A real Gap Report. Names hidden, findings intact.

1 · Executive summary

The practice currently has no presence in AI-generated recommendations across four major conversational AI engines (ChatGPT, Gemini, Grok, Claude), despite operating in a high-intent market segment where patients actively seek plastic-surgery providers in New York. A visibility score of 11/100 reflects systematic exclusion rather than marginal ranking — competitors are surfaced repeatedly while the practice is never mentioned, cited, or recommended. This gap is not a ranking issue; it is a discovery and citation problem rooted in how AI engines identify, extract, and recommend trustworthy service providers.

2 · How the scan ran

Five high-intent prompts were tested across all four engines, each designed to capture buyer behaviour at the moment of provider selection:

• Best plastic surgery companies in New York
• Who should I hire for plastic surgery services in New York?
• Top recommended plastic surgery providers near New York
• Most trusted plastic surgery firms in New York
• Plastic surgery experts in New York — who stands out?

Each prompt was run against every engine. Rather than guessing, we read the actual answer each engine returned and graded whether the practice appeared — recommended, mentioned, cited, displaced by a competitor, or excluded. All prompts target direct decision-intent and geographic specificity. (The free scanner samples a subset of these prompts; the full Gap Report runs the complete set.)

3 · AI recommendation coverage

The practice received zero recommendations across all four engines. The following competitors were surfaced instead:

The consistency of exclusion across independent platforms points to a structural visibility problem rather than engine-specific bias.

4 · Citation coverage

The practice was not named, mentioned, cited, or referenced in any response across any engine. Competing practices received explicit recommendation language, detailed descriptions, and positioning as trusted providers. The absence of citation is absolute — the practice does not appear even as a secondary mention, alternative, or contextual reference.

5 · Competitor coverage

Four distinct competitors were identified as the primary surfaced alternatives across the engine landscape. Each appears to benefit from at least one engine's recommendation set, suggesting they have achieved sufficient structured visibility, third-party citation, or authority signalling for AI systems to include them in response generation. The fact that different engines surface different competitors indicates no single rival has saturated all four platforms — an opportunity gap for the practice.

6 · Authority gaps

AI recommendation systems prioritise providers that demonstrate extractable authority signals: verifiable credentials, third-party citations from medical directories, press coverage, and institutional affiliations. The practice's current digital footprint likely lacks the structured, machine-readable signals these engines use to validate and recommend service providers. Without them, even high-quality descriptions on its own website remain invisible to recommendation algorithms.

7 · Structure gaps

The website and content do not appear to be formatted in ways that let AI systems easily extract, verify, and cite credentials, specialties, patient outcomes, or institutional authority. This includes the absence of structured metadata, third-party endorsements, and strategic positioning within the information networks these engines crawl and reference. The gap is not the visibility of the website itself — it is the discoverability and citability of the practice as a recommended provider.

8 · Prompt-intent matrix

All five prompts target the same user intent: active provider selection in a high-stakes, trust-dependent category. A provider with strong citation presence would expect to be recommended in at least two to four of five identical intent tests; a presence of zero indicates the practice is not yet integrated into any engine's recommendation knowledge base.

9 · Off-site authority snapshot

The competitive landscape shows that multiple New York–based plastic-surgery practices have achieved enough third-party recognition and authority signalling to be included in AI recommendations. This establishes that the market segment itself is actively indexed and that engines are configured to surface plastic-surgery providers. The barrier here is not market coverage — it is individual practice visibility and citation authority.

10 · Priority fixes

The following categories of work will directly impact AI visibility:

• Structured credential & specialisation mapping — credentials, board certifications, specialties, and treatment outcomes formatted so AI systems can extract and verify them as authoritative signals. Engines cannot recommend what they cannot reliably identify and validate.
• Third-party citation authority — presence within medical directories, healthcare review platforms, and professional networks that AI systems treat as authoritative. Engines prioritise providers cited by external, trusted sources over self-reported information.
• Service-page architecture — structured, extractable content engines need to cite the practice when patients ask for specific procedure expertise. This directly affects whether it appears in intent-matched queries.
• Institutional & practitioner authority signals — board certification, fellowship training, professional affiliations, and published outcomes featured and formatted for both human and machine readability.
• Local-market positioning — integrated positioning within New York's healthcare authority ecosystem so location-specific queries trigger inclusion.

11 · Final recommendation

The practice requires a comprehensive AI-visibility program. The depth of exclusion across four independent platforms indicates this is not a quick-fix ranking issue — it reflects gaps in how the practice is structured, cited, and positioned for discovery by AI recommendation systems. An Implementation engagement addressing credential mapping, third-party authority integration, and content restructuring establishes the foundation; a Growth retainer then monitors performance across these four engines and adjusts as competitive activity and platform updates evolve.

AI visibility is measured through repeated prompt sampling and should be read directionally; month-to-month change can reflect optimisation work, competitor activity, or platform updates.