At launch, your brand lives in two AI conversations.
One a prescriber has, one a patient has, and they ask AI completely different questions. Findabl tracks both. It queries ChatGPT, Gemini, Perplexity, and Claude with the unaided questions each audience asks, records which brands and sources every engine cites, and flags where an answer diverges from the approved label. Findings feed your review. Findabl publishes nothing on your behalf.
Free. About 60 seconds. No card.
Where Findabl works
Findabl is built for commercialization: from launch through in-market, when AI engines start answering the questions your prescribers and patients ask.
of U.S. physicians reported using AI in practice in 2024, up from 38% the year before.
American Medical Association, 2024
of pages cited in Google AI Overviews also ranked in the organic top 10 in 2026, down from 76% a year earlier.
Ahrefs, 2026
of searches lead to a result click when Google shows an AI summary, versus 15% without one.
Pew Research Center, 2025
The commercialization view
Two audiences, two question sets
A launched brand answers to two readers in AI search. A prescriber and a patient ask different questions, get cited different sources, and carry different risks. Findabl tracks both in one project.
Healthcare professionals
What a prescriber asks AI
Questions they ask AI
- "first-line treatment for [condition]"
- "[drug class] efficacy and dosing"
- "[your brand] vs [competitor], head to head"
- "mechanism of action of [molecule]"
- "where [brand] sits in the [society] guidelines"
Usually cited from: PubMed, the FDA label, and clinical guidelines.
The risk: An engine cites a competitor's molecule, leans on outdated efficacy data, or describes a product beyond its approved label.
Patients and caregivers
What a patient asks AI
Questions they ask AI
- "is [brand] safe"
- "side effects of [brand]"
- "what is [brand] used for"
- "[brand] vs [brand], which is better"
- "can I take [brand] with [other drug]"
Usually cited from: drugs.com, your official site, and MedlinePlus.
The risk: An engine gives a patient wrong safety information, drops the required risk context, or never names your brand at all.
One project, both lenses, tracked side by side. Bracketed terms are placeholders. Real runs use the unaided questions your team approves.
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.
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 your review
Each recommendation carries the observed data behind it and a verification step, tagged Quick Fix, Needs 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.
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.
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 Review, or Roadmap.
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).
The intelligence layer
Sophia reads your scan and tells you what to fix first.
Most tools hand a commercialization team a dashboard and leave. Sophia turns it into a plan. She reads where ChatGPT, Gemini, Perplexity, and Claude cite your brand, your GEO Score, and which sources get cited instead, then says it in plain English: why a competitor is cited despite weaker evidence, why AI does not surface you unaided even when your data is strong, and the one move that changes it this week. Everything stays evidence-based and ready for medical, legal, and regulatory review.
Measure: see where the four engines cite you, unaided. Your real GEO Score, not a modeled guess.
Understand: Sophia explains why, grounded in your actual scan.
Create: she names the assets and answers to publish that earn citations.
Earn: and which journals, medical publications, and indexed sources build the third-party authority AI trusts.
Measure again: re-scan and watch your citation share move.
Sophia diagnoses and writes the plan. Your team runs the plays. Auto-publishing is on the roadmap.
Sources
- American Medical Association, 2024: physician AI use 66% (38% in 2023)
- Ahrefs / Search Engine Journal, 2026: AI Overview citations ranking top 10: ~38%, down from 76%
- Pew Research Center, 2025: result clicks with vs without an AI summary (8% vs 15%)
- 21 CFR 202.1(e), Cornell LII: fair-balance requirement for prescription-drug ads
- U.S. FDA, Sept 2025: DTC ad crackdown; AI used to surveil drug ads
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.