AI Visibility Audit for aesthetic & elective clinics · AI Discovery Intelligence
When someone asks ChatGPT, Claude, Gemini, or Perplexity the best clinic for a treatment in your city, whether they are a candidate, or what it costs, the engine recommends a clinic - and decides whether your name comes up, a competitor's does, or a directory answers for both of you. That research used to start with a search and a call to you.
28 Labs measures how the engines recommend your category, shows where you stand and who is recommended above you, and gives you the specific moves to win it - across all four engines.
4 engines · intent-scored query set · every read confidence-tagged
Measured from the outside. We do not measure training-data exposure, and we never present a clinical claim we cannot source.
The queries that bring patients
scored
A clinic's patients ask hundreds of meaningful questions before they book. We score each by intent and track the set that brings consultations, not the general-explainer long tail.
The shift
When a patient asks an engine who to see, it names a clinic off your property - you do not know if it was you, a competitor, or a directory.
The patient's shortlist now forms inside a chat window, before they ever search your name or call.
We measure how the four engines recommend your category and your city, show exactly where you stand and who is named above you, and hand you the moves to change it.
The board-level risk
A clinic's growth depends on being the name a patient researches, trusts, and calls. Answer engines now sit in front of that whole decision. The exposure is specific, and it is measurable.
"Best clinic for {treatment} in {city}" gets answered directly; the patient who would have searched and called now has a shortlist from a chat window.
Engines lean on directories and review sites, naming aggregators or competitors ahead of the clinics themselves.
"Am I a candidate / how much does it cost" - the questions that precede a booking - get answered without you, sometimes inaccurately.
"Is {clinic} reputable / safe" gets decided by reviews and forums the engine trusts.
Every figure we put against these is anonymised and confidence-tagged. We show the pattern and the magnitude; we never expose another clinic's numbers to make the point.
The query universe
A clinic's patients do not ask four questions. They ask hundreds, across a journey. The work is not to track every random query. It is to score every query by commercial intent and monitor the high-intent ones that actually bring consultations, mapped to where the patient is in the decision.
Suitability, price, and best-clinic queries carry the consultations; general "what is {treatment}" carries almost none. We weight what you track to where the patients are - so the read you get is the one that matters to the practice, not a vanity average across the long tail.
How we keep it honest
No one can prove a single AI recommendation caused a single lead. Anyone who claims they can is selling certainty that does not exist. We treat it the way marketing-mix modeling treats a channel: we estimate the contribution from converging signals and we label the confidence of every read, so your analysts can audit any claim back to the evidence behind it.
When a read is directly observed in your own data, we say so. When it is inferred, we say so. When it rests on industry intelligence, we say that too. You never get a confident-sounding number with nothing under it.
Directly observed. The signal is present in your own analytics or the AI-engine output. We saw it happen.
Inferred. Multiple independent signals converge on the same read, but no single source confirms it outright.
Industry intelligence. The read rests on category benchmarks and external patterns, not your own data. Treated as directional.
What winning takes
A clinic wins when it becomes the source the engines trust to answer the category. That is structural. More blog posts will not do it. Being the authoritative, reachable source for what patients actually ask about will.
We diagnose where the engines already trust you, where they trust a competitor instead, and the specific moves that shift that trust toward you. The output is a build plan your team can act on, not a list of keywords.
Start here
A 60-minute working session. We walk a sample audit, show you how the recommendation read would shape up for your category and city, and scope a live run.
No obligation. We do not measure training-data exposure, and we tell you what we can and cannot see before you commit.