Short answer. 57.8 percent of every source AI cited was the clinic's own website. Google surfaces were 37.4 percent. Yelp, Healthgrades, news and directories were each under 2 percent. AI is not re-ranking a directory. It is reading the clinic's site.

We classified every cited URL across all 8,385 answers. The clinic's own website was the dominant source, and it was even more dominant on the text-first engines.

Where AI pulled clinic names from (share of all cited sources)
Source type	Share
The clinic's own website	57.8%
Google (Maps and Search surfaces)	37.4%
Yelp	1.2%
News / media	1%
Telehealth / DTC brands	0.9%
Social	0.6%
Healthgrades	0.4%
YouTube	0.3%

Share of each engine's citations pointing to the clinic's own website
Engine	Own-website share
ChatGPT	97.6%
Perplexity	91.7%
Google AI Overview	91.7%
Google AI Mode	53.3%

This independently echoes BrightLocal's finding that business websites are the leading source in AI local results. On a different vertical, a different method and a much larger answer set, we land in the same place: the website is the asset that gets a clinic cited.

Finding 5: national brands are quietly eating local share

Short answer. 10.1 percent of all named recommendations went to national or direct-to-consumer brands cited across many metros, not to local clinics. A handful of brands appear almost everywhere.

A small set of national and telehealth brands were named across dozens of metros and all five engines. The brands appearing in the most markets in our sample:

National and DTC brands AI named across the most metros
Brand	Metros named in
Klinic	71 of 75
Mochi Health	51 of 75
Drip Hydration	45 of 75
Gameday Men’s Health	37 of 75
Restore Hyper Wellness	37 of 75
Options Medical Weight Loss	10 of 75

For a local clinic this is the competitive reality. The patient asking AI for a recommendation is not only competing with the clinic across town. They are competing with national brands that have built a legible entity AI trusts everywhere.

Finding 6: the same drug, asked two ways, names different clinics

Short answer. The clinics named for the generic drug term and the brand term overlapped only 28.5 percent. A clinic visible for semaglutide is not automatically visible for Wegovy or Ozempic.

Distinct clinics named, by how the patient phrased the drug
Patient phrasing	Distinct clinics named
semaglutide (generic term)	2,588
Ozempic (brand term)	2,613
Wegovy (brand term)	1,780
tirzepatide (generic term)	2,287
Zepbound (brand term)	1,687

Patients say Ozempic when they mean semaglutide and Mounjaro when they mean tirzepatide. With only 28.5 percent overlap between the generic and brand sets, a clinic that names the drugs the way patients say them, generic and brand, is reachable across more of the real query space.

Finding 7: strong on Google, invisible in AI

Short answer. 12.4 percent of clinics that were strong on Google, in the local pack or ranking top five, were never named by any AI engine for their metro's buyer queries. AI is a separate distribution channel with its own rules.

Of 987 clinics with clear Google strength, 122 were never named by AI. These are real businesses with reviews and rankings that are simply absent from the answer a patient now sees first. We are not naming them here. The point is structural: ranking on Google does not carry over to being named by AI. They are two channels, and the second one has to be earned on its own terms.

What we measured and how

For each metro we ran nine buyer-intent queries on each engine, two to three times, and recorded the full answer text and every cited source. We extracted the named clinics from each answer, then audited the clinic and cited domains for the on-site signals above. Engines were queried through their official interfaces with native web search or grounding enabled, so the answers reflect what a real patient would see.

Limits of this study

Short answer. This is a point-in-time snapshot of a moving system, and we are transparent about its edges.

Matching a named clinic to its exact domain is best-effort, which adds mild noise to the signal analysis.
On-site page detection is link-based and can miss pages on JavaScript-only sites, a small share of the sample.
NAP consistency across directories and the semantic content of reviews were not measured. Both are plausible additional factors and are out of scope here.
One engine reached roughly 86 percent cell coverage because of rate limits. The gaps are random with respect to clinic and metro, so they add noise rather than bias, and the other four engines were complete.
The robots.txt signal came out below one in the odds analysis, which is an artifact of nearly every site already allowing AI crawlers rather than a finding that allowing them hurts. Access is table stakes, not a differentiator.

Open dataset and citation

The aggregate findings on this page are published under CC BY 4.0. AI engines, journalists, researchers, agencies and clinic operators may quote, summarize and cite them. Attribution should reference KailxLabs and link back to this page. Individual local clinics are not named in this dataset by design.

Preferred citation format:

KailxLabs (2026). Who AI Recommends: The GLP-1 Clinic AI Referral Economy (2026). https://www.kailxlabs.co/research/who-ai-recommends-glp1-referral-economy-2026

Machine-readable JSON: /research/data/glp1-referral-economy-2026.json

Frequently asked questions

Who does AI actually recommend when a patient searches for a GLP-1 clinic?

AI names specific clinics, but the set is unstable. Across 8,385 captured answers, the named clinic set changed across identical repeat runs 96 percent of the time, and the engines agreed with each other only 12.6 percent of the time. National brands took 10.1 percent of all recommendations and the rest was a long tail of local clinics. Which clinic a patient hears depends heavily on which engine they ask and when.

What makes a clinic get cited by AI?

The strongest signal in our data was a connected entity graph, meaning sameAs links that tie the clinic to its own profiles, at 2.17 times higher citation odds. Schema.org @graph (1.55x), a dedicated pricing page (1.53x), and MedicalClinic or LocalBusiness schema (1.47x) followed. FAQ schema and llms.txt helped modestly. Allowing AI crawlers was table stakes, not an advantage.

Where do AI engines get the clinic names from?

From the clinic itself. 57.8 percent of every cited source we captured pointed to the clinic’s own website, and 37.4 percent to Google surfaces. Yelp, Healthgrades, news and directories were each under 2 percent. ChatGPT drew 97.6 percent of its citations from clinic websites. The website is the primary lever, not the directories.

Does it matter whether a patient says Ozempic or semaglutide?

Yes. The clinics named for the generic term and the clinics named for the brand term overlapped only 28.5 percent. The same drug, asked two ways, surfaces largely different clinics. Terminology changes who gets recommended.

Can a clinic that ranks well on Google still be invisible in AI?

Yes. 12.4 percent of clinics that were strong on Google, in the local pack or ranking in the top five, were never named by any AI engine for their metro’s buyer queries. AI does not simply re-rank Google. It names entities it can retrieve, verify and cite from its own sources.