BoilyTrack Hospital AI Search Visibility

Patients are increasingly asking conversational tools questions that used to belong to Google: Which dental clinic is good for implants nearby? Where should a parent take a child for pediatric care? The answer often contains only a short list of providers, which makes visibility a meaningful problem for hospitals and clinics that depend on local discovery. Boily is a South Korea-focused service built around that shift. Rather than buying ads or promising a place at the top, it checks how often a hospital is mentioned by major AI assistants and turns those observations into recurring reports.

That positioning matters. AI recommendations are not ordinary search rankings, and no outside service can reliably control exactly what a language model will say in every conversation. Boily describes itself as a measurement and preparation service for GEO and AEO, or optimization for generative and answer-oriented search. Its stated approach is deliberately cautious: establish a baseline, identify patterns, make optional changes, and measure again. That is a more credible proposition than a guaranteed ranking claim, especially in a regulated healthcare advertising environment.

Why AI visibility is becoming a local healthcare issue

Traditional search visibility can be influenced by website authority, keyword targeting, map listings, reviews, and paid placement. Generative systems use a less transparent mix of web content, available sources, user context, and model behavior. They may synthesize an answer and mention just two or three hospitals, leaving other legitimate providers out of the response altogether. A polished website does not automatically solve that problem, particularly when important information is hidden inside images or presented in a format that automated systems cannot interpret easily.

Boily’s practical question is simple: when people ask AI tools about a hospital category or neighborhood, how often does a particular clinic appear? The service can test prompts related to locations and treatments, then compare the hospital’s mentions with those of other nearby providers. A local dental clinic, for example, could use the results to see whether it is being surfaced for implant-related questions while appearing rarely for broader neighborhood searches. This is not a patient-quality score or a clinical recommendation; it is a visibility signal that helps a marketing team decide what to investigate.

What the recurring reports measure

Boily measures the same hospital across ChatGPT, Claude, Gemini, and Perplexity. Looking across several systems is useful because their answers can differ considerably. A clinic might be mentioned frequently by one assistant and rarely by another, while a competitor may show the opposite pattern. The service’s public examples include different exposure percentages by engine, but those numbers are illustrative rather than a promise of typical performance. Actual results depend on the hospital, prompts, timing, location, and changes in each platform.

• Exposure rate for selected questions, such as how many test responses mention the hospital.
• Competitor comparisons using the same group of local healthcare queries.
• Breakdowns by AI engine and by individual question, rather than one blended score.
• Written observations that point out differences and possible areas for review instead of handing over raw tables alone.

The default cadence is every two weeks. That schedule is more useful than a one-time screenshot because AI-generated answers can vary from one run to another. Repeated measurements can reveal whether a change is persistent or simply the result of prompt variation. Still, users should treat the reports as directional evidence, not a scientific ranking system. The public information does not describe a fully open methodology, sample size, or independent validation process, so hospitals should ask for those details before using the data to support major business decisions.

From measurement to website restructuring

Boily also offers optional optimization work for hospitals that want to act on weak visibility. One of its central ideas is to preserve the site patients see while improving the underlying information layer. Many healthcare websites place treatment descriptions, doctor biographies, and frequently asked questions in images or highly visual components. Those pages may look fine to a person but offer less readable material to crawlers and other automated systems.

The proposed approach is to recreate the existing site without changing its visual identity, then convert key information into accessible text and add structured data. Areas such as services, medical staff, procedure explanations, and FAQs can be organized so that search engines have clearer signals to process. Boily says the resulting site can be submitted to indexing systems including Google, Bing, and Naver, and that an existing domain may continue to be used where appropriate. In practical terms, this is less a redesign than a technical and editorial rebuild beneath a familiar surface.

That can be a sensible option for a clinic that has invested heavily in branding but has an image-heavy or difficult-to-crawl website. It also carries risks. A cloned or rebuilt site needs careful review for accessibility, canonical URLs, redirects, consent language, medical claims, and duplicate-content problems. Healthcare information must remain accurate and compliant, and no markup strategy can compensate for thin, outdated, or exaggerated content. Boily’s own public material characterizes its observations as limited and non-public, so claims that text restructuring directly caused better AI exposure should be treated as hypotheses to test rather than established proof.

Who Boily fits, and what to check before signing up

The service is designed for hospitals and clinics operating in South Korea, particularly practices that rely on local patients and want to understand how AI assistants describe them. Dentistry, pediatrics, dermatology, and other location-sensitive specialties are natural candidates, although the public description does not limit the service to those fields. The onboarding flow is presented as lightweight: sign in with a Kakao account, provide hospital details and target keywords, confirm payment by bank transfer, and begin an initial measurement. Reports then continue on a two-week cycle, while optimization is scoped separately with the hospital.

• Define a small set of real patient questions before testing; generic keywords alone may produce less useful findings.
• Keep a record of website edits, review changes, and major platform updates so later reports have context.
• Ask how prompts, locations, languages, sampling, and repeat runs are handled before treating exposure percentages as benchmarks.
• Separate visibility from clinical quality: appearing in an AI answer is not evidence that a provider is medically superior.

Boily’s price is not publicly listed, and the disclosed payment method is bank transfer. That lack of a standard public plan makes direct consultation necessary, especially when the optimization work is customized. The service is also not presented as a global product, so hospitals outside South Korea may find the language, search ecosystem, and operating assumptions less relevant. For Korean healthcare marketers, however, its value is easy to understand: it provides a recurring instrument for observing a channel that conventional SEO dashboards do not fully capture.

Boily is best viewed as an early monitoring and optimization service, not an oracle for AI recommendations. Its strongest feature is the combination of cross-engine comparisons and repeat measurements, while its biggest open questions involve methodology, pricing, and the evidence behind optimization results. Hospitals considering it should begin with a baseline and clear patient-style prompts, then judge progress over several reporting cycles rather than expecting an immediate ranking change.