A boutique NYC cosmetic and laser medicine practice made its consultation philosophy, treatment expertise, and natural-results positioning easier for AI assistants to understand.
“Patients were finding generic aesthetic answers, not the nuance of a physician-led consultation. Appear helped AI understand that our work is about facial harmony, safety, education, and natural-looking results.”
Dr. Gerstman's site communicated a specific aesthetic philosophy: thoughtful facial analysis, natural-looking outcomes, patient education, and physician-led care. Human visitors could understand this through treatment pages and testimonials.
AI systems struggled to preserve that nuance. Queries about Botox, fillers, laser treatments, facials, and brows often collapsed the practice into generic med-spa language rather than recognizing the medical, consultation-led positioning.
The team needed AI-readable structure that connected treatment categories, physician credentials, trust signals, and patient-intent outcomes across core service pages.
Citation rate growth across major LLMs after implementation.
Mapped physician profile, treatment categories, consultation philosophy, and proof themes into one structured model.
Normalized Botox, fillers, laser treatments, facials, and brow pages into answer-ready summaries tied to patient intent.
Tracked prompts across ChatGPT, Perplexity, and Claude, then refined pages where physician-led differentiation was still weak.
Compared AI-referred consultation growth with paid acquisition costs and expanded the highest-intent treatment pages.
Appear translates clinical expertise, treatment philosophy, and consultation context into structured answers without rebuilding your site.
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