Digital Compliance Infrastructure for Pharma

Pharmaceutical development does not fail at the point where problems become visible. It fails upstream, through unnoticed drift between the artifacts that define a programme — a mechanism that costs sponsors hundreds of millions and surfaces as Day 120 questions, Complete Response Letters, and inspection findings. This paper presents EOCME-CP, a deterministic meaning-analysis infrastructure that detects drift where it is still a semantic divergence between documents, before it becomes a late-stage failure. The infrastructure combines a fixed deterministic core (cosine similarity, field curvature, coherence Q, cross-pair analysis) with an interchangeable reporting layer. At T=0.0 with fixed parameters, the same input produces the same output across runs, operators, and sites — generating compliance with ALCOA+, the EMA-FDA Guiding Principles of Good AI Practice in Drug Development (January 2026), and the EU AI Act by construction rather than as a verification layer bolted on afterwards. Grounded in forty years of observational practice and formalized through Context Psychology and The Holographic Meaning Field, the method is available under license for deployment at project, departmental, corporate, or federated global scale.