The Valens Clinic

Overview

The Valens Clinic offers specialist medical and aesthetic treatments in a premium, patient-first environment. Even with excellent outcomes and facilities, they were missing from the new places patients now begin their care journey — AI assistants like ChatGPT, Perplexity, and Google AI Overview. They partnered with Liplyn Information Group to close that visibility gap and become the clinic AI systems confidently recommend.

Patients increasingly ask AI assistants for clinic recommendations instead of typing traditional search queries. The Valens Clinic had strong local credibility but lacked the structured online presence, entity signals, and authoritative citations that AI models use to choose which providers to mention. They were effectively invisible in the fastest-growing patient discovery channel.

We implemented a healthcare-focused GEO program for The Valens Clinic: structuring treatment and condition content so AI models can clearly understand their expertise, earning trusted citations from health and lifestyle publications, optimizing their knowledge graph presence, and aligning their website with the natural-language questions patients ask AI assistants.

The Valens Clinic now appears in AI assistant responses when prospective patients ask about relevant treatments and specialist care. Their organic visibility for high-intent condition queries improved significantly, and the clinic reports a growing share of inquiries from patients who say they were referred by an AI search result or assistant recommendation.

Why AI search matters for private clinics

Patients researching a treatment no longer start with a list of ten blue links. They ask ChatGPT, Perplexity or Google AI Overview which clinic is best for their situation, what a procedure costs, and what recovery looks like. Those assistants answer with a short, curated shortlist — and if a clinic is not part of the underlying data, it is simply never mentioned. For The Valens Clinic, that meant excellent medical outcomes were invisible at the exact moment a patient decided where to go.

Building the entity foundation

We started by making the clinic machine-readable. Every treatment received its own structured page with MedicalProcedure and FAQPage schema, linked to the practitioners performing it and the conditions it treats. Practitioner profiles were connected to verifiable registrations and publications, so language models can recognise real medical expertise. Name, address, specialisms and opening hours were made consistent across the website, maps, healthcare directories and review platforms — inconsistency is one of the fastest ways to lose an AI citation.

Answer-first content for real patient questions

Using query mining across AI assistants and search data, we mapped the questions patients actually ask before booking: suitability, risks, recovery time, cost ranges and alternatives. Each page opens with a direct, quotable answer of two to three sentences, followed by clinical depth, and is reviewed by a named practitioner. That format is exactly what generative engines extract and cite, and it doubles as reassuring content for patients who arrive from a traditional search result.

Authority and citations

Language models weigh independent sources more heavily than a brand's own website. We built a digital PR programme around genuine clinical expertise: expert commentary in health and lifestyle media, contributions to condition-specific resources, and accurate profiles in medical directories. Over the programme the clinic earned more than forty relevant citations, which is what moved it from occasionally mentioned to consistently recommended.

Measuring AI visibility

We track a fixed set of high-intent prompts across ChatGPT, Perplexity, Gemini and Google AI Overview every month, scoring whether the clinic is mentioned, in which position, and with which source attached. Combined with intake forms that ask new patients how they found the clinic, this gives a defensible link between AI visibility work and booked consultations — the same reporting model we use in every GEO programme.

Frequently asked questions

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