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AI Citation Intelligence for Pharma

Measure how often AI engines cite your brand in clinical answers.

Findabl queries ChatGPT, Gemini, Perplexity, and Claude with the unaided questions clinicians and patients ask, records which brands and sources each engine cites, and flags where answers diverge from the approved label. Findings feed your MLR review. Findabl publishes nothing on your behalf.

Free. About 60 seconds. No card.

Recommendations checked againstFDA 21 CFR Part 202OPDP guidanceFair balanceMLR routing
Sample outputacmehealthcare.com
1/4
Cited by 1 of 4 engines
Citation rate 25% across the questions tested
ChatGPT Not cited
Gemini Not cited
Perplexity Cited
Claude Not cited
Cited instead: a competitor in 3 of 4 answers
Tested acrossChatGPT·Gemini·Perplexity·Claude
66%

of U.S. physicians reported using AI in practice in 2024, up from 38% the year before.

American Medical Association, 2024

38%

of pages cited in Google AI Overviews also ranked in the organic top 10 in 2026, down from 76% a year earlier.

Ahrefs, 2026

8%

of searches lead to a result click when Google shows an AI summary, versus 15% without one.

Pew Research Center, 2025

The mechanism

How a citation actually happens

An unaided buyer question goes to four engines. Each engine writes an answer and cites a few sources. Some of those sources are publishers. Some are competing brand sites. Sometimes yours. Findabl measures which.

How a citation actually happensAn unaided buyer question is sent to ChatGPT, Gemini, Perplexity, and Claude. Each engine cites some sources and not others. In this example, only Perplexity cites the brand site; the other three cite a competitor brand site plus publishers like drugs.com, webmd.com, and pubmed.gov.UNAIDED BUYER QUESTION"What treatment options are available for [condition]?"ChatGPTENGINEGeminiENGINEPerplexityENGINEClaudeENGINEdrugs.comPUBLISHERwebmd.comPUBLISHERyour brand.comYOUR BRANDcompetitor brand.comCOMPETITOR BRANDpubmed.govPUBLISHERcitednot citedFindabl records every engine, every source, every run.

Example only. Real runs use the unaided questions your team approves and the engines you select.

Why this matters

Physician AI use roughly doubled, from 38% in 2023 to 66% in 2024 (AMA). Clinical questions are increasingly answered by an AI engine before a clinician reaches a rep or the label.

When an engine does not cite a brand, it cites other sources. In Google AI Overviews only about 38% of cited pages also rank in the top 10 (Ahrefs, 2026), so a brand site can be absent while third-party pages are cited.

Fair-balance and off-label rules attach to a drug claim, not to who produced it (21 CFR 202.1). On Sept 9, 2025 the FDA issued about 100 cease-and-desist letters in a DTC ad crackdown and said it used AI tools to surveil drug ads (FDA).

What the rules require

Findabl checks AI-facing and on-page content against the regulations that govern prescription-drug promotion.

Fair balance, 21 CFR 202.1

FDA requires risk information in prescription-drug ads to appear with prominence comparable to benefit information. Findabl flags AI answers and on-page content that present benefits without the corresponding risks.

Off-label and OPDP

Promotion of unapproved uses is prohibited, and FDA's OPDP reviews promotional material. Findabl flags where an answer describes a product, or a competitor, beyond its approved label.

Findings feed MLR review

Each recommendation carries the observed data behind it and a verification step, tagged Quick Fix, Needs MLR Review, or Roadmap. Findabl produces findings for your review and publishes nothing on your behalf.

The workflow

How a scan moves through Findabl

From input to output, three stages. Every line below is verifiable against the shipped product.

How a scan moves through Findabl. Three stages. INPUT: your brand URL or a project's tracked URLs; the unaided buyer questions your team approves, with the brand name never injected; the engines you select, ChatGPT, Gemini, Perplexity, Claude. AUDIT: each engine answers each question and Findabl records who is cited and which sources fill the answer; the brand's public pages are checked against fair-balance and off-label rules; each recommendation is tagged Quick Fix, Needs MLR Review, or Roadmap. OUTPUT: citation rate per engine, per question; the sources cited instead, with publishers separated from competing brand sites; a reviewed action list with the observed data point and a verification step.
  • INPUT: What goes in.
    • Your brand URL, or a project's tracked URLs.
    • The unaided buyer questions your team approves. The brand name is never injected.
    • The engines you select. ChatGPT, Gemini, Perplexity, Claude.
  • AUDIT: What Findabl does.
    • Each engine answers each question. Findabl records who is cited and which sources fill the answer.
    • The brand's public pages are checked against fair-balance and off-label rules.
    • Each recommendation is tagged Quick Fix, Needs MLR Review, or Roadmap.
  • OUTPUT: What you get back.
    • Citation rate per engine, per question.
    • The sources cited instead, with publishers separated from competing brand sites.
    • A reviewed action list with the observed data point and a verification step.

Illustrations show sample output. Real runs use your approved questions and the engines you select. The blended GEO Score uses 70% citation rate + 30% site readiness (ADR-005).

Sources

Run a scan on your brand

Findabl returns your citation rate across ChatGPT, Gemini, Perplexity, and Claude, the sources cited instead, and a reviewed action list.

Free. No card. About 60 seconds.