Pharmaceutical AI Governance & Regulatory Operations
Helping pharmaceutical companies operationalize enterprise AI governance within Regulatory Affairs and labeling — from AI risk to regulatory control to auditable evidence.
RAAF — Turning AI Governance into Regulatory Affairs Execution
RAAF is a practical, risk-based framework for governing, assessing, assuring, and managing AI used within pharmaceutical Regulatory Affairs. It provides an operational bridge between enterprise AI governance and real regulatory workflows, decisions, human responsibilities, controls, and evidence.
Context of Use→Risk & Impact→Regulatory Controls→Human Oversight→AI Assurance→Auditable Evidence
Risk Follows Use
The same AI technology can create very different regulatory risk depending on purpose, workflow, decision influence, human authority, and evidence requirements.
30-Control Architecture
GOVERN 7 · MAP 7 · ASSURE 9 · MANAGE 7. Controls are applied proportionately to the AI Context of Use and risk.
Evidence by Design
RAAF connects the use case to risk, controls, tests, results, evidence, and human decisions so governance can be demonstrated.
From Enterprise AI Policy to Operational Regulatory Controls
RAAF consulting starts with the client's actual Regulatory Affairs AI use case and translates governance expectations into practical controls, assurance activities, human decision authority, and evidence.
Assess
Design
Assure
Implement
Monitor
Start Small: 30-Day RAAF Pilot
Take one real Regulatory Affairs AI use case through Context of Use, risk and impact assessment, control applicability, assurance requirements, evidence expectations, and an executive roadmap.
Coming Next — Free RAAF Assessment
How Much Governance Does Your Regulatory AI Use Case Need?
A short, no-cost preliminary Context-of-Use assessment is being developed to help Regulatory Affairs teams think through decision influence, human oversight, traceability, regulatory impact, and appropriate assessment depth.
Working applications demonstrating practical Regulatory Affairs AI engineering, structured content, human oversight, assurance, traceability, and governed automation. These workspaces complement RAAF by showing the technology and workflow challenges that governance must address.
eCTD v4.0 Bi-Directional AI Resolver
NEW
An intelligent lifecycle communication engine that ingests inbound FDA eCTD v4.0 Information Requests (IR), maps Context of Use (CoU) concepts, drafts GxP response letters via AI, and auto-synthesizes outbound XML with compliant target bindings.
An automated diagnostic suite evaluating dossier readiness for ICH v4.0 migration. Performs pre-validation checks on Context of Use (CoU) assignments, Controlled Vocabulary compliance, and XML schema integrity prior to gateway submission.
An in-memory metric extraction pipeline powered by PyMuPDF and Llama 3.3. Evaluates PDF layout complexity, text density, vector graphics, and embedded tables to dynamically route regulatory documents to the optimal extraction engine (PyMuPDF, Docling, OCR, or Hybrid).
An automated regulatory document classifier that extracts in-memory streams from protocol PDFs and evaluates CDISC Therapeutic Area terminology matrices. Features confidence scoring threshold logic with explicit Human-in-the-Loop review routing.
Automated conversion engine transforming legacy unstructured folder trees into structured ICH/FDA Context of Use (CoU) codes, NCI Concept IDs, and XML snippets.
A governed Agentic AI orchestration demonstrator designed to explore human oversight, workflow controls, traceability, and risk management in regulated environments.
Accelerates complex corporate research by deploying an advanced semantic search stack over verified regulatory corpuses. Built using high-performance vector stores and Together AI infrastructure, providing rapid response times backed by precise source document citations.
A data-driven semantic command panel tracking FDA recalls, emergency approvals, and manufacturing alerts into an exportable, highly filterable analytical table dashboard.
Orchestrated cross-functional lifecycle pipelines for complex labeling artwork modifications across manufacturing sites, brand groups, and worldwide regulatory segments.
Enforced thorough data integrity profiles and managed technical product dependencies inside the Veeva software suite.
Audited and cross-checked foundational compliance documentation to fulfill strict GxP operational goals and prevent structural printing errors.
Pharma Labeling Business Analyst / Service Delivery Consultant
Merck Pharmaceuticals • North Wales, PA
Led global business analysis operations optimizing end-to-end tracking systems for critical corporate drug labels.
Configured complex system testing specifications utilizing HP ALM for validation, tracking, and JIRA application bug remediations.
Provided specialized application workflow support for headquarters and global country managers during large-scale portfolio rollouts.
Solution Architect – Development Informatics
Novartis Pharmaceuticals • East Hanover, NJ
Supervised systemic engineering, security topologies, and hardware layout architectures across global research groups.
Integrated enterprise cloud resources and early-stage infrastructure layers securely into existing regulatory computing environments.
Drafted and executed system validation tests to meet standard compliance parameters, monitoring capacity metrics and coordinating emergency system recoveries.
Start With One Real Regulatory Affairs AI Use Case
We can begin with a focused conversation about what the AI is doing, where it operates, who relies on its output, what could go wrong, and what evidence may be needed to demonstrate appropriate governance.
