The specialist AEO and GEO agency, with our own AI search monitoring platform.
Focus vertical — Healthcare and MedTech.
We track how ChatGPT, Claude, Gemini and other LLMs describe your clinic, your medical device or your pharmaceutical product, diagnose what is holding you back, then execute the answer engine optimisation work end to end. Built on the AI monitoring tool we developed in-house and configure around your specialty, your regulatory footprint and the patient or HCP queries that matter.
Patients, caregivers and healthcare professionals are now asking ChatGPT and Claude about symptoms, treatments, devices and clinics before they ever open a search engine or pick up the phone.
Answer engine optimization and generative engine optimisation are the layer where those decisions get shaped, and it is the layer where most healthcare and medtech brands have no presence, no data and no plan in place.
You rank on Google but you do not exist in AI search.
Search rankings and AI visibility run on entirely different signals, and ranking well on Google gives you almost no head start in AI search. Language models do not read search results pages. They draw on their training data, on medical citations and on how clearly your clinic, device or treatment is described across trusted sources, which means you can hold page one on Google and still be completely absent from ChatGPT, Claude and Gemini answers.
AI does not recognise you as an authoritative source.
Healthcare sits squarely in YMYL territory, and language models are deliberately cautious about which brands they surface in medical answers. Without visible accreditations, peer-reviewed publications, named clinicians and authoritative third-party citations behind your name, LLMs default to recommending the institutions and products they trust most, and your brand quietly drops out of the answers your patients are actually reading.
Your product or treatment gets described inaccurately.
AI models describe your indications, contraindications, mechanisms of action and clinical use cases based on what they have learned, and what they have learned is often outdated, incomplete or misaligned with your regulatory status. Approved uses get conflated with off-label ones, side effects get misstated, and device specifications get blurred with competitors. All of this shapes how patients and clinicians perceive your product before you have had a chance to say a word about it.
Your visibility breaks down on symptom-led queries.
Patients rarely ask AI for your brand by name. They describe a symptom, a condition or a situation and ask which clinic, treatment or device could help. You may be the right answer clinically and still be entirely absent from those responses because your content was never structured around the language patients actually use. Two patients with the same condition end up with two completely different shortlists, and yours often only makes one of them.
The signals that decide whether AI recommends you.
Healthcare and medtech have a particular set of signals that drive visibility in AI search and the accuracy of how AI describes you. These are the six we track, diagnose and improve through our monitoring platform.
01
Authority and trust signals.
Does AI associate your brand with accreditations, named specialists, peer-reviewed publications and recognised medical institutions? In YMYL categories, language models lean heavily on perceived authority before they recommend anything, and brands without a visible trust footprint get filtered out long before a patient or HCP ever sees them in an answer.
02
Indication and contraindication accuracy.
Are your approved indications, contraindications and warnings described correctly across AI responses? For devices and pharmaceuticals especially, even small inaccuracies in how AI summarises your product create regulatory exposure, erode clinician confidence and push your brand out of the answers where it should be the obvious recommendation.
03
Symptom-led query presence.
Do you appear when patients ask AI about the symptoms, conditions or scenarios your product or service is built to address? Symptom-led prompts are where most healthcare buyer journeys now begin, and presence in "what should I do if…" or "best treatment for…" answers is the point at which AI search starts feeding real demand rather than vanity impressions.
04
Geographic and local accuracy.
For clinics and provider networks, are you being surfaced for the right cities, regions and catchment areas? LLMs handle local intent very differently from traditional search, and weak or inconsistent geographic signals across your owned and third-party surfaces mean patients in your actual service area end up being pointed elsewhere.
05
Regulatory status alignment.
Does AI describe your product in line with its current regulatory status in each market? Approval level, prescription status, age indications and market availability all need to match reality, because when AI describes a product outside its approved scope, the downstream consequences land on the brand, not on the model.
06
Share of Voice versus key clinical alternatives.
How often do you appear against the three to five clinical or commercial alternatives that matter most? Share of Voice in AI search is the most direct read on how likely you are to be recommended in the answers patients and HCPs actually see, and it tends to be a leading indicator of patient flow and prescribing behaviour a quarter or two ahead of the funnel.
A monitoring platform that is built around your healthcare specialty. We do not offer just a generic template.
We built our own AEO monitoring platform in-house, and we configure it around your specialty, your product or service line, your regulatory footprint and the queries your patients and HCPs actually run. The clinical and commercial alternatives we track are the ones you actually compete with, and the metrics we surface map to how AI search visibility translates into patient flow, prescribing behaviour or device adoption in your specific niche.
We are a full-cycle agency, which means we do not hand over a dashboard and walk away. We analyse, we diagnose, we build the roadmap and we execute, either alongside your team or as a fully outsourced function.
Custom prompt library.
Tell us the conditions, specialties or product lines you want to monitor, generate prompts automatically and pick the ones that fit, or upload your own. You get a fully configured AEO monitoring dashboard built around the symptom-led, treatment-led and brand-led queries that matter to your business, not a default one.
Configurable Niche metrics.
Mention Rate, Share of Voice, Average Rank, Authority Signal Score, Indication Accuracy, GEO Score and several others, all weighted and reported against the clinical and commercial goals you actually care about. The dashboard is customisable from the ground up, so the view your team logs into reflects how your organisation measures success.
Adjustable competitor lists.
Nobody knows your real clinical and commercial alternatives better than you do, and AI knows even less than that. You can adjust the clinics, devices, treatments or brands you want to monitor and benchmark against at any time, on your terms and at the cadence that suits your category.
Full setup support by our team.
As a full-cycle AEO and GEO agency, we are involved from step zero. We set the tool up, tailor it to your specialty and audience, and guide you through prioritising the GEO initiatives that will move the needle first. Execution can run with your team, with ours, or with a blend of both, depending on the bandwidth and regulatory constraints you have in-house.
Execute with your team or with our support, depending on your internal capabilities.
We adapt monitoring and AEO implementation to your specialty, internal metrics and workflows.
We adapt monitoring and implementation to your specialty, your internal metrics and the workflows your team already uses. Strategy, execution and our in-house monitoring platform run as one engagement, so the work shows up in AI search visibility you can actually measure.
4 STEPS
AEO monitoring,
Monitor AEO – if & how you appear in AI search:
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mention rate,
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share of voice,
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authority and trust signals,
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symptom and condition coverage matrix,
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brand and source co-occurrence,
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your brand GEO score and dynamics,
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and any other tailored criteria.
Learn how AI systems describe:
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your indications and contraindications,
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treatment outcomes and efficacy perception,
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safety profile and side effects,
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patient and HCP fit,
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geographic relevance,
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and not limited to.
1
GEO readiness,
Analyse your GEO readiness:
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own resources programming layer,
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own resources content layer,
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third-party medical presence,
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clinical and regulatory citations.
Check competitor visibility:
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who gets recommended,
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in what clinical contexts,
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with which attributes,
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how positioning changes over time.
Understand which sources influence AI outputs:
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medical review platforms,
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peer-reviewed publications,
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regulatory and guideline bodies,
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patient communities, Reddit, forums,
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HCP knowledge bases, etc.
2
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quick-win identification,
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authority signal prioritisation,
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content and clinical documentation recommendations,
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external visibility opportunities,
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phased execution framework aligned to compliance review,
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our team support,
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dynamics tracking.
Get a 90-Day GEO Roadmap.
3
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website and semantic structure recommendations,
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condition, treatment and product page optimisation,
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clinical documentation and knowledge base optimisation,
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AI-oriented content strategy with medical accuracy review,
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content creation and placement automation,
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review platform visibility increase,
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external mentions and authority building,
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patient community and HCP forum presence,
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competitive and clinical positioning optimisation.
Enjoy Generative Engine Optimisation Support
4
What GEO fixes look like in practice.
These are the patterns that come up repeatedly across healthcare and medtech engagements. The specifics shift from specialty to specialty, but the mechanics behind the fix stay consistent.
"AI never surfaces our clinic when patients in our city search for our specialty."
Building local authority from zero.
GEO Fix:
When geographic and authority signals are too thin or too scattered, language models cannot surface a clinic in local specialty queries with any confidence. The fix involves rebuilding location and specialty pages around consistent terminology, creating condition-led content targeting high-intent local prompts and expanding external authority through medical directories, named clinician profiles and patient review platforms.
What to expect: local presence in AI responses builds incrementally as authority signals accumulate across owned and external sources. Clinics starting from near-zero typically see measurable improvement within eight to twelve weeks of a coordinated push.
"ChatGPT is describing our device's indications incorrectly and it is creating issues with clinicians."
Fixing indication and feature signals.
GEO Fix:
Thin product pages that are inconsistently structured around regulatory language rarely get cited by clinical sources, so AI models fill the gaps with outdated specifications inferred from competitor pages and older reviews. The fix involves rebuilding product documentation as citable, structured content blocks aligned to approved indications, adding FAQ schema, publishing clinical comparison content and distributing the corrected information through trusted medical channels.
What to expect: indication descriptions in AI responses improve as the structured content gets indexed and picked up by external sources. Four to eight weeks is a typical window before the correction becomes visible in monitored responses.
"We appear in AI answers but we are grouped with treatments we do not clinically compete with."
Correcting the clinical comparison set.
GEO Fix:
When category and indication signals are inconsistent across the brand website, external profiles and third-party mentions, AI models draw from contradictory inputs and fall back on generic groupings. The fix involves aligning clinical positioning language across owned and external surfaces, building condition and use-case pages targeting the right comparison queries and updating medical directories, HCP platforms and patient resources to reinforce the correct positioning.
What to expect: clinical grouping in AI responses improves as consistent signals replace the contradictory ones. With aligned language across owned and external sources, the shift typically becomes measurable within six to ten weeks.
"Our visibility across ChatGPT and Claude swings wildly between symptom queries."
Stabilising multi-LLM visibility on symptom-led prompts.
GEO Fix:
When different language models return different recommendations and different clinical contexts for near-identical symptom queries, the root cause is usually semantic inconsistency across the website, metadata and external mentions. The fix involves standardising clinical terminology, improving internal linking between related conditions and treatments and setting up continuous monitoring across ChatGPT, Claude, Gemini and Perplexity to track variations and respond to them as they emerge.
What to expect: stability improves as the underlying signals become more consistent across sources. Monitoring is essential here as without a baseline across multiple LLMs it is difficult to tell whether changes are having any effect.
Who we work with:
Our AEO and GEO work spans private clinics and clinic networks, hospital groups, dental and aesthetic practices, fertility and IVF centres, specialty diagnostic centres, telehealth and digital health platforms, medical device manufacturers, surgical and implantable device brands, in vitro diagnostics, wearable and remote monitoring devices, pharmaceutical companies, biotech and OTC brands, veterinary medicine, mental health and wellness platforms, and healthtech startups across regulated and consumer-facing categories.
In marketing since 2009. In AI search since it became a category.
Our team has worked with international brands across SaaS, e-commerce, fintech and professional services for over fifteen years, originally as ContActive Tech Communications.
Today, that same senior team applies its strategic and technical expertise to answer engine optimisation, generative engine optimisation, and AI search visibility.