Whether your team is already using AI without a framework, or hasn't yet found a way to start safely, the problem is the same: no governance layer, no validated system, no one with the scientific and regulatory expertise to build one. Claros AI Scientific builds that infrastructure for medcomms agencies — so your team can use AI without compromising content accuracy, ABPI compliance, or client trust.
Average MLR review cycle for promotional content
Year-on-year increase in agency content volume
Medcomms agencies with a formal AI governance policy
Speed, cost per deliverable, and capacity to handle volume without headcount growth — these will increasingly separate agencies. The question is not whether AI will change medcomms workflows. It is whether your agency captures that advantage or cedes it to competitors who act sooner.
Unpublished compound data, trial results, proprietary product information — being submitted to consumer AI tools under no data classification policy, no GDPR position, no audit trail. This is a liability that compounds with every project and that most agency leadership has not yet formally addressed.
AI-generated content without scientific validation introduces off-label language, missing safety statements, and unsupported claims. The errors surface at MLR review — where they cost the most to fix. Without guardrails, AI accelerates the production of problems rather than solving them.
They cannot explain what makes a clinical claim substantiated, why safety language placement matters in an HCP document, or what an ABPI Code violation looks like in a congress abstract. Implementing AI correctly in this environment requires someone who has worked on both sides of the content.
Average first-draft time per congress abstract
Before implementationWith validated prompt templates
After implementationTime to full ROI on governance engagement
Measured outcomeAI use is ungoverned — no policy, no audit trail, no data classification
No GDPR position on which content categories can be processed by AI tools
3.5 hours per congress abstract, manual first draft every time
MLR rejection rates unchanged — AI errors caught at the most expensive stage
A governance policy covering ABPI Code, GDPR, data classification, and audit requirements
Data classification rules defining what can and cannot be processed through AI systems
Validated prompt templates tested against your actual content types and therapeutic areas
Pre-MLR quality checking catching off-label language and missing safety statements before submission
Dr Jas Gill
Physician · Strategy Consulting · AI Implementation
Jas is a physician whose career spans strategy consulting, AI product development, and life sciences. At McKinsey, he led biopharma and healthcare strategy engagements spanning medical affairs, real-world evidence synthesis, and market access, building compliance and content requirements for medcomms environments, including MLR review and ABPI-regulated materials. In regulated AI, he led strategic partnerships and drove go-to-market strategy for AI products at Microsoft AI Health, developing clinical AI safety frameworks and LLM output evaluation methodology in environments where the consequences of errors matter. As an independent expert, he now designs and delivers scientific AI infrastructure — governance frameworks, validated content pipelines, and pre-MLR quality systems.
Dr Natasha Rangwani
Scientist · Medical Writer · MSL
Natasha holds a first-class MSci and a PhD in Neuroscience, and has worked across both sides of the medcomms environment. As a medical writer at a leading global medcomms agency, she produced publications, medical education, advisory board content, and congress materials across gastroenterology, oncology, respiratory, cardiology, and haematology — giving her direct experience of the production workflows that AI systems must genuinely serve. As a Senior Medical Science Liaison at mid-cap and big pharma, she reviews promotional materials against the ABPI Code and delivers internal ABPI training to cross-functional teams, bringing the pharma-client perspective on what medcomms output must withstand in regulatory review. She is the clinical accuracy and ABPI compliance layer of every Claros AI Scientific engagement.
A 30-minute diagnostic call to map your current AI use, content types, client agreements, and regulatory exposure. You receive a written assessment — no obligation to proceed. All engagements begin under NDA and AI processing uses Anthropic's API with zero data retention.
Governance frameworks, prompt templates, or quality systems designed for your specific agency. Tested against your actual content before delivery. Weekly progress updates throughout.
Full documentation, team training sessions, and a maintenance guide. Your team owns the system completely — no ongoing retainer dependency.
Every engagement is fixed-fee with defined deliverables. The diagnostic call establishes which of these is the right starting point for your agency — typically the governance policy, since it is the prerequisite for everything else.
A comprehensive governance framework written for your specific content types, client base, and regulatory context. Covers ABPI Code compliance, GDPR obligations, data classification, and audit trail protocol.
Validated prompt library for congress abstracts, symposia decks, HCP summaries, and patient materials. 10–15 templates tested against your actual content, with Claude Projects setup and team training.
AI-assisted pre-submission checking for off-label language, missing safety statements, and reference gaps. Structured issue reports for writer action before formal MLR review.
AI-augmented systematic review with PICO-defined abstract screening, data extraction templates, and PRISMA documentation. Human validation checkpoints built into every stage.
The first conversation is a 30-minute diagnostic — not a sales call. We'll map your current AI use, identify regulatory exposure, and determine whether a formal engagement makes sense for your agency.
If there's a fit, you'll receive a scoped proposal with a fixed fee within 48 hours. If not, you'll still leave with a clear picture of where your risks are.
2 new engagements available for Q3 2026.
Diagnostic calls booking now.