A design framework for deciding where AI must NOT be used.
π§ Design framework stage
οΌConcept & structure finalizedοΌ
This package is a design- and governance-level framework
for making defensible, explainable decisions about
AI / LLM usage in control systems.
| Language | GitHub Pages π | GitHub π» |
|---|---|---|
| πΊπΈ English |  |
 |
This package does not deliver algorithms or code.
It delivers engineering judgments:
It is intended for:
AI and LLMs are increasingly pushed into control systems.
In many projects, the real question is not:
How can we use AI?
but rather:
β Where must AI be limited, isolated, or explicitly stopped?
This package exists to answer that question:
The focus is on:
Risk judgment Β· Safety boundaries Β· Recovery logic
βnot performance optimization.
The packages are applied in the following order
and form a single, coherent safety story:
| Step | Package | Key Question |
|---|---|---|
| β | Risk Review | Should AI be allowed at all? |
| β‘ | Safety Envelope | If allowed, where must AI be strictly constrained? |
| β’ | Recovery Control | When things go wrong, how do we return safely β 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
β Decide whether AI / LLM is allowed at all
(AI Control Risk Review)
During normal operation
β AI is constrained within explicitly defined boundaries
(Safety Envelope)
When boundaries are violated
β Deterministic fallback is enforced immediately
(FSM-governed Safe Mode)
After failure or degradation
β 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 β end to end.
Architectural Go / Conditional Go / No-Go judgment
for AI / LLM-based control concepts.
π Focus:
π Open:
π AI Control Risk Review
Explicit definition and enforcement of
operational boundaries AI must never violate.
𧱠Safety Envelope defines:
π Core elements:
π Open:
π Safety Envelope Design
Deterministic recovery design after
disturbances, degradation, or abnormal behavior.
π Recovery Control governs:
π Core elements:
π Open:
π 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 β 100,000 |
| Safety Envelope Design | 100,000 β 300,000 |
| Recovery Control Design | 150,000 β 400,000 |
Fees depend on:
If you are unsure where to begin:
π AI Control Risk Review is recommended as the first step.
π
Start with AI Control Risk Review
| π Item | Details |
|---|---|
| Name | Shinichi Samizo |
| Expertise | Semiconductor devices (logic, memory, high-voltage mixed-signal) Thin-film piezo actuators for inkjet systems PrecisionCore printhead productization, BOM management, ISO training |
| π§ shinichi.samizo2@gmail.com | |
| GitHub |  |
This repository uses a hybrid (dual) license structure.
| π 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 |
β οΈ This repository is not an open safety standard
and not a certification scheme.
Design questions, clarification, and architectural discussion are welcome:
π
Primary topics:
