KSMModel.ai and PeptideDoc launch 90-day AI visibility study

KSMModel.ai and PeptideDoc have started a 90-day case study to measure how a healthcare practice appears across search engines and AI platforms, with Day 1 now frozen as the baseline. The project focuses on whether AI systems can accurately understand, connect, and cite physician-led healthcare information.

Why it matters: - Healthcare visibility is no longer just about search rankings. - AI systems also need to identify the right physician, practice, services, and location. - Independent corroboration can determine whether AI platforms treat a medical practice as trustworthy and citeable.

What happened: - KSMModel.ai and PeptideDoc.com announced a 90-day AI Visibility case study for healthcare marketing. - The study tracks how PeptideDoc’s digital presence changes across search engines, generative AI platforms, and other AI-driven discovery experiences. - The project is led by Dr. Anthony Q. Bowen, founder of KSMModel.ai. - Bowen’s background includes SEO, digital marketing, digital transformation, consulting, and AI work across client, agency, and consulting settings. - Bowen also teaches graduate marketing at Grand Canyon University and previously taught at the Jack Welch Management Institute and Southern New Hampshire University.

The details: - The study uses the Knowledge Structuring Model™ (KSM™) at four measurement points: Day 1, Day 30, Day 60, and Day 90. - KSM™ is framed around three checks: structured extractability, entity salience, and citation authority. - Structured extractability tests whether search engines and AI systems can retrieve and interpret an organization’s information. - Entity salience tests whether AI can correctly connect the organization to its people, services, locations, credentials, and related entities. - Citation authority tests whether trusted external sources support those relationships strongly enough to reinforce credibility. - For PeptideDoc, the study is evaluating whether AI can identify the practice as physician-led, connect Dr. Saul F. Maslavi, MD to the founder-and-owner role, tie the practice to Bayside, Queens, and verify those relationships through external sources. - The study also looks at corroboration from health-system profiles, insurer directories, professional registries, and other authoritative sources. - PeptideDoc started with a pre-launch KSM™ AI Visibility score of 35/100. - The formal Day 1 assessment scored 62/100, a 27-point gain. - Day 1 is frozen as the baseline for the study, and later improvements will be measured against that score rather than added retroactively. - Early work focused on making organization, physician, service, and location information more consistent. - Priority pages received clearer titles, descriptions, canonical URLs, indexing signals, and structured data. - The core entity chain was clarified as Saul F. Maslavi, MD → Founder & Owner → PeptideDoc → Bayside, Queens → Physician-Led Medical Services. - Bing Webmaster Tools later showed a priority service page as eligible for indexing after it had previously been flagged as not allowed for indexing. - Independent health-system and insurer directories corroborate Dr. Maslavi’s Family Medicine identity, the Bayside location, a telephone number, and National Provider Identifier 1134137383. - PeptideDoc is a physician-led practice in Bayside, Queens, serving eligible patients from New York City and the broader Tri-State area. - Saul F. Maslavi, MD is a board-certified Family Medicine physician with more than 20 years of medical experience. - PeptideDoc provides medically supervised care that includes medical weight management, peptide care, functional and integrative medicine, hormone optimization, metabolic health, recovery, body composition, and individualized wellness.

Between the lines: - The study reflects a broader shift in healthcare marketing from simple discoverability to AI-readable identity. - The emphasis on external authority suggests that being found by AI is not enough; systems must also be able to verify and connect the facts. - Freezing the Day 1 score creates a clean benchmark for measuring future gains in AI visibility.

What's next: - KSMModel.ai will measure results at Day 30, Day 60, and Day 90. - The next phase will assess search-engine recognition, physician and practice entity consistency, Google Business Profile alignment, healthcare directory references, professional profiles, external mentions, and branded and non-branded AI queries. - The study will also track how major AI platforms describe PeptideDoc and Dr. Maslavi.

The bottom line: - The project is designed to show whether a healthcare practice can become not just searchable, but understandable, verifiable, and citable by AI.