Your AI is making decisions. Do you know which ones it is not allowed to make?
A structured diagnostic for organisations deploying AI in critical infrastructure — energy, transport, ports, and government — where a wrong decision has physical, financial, or regulatory consequences.
What every engagement produces: evidence, not opinion.
AI that optimises one thing can quietly break everything else.
Most AI pilots that fail in production don't fail because the model was wrong. They fail because no one defined the rules that sit above the AI — what happens when two priorities collide. The fix is not a better prompt. It's architecture.
It hit the target and caused the incident.
A system treats a safety limit as just another variable to trade against speed or cost. Safety should be a gate, not a weight — decided before the crisis, not during it.
Brilliant decisions, ruinous economics.
An expensive model running continuously on stable data isn't a success — it's an architecture failure. When the AI acts is a decision in its own right, governing both cost and attack surface.
Smart enough to see it, not allowed to fix it.
An AI that spots the conflict but has no authority to resolve it creates the illusion of governance while delivering paralysis. The tie-breaker has to be written in advance.
The Decision Governance Audit
A time-boxed, structured diagnostic that answers one question with evidence: where does your AI deployment have unresolved decision boundaries — and what must be written down before the next pressure event arrives?
Built for leaders accountable for critical decisions.
Energy & Grid
Operators balancing safety, demand response, and cost at machine speed.
Transport & Mobility
Depots, fleets, and rail where load and readiness conflict in real time.
Ports & Logistics
High-throughput operations with hard physical and scheduling limits.
Government
Ministries and public bodies procuring AI for high-stakes infrastructure.
For the executive, technical, and risk owners who will answer for the system once it is live.
Four deliverables. Each one defensible in a boardroom and a regulator's office.
Governance Assessment
An objective evaluation of your AI deployment's decision environment, resolving to a clear governance position and the evidence to defend it.
Governor Constitution
The explicit rule set your AI must operate under — written as a document a board member or regulator can read and sign off on.
Risk Exposure Map
A clear picture of where your current deployment is exposed — the failure modes that survive a model upgrade or a better prompt.
Governance Roadmap
A prioritised path from diagnosis to a production-ready governance architecture, scoped to your risk profile and resources.
Governance is an architecture decision, not a policy document.
The audit applies a structured methodology developed specifically for high-stakes industrial AI environments — where decisions happen at machine speed, safety limits are non-negotiable, and human verification is not always possible.
The assessment produces a scored position — not a set of recommendations that shift with the next conversation. You leave with something you can show a regulator, a board, or an insurer.
The methodology was developed from operational engineering experience in energy and mobility systems — not from academic AI ethics. It addresses the decisions that happen at 3am when no one is watching.
The Governor Constitution produced by every audit is structured to satisfy the documentation requirements emerging under the EU AI Act — and to hold up if an incident triggers an inquiry.
The audit is the entry point. What you build after it is an architecture your organisation can stand behind.
A clear sequence from question to roadmap.
Scoping call
We define the deployment, the decision environment, and who is accountable when it runs. No pre-reading required.
Structured assessment
We work through your live use cases in structured sessions — mapping conflicts, decision velocity, and the boundaries that are not yet written down.
Governance document
The Governor Constitution is drafted for your highest-risk deployment. Every boundary explicit. Every escalation path defined.
Executive briefing
Findings and roadmap delivered to your leadership team. Board-ready. Questions answered directly.
- Dipl.-Ing., University of Stuttgart/Germany
- Creator of a structured AI governance methodology for industrial environments
- Background in complex technical project management and solution management across energy and mobility systems
- Founder, SFK AI Solutions · Lead Instructor, GenAI.academy
Said Fassih Karimzad is an AI governance architect based in Munich. He works with enterprise and government organisations deploying AI in high-stakes operational environments — energy, transport, ports, and critical infrastructure — where the consequences of a wrong decision are physical, financial, or regulatory.
His approach applies the same engineering rigour used to commission physical infrastructure to the question of whether an AI system can be trusted with consequential decisions. Through SFK AI Solutions he runs Decision Governance Audits for enterprise and government clients. Through GenAI.academy he teaches the methodology to technical and executive audiences.
The SAIA Executive Masterclass
Learn to govern AI in critical infrastructure — and to specify it as a buyer.
A live, two-session masterclass through GenAI.academy for the leaders and architects deploying AI where the stakes are physical. Builders learn to implement; commissioners learn what to demand from a vendor.
Inaugural cohort · August 2026
One conversation to find out where you stand.
Send a one-line description of your AI deployment. The first conversation is a direct discussion of your decision environment and its risk profile — no proposal, no commitment.