AI visibility for life sciences

When AI answers a health question, your brand should be the first one it trusts.

HCPs, patients and payers now ask an AI engine before they open a search results page. firstQ.ai measures how each engine represents your pharma brands, indications and evidence, and builds the compliant action plan to improve those answers.

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Answers tracked across

ChatGPTGeminiClaudePerplexityGoogle AICopilot

The shift

One synthesised answer replaced ten blue links, and pharma is the hardest place to get it wrong.

Search was the discovery channel for two decades. Today a physician asks one question and receives one answer, assembled from guidelines, journals, registries, patient forums and whatever else the model trusts. If your evidence is not in that mix, the model fills the gap with someone else's.

Answers are opaque

No rank, no impressions. Just a paragraph that either mentions your brand correctly, mentions a competitor, or gets the label wrong.

Accuracy is a safety issue

An engine that misstates an indication or contraindication is not a marketing problem, it is a medical affairs one.

Promotion rules still apply

Everything you do to influence answers must survive EFPIA codes, national rules and MLR review. Growth hacks are not an option.

How firstQ works

A continuous loop: define the answer you need, diagnose what blocks it, ship the fix.

firstQ.ai is not another dashboard. It starts from the answer your brand team needs to win, finds the drivers behind today's answer, and hands your teams work they can execute this quarter.

Define

Pick the therapy areas, indications, HCP and patient prompt categories and competitor brands where the answer has to go your way.

Monitor

Continuous prompt panels across engines and markets track citation share, sentiment, indication accuracy and off-label risk.

Diagnose

Driver analysis shows which sources, publications, monographs and registries are shaping the answer, and which gaps are blocking you.

Execute

A Playbook of MLR-ready briefs, source fixes and content workstreams your brand and medical teams can actually ship.

Built for outcomes

Made for brand, medical affairs and digital teams who share one score.

Own the prompts that matter

From "best treatment for moderate plaque psoriasis" to "is X safe in pregnancy", we map the real questions HCPs, patients and payers ask AI engines in each market.

Benchmark against competitor brands

See your share of AI answers versus originators, biosimilars and guideline bodies, split by engine, market and audience.

Catch misinformation early

Automatic flags when an engine misstates indication, dosing, contraindications or approval status, with evidence trails for medical affairs.

Turn insight into compliant action

Every recommendation ships as a brief with source references, so regulatory and MLR review is fast rather than blocking.

AI engines monitored continuously

300+

Therapy-area prompts per brand

European markets supported

100%

EU-hosted, GDPR-first data

Pharma use cases

What this looks like on a real brand, five worked scenarios.

Not personas and not a template. Each case starts from the prompts HCPs, patients, pharmacists and payers actually typed, and ends with what brand and medical affairs shipped to change the answer.

Cardiometabolic · obesity · HCP + patient

A GLP-1 obesity brand losing the launch conversation to compounded copies

Two weeks before a national obesity launch, the brand team had no view of how AI engines answered weight-management questions. Consumer prompts were dominated by compounding pharmacies and telehealth resellers; HCP prompts leaned on a competitor's head-to-head trial.

“What is the most effective weight loss injection available in Germany?”

12% → 38%

Patient-prompt citation share in 14 weeks

Outdated sources corrected at origin

Read the full case Run it on my brand

Immunology · biosimilars · HCP + payer

An originator biologic being written out of switching answers

When hospital pharmacists asked AI engines about switching stable patients from the originator to an adalimumab biosimilar, the answers read as a straightforward cost decision. Nothing in the answer mentioned device differences, citrate-free formulation or nurse-support programmes.

“Should stable rheumatoid arthritis patients be switched to an adalimumab biosimilar?”

71% → 34%

Negatively framed switching answers

Markets tracked on payer prompt panel

Read the full case Run it on my brand

Oncology · targeted therapy · HCP + medical affairs

An engine stating the wrong dose reduction for a targeted oncology therapy

A medical information team noticed a pattern of enquiries citing a dose-reduction schedule that did not exist in the SmPC. The source turned out to be an AI answer reproducing an early-phase protocol as if it were the approved regimen.

“Dose reduction schedule for grade 3 hepatotoxicity on [molecule]”

Safety-critical inaccuracies documented

27 days

From detection to corrected answers across engines

Read the full case Run it on my brand

Rare disease · enzyme replacement · GP + patient community

A rare disease invisible in the questions that precede diagnosis

The commercial problem was not brand share, it was that patients waited an average of six years for diagnosis. Increasingly, the first place a symptomatic adult or a puzzled GP describes those symptoms is an AI chat.

“Adult with unexplained proximal muscle weakness and elevated CK, what could it be?”

9% → 46%

Symptom answers naming the condition

Languages in the prompt panel

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Vaccines · adult immunisation · Patient + pharmacist

Adult vaccine answers drifting toward hesitancy sources

In three markets, answers to routine adult RSV and shingles vaccination questions began hedging, quoting forum threads and a retracted preprint alongside national immunisation guidance.

“Is the RSV vaccine safe for a 68-year-old with COPD?”

8 wks

Drift detected before it reached campaign reporting

Markets on continuous sentiment alerting

Read the full case Run it on my brand

What teams tell us

Trusted by pharma brand and medical teams across Europe

We could finally see why the engines recommended the competitor in three of our five priority markets, and what evidence was missing.

Global Brand Director, immunology

The misinformation alerts alone justified the platform. Two dosing errors were corrected at source within a month.

Medical Affairs Lead, Nordics

Recommendations arrive as briefs with references, so MLR review stopped being the bottleneck.

Head of Digital, specialty care

See how AI describes your brand today.

We run a free baseline audit on one brand, in one market, across four engines, and walk your team through the answers.

Book a brand audit