Measure the answer. Find the driver. Ship the fix.
Six modules that take a pharma brand from "we have no idea what ChatGPT says about us" to a governed, repeatable programme owned jointly by brand and medical affairs.
What you get
Curated question sets per therapy area, indication and audience, HCP, patient, caregiver, payer, refreshed on a fixed cadence in every market you run.
In practiceRare disease: 40 symptom-shaped GP and caregiver prompts in five languages, because diagnosis questions come long before brand questions.
One score per brand combining citation share, factual accuracy against the label, sentiment and competitive positioning inside the generated answer.
In practiceBiosimilar switching: originator mentioned in 88% of answers but framed negatively in 71%, visibility looked fine, framing did not.
Which domains, journals, guideline bodies, registries and community sources the engines actually leaned on, ranked by influence on your answer.
In practiceAdult vaccines: a single high-traffic forum thread had become a top-cited source in two engines, outranking national immunisation guidance.
Flags for off-label statements, wrong dosing, missing contraindications, outdated approval status or attribution to the wrong molecule.
In practiceOncology: three engines returned a phase-I dose-reduction schedule as if approved; a fourth omitted a boxed contraindication.
Prioritised workstreams with draft briefs, source references and owners, structured so medical, regulatory and legal review runs in days rather than weeks.
In practiceGLP-1 launch: a guideline-page evidence update, question-shaped HCP dosing pages, and a non-promotional compounding-risk explainer.
EU-hosted data, full audit trail on every measurement, role-based access, and exports formatted for internal compliance documentation.
In practiceEvery finding ships with reproducible, time-stamped answer transcripts, the format affiliate medical governance committees accept.
One brand, end to end
The oncology accuracy case shows every module doing its job in sequence, from the prompt that surfaced the problem to the transcripts medical affairs filed.
Daily prompts on dosing and interactions flagged a phase-I dose-reduction schedule being returned as approved guidance.
Driver analysis traced it to an indexed early-phase protocol outranking the SmPC, reproducible across three engines.
SmPC-aligned dosing published in machine-readable form; corrected answers observed across engines within 27 days.
From baseline to programme in one quarter
- 01Weeks 1–3 · Baseline
One brand, one market, four engines. Prompt panel built with your medical team, first answer-quality report delivered.
- 02Weeks 4–8 · Diagnosis
Driver analysis across sources, competitor benchmark, accuracy findings triaged with medical affairs.
- 03Weeks 9–12 · Playbook
Prioritised workstreams into your MLR pipeline, tracking set up, expansion plan for further markets and brands.
Compliance first, always
No. firstQ.ai measures how engines already describe your brands and identifies factual gaps in the public evidence base. What you do with that is decided inside your own MLR process, we give the evidence trail it needs.
Prompt panels are configured per market to respect local rules on naming and audience. Patient-facing panels can be run on molecule and disease terms only.
Weekly by default, daily for launch or crisis windows. Every run is stored so you can show change over time and reproduce a historic answer.
Inside the EU, on European infrastructure, with no customer data used to train third-party models.