The foundational principles behind trustworthy industrial, GMP and AI-ready systems.
This section lays the groundwork for everything that follows.
The articles in Foundations focus on the underlying principles that make industrial systems trustworthy, explainable, and scalable — long before specific technologies or tools are discussed.
Here, GMP is treated not as a checklist, but as a way of thinking about time, responsibility, and system behavior. Trust is not assumed. It is designed.
You will find perspectives on:
- why GMP is fundamentally shaped by time and proximity
- why trust cannot be automated after the fact
- what regulated industries like Pharma teach about digital systems
- what it actually means for IIoT systems to be AI-ready
These articles answer why certain architectural constraints exist — and why ignoring them leads to fragile automation and stalled AI initiatives.
If you are new to this wiki, start here.
Why auditability in regulated systems is fundamentally a time-based architecture problem, not a documentation issue.
6 min read
Why Pharma's emphasis on traceability, ownership and explainability is not a burden but a blueprint for scalable automation and AI.
6 min read
Why trust cannot be added after automation and must be embedded in industrial communication and data pipelines.
7 min read
Why AI readiness in IIoT is determined by validation, context handling and traceable data foundations, not by model connectivity.
6 min read
A practical way to approach GMP in IIoT by tracing one data path and making trust explicit from the start.
6 min read
Why IIoT systems in regulated environments need traceability, validation and lifecycle control beyond basic connectivity.
7 min read