A design framework for deciding where AI must NOT be used.
🚧 Design framework stage
（Concept & structure finalized）
- ❌ Not a deployable control product
- ❌ Not a certification or compliance scheme
This package is a design- and governance-level framework
for making defensible, explainable decisions about
AI / LLM usage in control systems.
| Language | GitHub Pages 🌐 | GitHub 💻 |
|---|---|---|
| 🇺🇸 English |  |
 |
This package does not deliver algorithms or code.
It delivers engineering judgments:
- 🧱 Architectural boundaries
⚠️ Safety constraints- 🔁 Recovery and fallback logic
- 👤 Responsibility and decision ownership
It is intended for:
- 🧠 Control engineers
- 🧭 Technical leaders
- 🏛️ Decision-makers responsible for system safety & accountability
AI and LLMs are increasingly pushed into control systems.
In many projects, the real question is not:
How can we use AI?
but rather:
❗ Where must AI be limited, isolated, or explicitly stopped?
This package exists to answer that question:
- 📐 Clearly
- 🧱 Structurally
- ⚖️ Defensibly
- 🚫 AI is not trusted by default
- 🏗️ Safety comes from architecture, not intelligence
- ⏱️ Real-time control must remain deterministic
- 🧯 Failure handling must be designed — not improvised
The focus is on:
Risk judgment · Safety boundaries · Recovery logic
—not performance optimization.
The packages are applied in the following order
and form a single, coherent safety story:
| Step | Package | Key Question |
|---|---|---|
| ① | Risk Review | Should AI be allowed at all? |
| ② | Safety Envelope | If allowed, where must AI be strictly constrained? |
| ③ | Recovery Control | When things go wrong, how do we return safely — and who decides? |
This package defines a single end-to-end safety narrative for AI-assisted control systems.
It is not an operational sequence
and not a runtime behavior specification.
It describes how safety responsibility flows by design.
-
Before deployment
→ Decide whether AI / LLM is allowed at all
(AI Control Risk Review) -
During normal operation
→ AI is constrained within explicitly defined boundaries
(Safety Envelope) -
When boundaries are violated
→ Deterministic fallback is enforced immediately
(FSM-governed Safe Mode) -
After failure or degradation
→ Controlled and accountable recovery is executed
(Recovery Control)
At no point does AI make final safety decisions.
This framework ensures that
safety, recovery, and responsibility remain human-designed,
deterministic, and explainable — end to end.
Architectural Go / Conditional Go / No-Go judgment
for AI / LLM-based control concepts.
🔍 Focus:
- AI placement strictly outside real-time control loops
- AI / LLM limited to:
- Planning
- Diagnosis
- Parameter proposal
- Explicit stop conditions
- Clear fallback paths
- Unambiguous failure ownership
🔗 Open:
👉 AI Control Risk Review
Explicit definition and enforcement of
operational boundaries AI must never violate.
🧱 Safety Envelope defines:
- ❌ What must never be violated
- ⏱️ How violations are detected before they occur
- 🧭 How constraints are enforced independently of AI behavior
🔍 Core elements:
- Allowed operational envelopes
- Pre-violation detection
- FSM-based supervisory enforcement
🔗 Open:
👉 Safety Envelope Design
Deterministic recovery design after
disturbances, degradation, or abnormal behavior.
🔁 Recovery Control governs:
- What happens after a violation
- How the system enters safe fallback
- How (and whether) controlled re-entry is allowed
🔍 Core elements:
- Recovery triggers and modes
- Safe fallback structures
- Controlled re-entry with accountability
🔗 Open:
👉 Recovery Control Design
This package is offered as a limited-scope design review / consulting service,
focused on architecture, responsibility, and safety logic.
| Service | Fee (JPY) |
|---|---|
| AI Control Risk Review | 50,000 – 100,000 |
| Safety Envelope Design | 100,000 – 300,000 |
| Recovery Control Design | 150,000 – 400,000 |
Fees depend on:
- System complexity
- Documentation quality
- Required depth of review
If you are unsure where to begin:
👉 AI Control Risk Review is recommended as the first step.
🔗
Start with AI Control Risk Review
| 📌 Item | Details |
|---|---|
| Name | Shinichi Samizo |
| Expertise | Semiconductor devices (logic, memory, high-voltage mixed-signal) Thin-film piezo actuators for inkjet systems Printhead productization, BOM management, ISO training |
| 📧 shinichi.samizo2@gmail.com | |
| GitHub |  |
This repository uses a hybrid (dual) license structure.
| 📌 Item | License | Scope |
|---|---|---|
| Source Code (utilities, examples) | MIT License | Code-level reuse permitted |
| Design Text & Framework Description | CC BY 4.0 or CC BY-SA 4.0 | Attribution required; framework reuse requires agreement |
| Figures, Diagrams, Architecture Drawings | CC BY-NC 4.0 | Non-commercial use only |
| Service Model / Review Criteria | Proprietary | Consulting use only |
and not a certification scheme.
Design questions, clarification, and architectural discussion are welcome:
👉
Primary topics:
- Go / Conditional Go / No-Go judgments
- Safety boundary interpretation
- Architecture-level responsibility discussions