RegOpsPro™RegOps Methodology
RegOpsPro converts GDP responsibilities into practical operating models, structured tools, governance outputs, QMS-transfer summaries and GDP-grounded AI drafting support.
1. Operating model first
The platform starts with role and system design before tools. RP and RP-I workspaces define accountability, sequence, governance and evidence expectations.
2. Evidence-led execution
Tools are designed to convert events, risks, import controls, inspection preparation and governance activity into evidence-led outputs.
3. QMS-transfer orientation
Outputs are drafts and summaries for review. They are intended to support transfer into QMS records, governance packs, CAPA records or inspection-preparation files.
4. Inspection-aware language
Wording is designed to help users explain control, evidence, uncertainty, ownership and escalation clearly during review or inspection preparation.
Standard output model
- clear factual context;
- risk or readiness interpretation;
- evidence and missing-evidence view;
- actions, owner and due-date logic;
- QMS or governance transfer summary;
- practical boundary wording.
GDP-grounded AI model
Where AI is used, it is treated as a bounded drafting layer over the same methodology. The AI is prompted using controlled GDP themes and tool-specific control cards, and the output is designed to show:
- GDP control themes applied;
- evidence gaps or uncertainty;
- QMS-transfer caution;
- practical judgement and approval boundaries.
This keeps AI visible, challengeable and reviewable rather than hidden as generic drafting.