Case studyHealthcare · SpecialistCentral EU

ConfidentialHow a leading aesthetic surgeon went from zero AI footprint to #1 visibility in their specialty across ChatGPT, Perplexity and Gemini in 27 days.

#1
in AI visibility in 27 days
  • #1 AI rank
  • 27 days
  • From zero
  • Default expert
0130-day signals
#1AI rank, primary procedure
0first-time citations earned
+0xconsult inquiries
01The challenge

High-trust healthcare is the hardest category to GEO into responsibly. Buyers ask AI assistants for the best surgeon for a specific procedure, the model returns a name, and that name converts at extraordinary rates because the consequence of a wrong choice is so high. Being the model's default recommendation is worth more here than in almost any other vertical — and being absent costs more.

This surgeon had decades of clinical credibility, peer-reviewed publications and a strong referral network. They also had essentially no AI presence. Every model defaulted to a small number of clinic chains and a couple of well-known individual practitioners. None were better surgeons. They were just more legible to the models.

The brief: become the default cited expert for the specialty in the target region, ethically, in under a month.

Day 0
Day 14
Day 27
02What we did
  1. 01

    Built a complete entity profile from the surgeon's clinical record — credentials, board certifications, publication history, procedure-specific outcomes — in a structured form models could actually parse.

  2. 02

    Reconciled inconsistent signals across the existing footprint (clinic site, professional directories, hospital affiliations) so the model saw one coherent expert rather than five contradictory mentions.

  3. 03

    Built procedure-specific content that answered the questions patients actually ask before consulting — recovery, candidacy, alternatives — sourced and cited so models would trust and surface it.

  4. 04

    Engaged with the third-party content layer responsibly: peer-reviewed citations, editorial profiles, structured directory data on the platforms models prefer for medical expert queries.

  5. 05

    Daily citation tracking across procedure-specific prompts, with weekly compliance review to ensure all content met healthcare advertising rules in the region. Exact clinic-side team allocationsPending client approval

0327 days outcomes
#1default expert, target region
0days to top rank
0compliance issues
Client quote pending publication approval

We don't publish what we can't verify with the client. This will fill in when they sign off.

04The reusable framework

In high-trust verticals, the model's job is not to find the most popular practitioner — it is to find the most credentially legible one. Most genuine experts lose the AI ranking not because their work is worse but because their signal architecture is worse: scattered profiles, inconsistent credentials, no structured procedural content. Rebuild that legibility, ethically, and the model will recommend the right expert. The reward is asymmetric because trust converts at multiples in this category.

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