Hub for Pharma Marketing

Therapeutic marketing material cannot ship until a medical signatory has confirmed it complies — with FDA regulation, with the PhRMA Code on Interactions, and with whatever additional guidance applies to that region and that therapy. That review is manual, it is a bottleneck by design, and it is performed on documents where the risk is often in the artwork: an efficacy chart with a truncated axis, a claim carried in an image rather than in body copy. Reviewers read the text carefully and the visuals quickly, which is the wrong way round.

• Compliance risk frequently sits in charts and imagery, which text-based review tools cannot read.
• The applicable rules span regulation, industry codes and region- and therapy-specific guidance held in different places.
• Signatory review is the throughput limit on every launch and every localisation.
• Approved content is not reusable in practice, so each new asset restarts the review cycle.

Hub works on the whole document rather than its text layer. Large language vision models extract comprehensive context from marketing material and prescriber information — text, images, charts and graphs — so an efficacy graphic in a detail aid is reviewable on the same footing as a sentence. Against that extraction, a centralised, versioned repository of compliance rules is applied: industry regulation such as FDA requirements and the PhRMA Code on Interactions, alongside tailored guidance for specific regions and therapies, created or imported by the organisation. Discrepancies are surfaced to the user with suggestions for enhancement rather than a pass-or-fail verdict. For generation and localisation, a human-approved content ontology supplies material the system can recommend for reuse, with rephrasing options, so new documents start from language that has already cleared review. Privacy and security underpin the workflow, with granular access controls enforced across GDPR, FISMA, HIPAA and GxP obligations.

Vision is not a nice-to-have here; it is the requirement. A promotional claim expressed as a chart is legally identical to one expressed as a sentence, and a review system that reads only text is systematically blind to a whole category of risk. The second model task is matching extracted content against rules written as prose across several codes and jurisdictions — a comprehension problem, not a checklist. What the system explicitly does not do is approve: it surfaces discrepancies and proposes wording, and the medical signatory remains the person who signs.

EXTRACTION

Vision models over text, images, charts and graphs

RULES

Centralised, versioned repository — regulation, codes, local guidance

OUTPUT

Discrepancies surfaced with suggested enhancements

REUSE

Human-approved content ontology with rephrasing options

LOCALISATION

Compliant generation for geographic and therapeutic requirements

GOVERNANCE

Granular access control; GDPR, FISMA, HIPAA and GxP

1. 01Comprehensive context extracted from image-heavy advertisements and detail aids alike.
2. 02Efficacy data in charts and graphs made reviewable, not skimmed.
3. 03Versioned compliance rule library covering regulation, industry codes and local guidance.
4. 04Discrepancies surfaced with enhancement suggestions rather than a binary verdict.
5. 05Human-approved content recommended for reuse, with rephrasing options for new material.
6. 06Localisation for specific geographic and therapeutic requirements, generated compliantly.

Text + visual

Review coverage, against text-only toolingdesign intent

Versioned

Rule repository, against distributed guidancedesign intent

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