Reference Blueprint for Modern CMC Systems
A modular architecture that connects CMC data, scientific compute, and compliance-ready workflows. Built for scalable digital twins and rapid decision support.
Source Systems
LIMS, MES, ERP, historians, CDMO data feeds, and lab notebooks.
- • Batch records
- • Process parameters
- • Analytical results
- • Supply signals
Data Foundation
Normalized CMC data model with audit trails, lineage, and governed access.
- • PostgreSQL + pgvector
- • Drizzle ORM schema
- • Audit triggers
- • Change justification
Compute & Simulation
Hybrid Python + TypeScript analytics to power forecasting and optimization.
- • Monte Carlo risk
- • DoE modeling
- • Optimization solvers
- • Scenario planning
Experience Layer
Interactive dashboards, digital twins, and decision support workflows.
- • Dashboard KPIs
- • P&ID canvas
- • Document automation
- • Regulatory copilot
Compliance & Control
Part 11 aligned governance baked into workflows and data access.
- • E-signatures
- • Validation packs
- • Role-based access
- • Session controls
Continuous Intelligence
AI agents and analytics that learn from process outcomes.
- • RAG pipelines
- • Deviation insights
- • Predictive alerts
- • Knowledge graphs
Delivery roadmap
Phased implementation to deliver fast wins while protecting compliance.
Discovery & Blueprint
Architecture, data inventory, and value prioritization
Foundation Build
Data model, authentication, audit trail, and core demos
Digital Twin Modules
Supply forecasting, DoE analytics, P&ID visualizations
Automation & AI
Copilots, document automation, and continuous optimization
Why this architecture works
A hybrid approach that keeps scientific compute close to the engineers who need it.
System-of-record integrity
Immutable audit trail and data lineage to satisfy regulatory expectations.
Composable compute
Add new models without replatforming your UI or compliance layer.
Portfolio-ready demos
Public-facing experiences backed by synthetic data and secure boundaries.