Overview

This wiki is a thinking space.

It explores how regulated, industrial, and automated systems should be designed when trust, traceability, and explainability matter as much as performance or scale.

What this wiki is about

This wiki looks at questions such as:

  • Why do systems with "all the data" still fail audits?
  • Why does AI remain stuck in pilots despite technical maturity?
  • Why do dashboards increase visibility but not trust?
  • Why does compliance break long before regulation is involved?

The answers are rarely technical. They are architectural.

Reading Paths

If you are new here, these guided paths may help.

Path 1

Understanding Regulated Systems

For readers who sense that compliance and GMP are often discussed incorrectly, but cannot quite articulate why.

  1. GMP is shaped by time →
  2. Audit is not logging →
  3. GMP is not binary – it scales with proximity →
Outcome: Reframe regulation as a question of time, context, and architectural responsibility — not paperwork.
Path 2

Designing Trustworthy Architectures

For architects, lead engineers, and system designers working across IT, OT, and data layers.

  1. Decision-centric architecture →
  2. Why context must exist at decision time →
  3. Intervals are not abstractions – they are commitments →
  4. We can't automate trust →
Outcome: Understand where systems silently lose explainability and how architectural choices determine long-term trust.
Path 3

AI in Regulated Environments

For readers involved in AI, data platforms, innovation, or digital transformation.

  1. Manufacturing is not behind in AI – it is behind in trust →
  2. AI is not the solution when the process is not understood →
  3. Why data pipelines decide whether regulated AI will succeed →
  4. Why AI does not break GMP →
Outcome: See why AI success depends less on models and more on context, pipelines, and accountability.

Browse by Category

Foundations

Core principles behind trustworthy systems

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Distinctions

Concepts that are often confused

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Models

Architectural ways of structuring complexity

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Implications

What this means for AI, IIoT, and automation

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A note on scope

The ideas in this wiki intentionally stop before implementation. Not because implementation is unimportant, but because architecture must be understood before solutions make sense.

Author

This wiki is maintained by Florian Przybylak, working on the architecture of regulated industrial systems, data pipelines, and trustworthy automation.

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