Is this an AI consulting practice?
No.
This work focuses on process and decision design, not AI models, tooling, or architecture.
AI and automation are often the catalyst, but the engagement centers on clarifying decision boundaries, ownership, and failure controls—regardless of technology choice.
Do you build, tune, or deploy AI systems?
No.
I do not:
- Build models
- Tune prompts
- Deploy pipelines
- Access production systems
This work happens upstream of implementation and complements internal teams rather than replacing them.
How is this different from governance or compliance work?
Governance often documents what should exist.
Process assurance defines how decisions actually operate.
This is not a checklist or certification exercise. It is practical operating clarity that teams can execute against immediately.
Will this slow teams down?
No; when done correctly, it does the opposite.
Explicit boundaries and ownership reduce rework, escalation, and second-guessing. Teams move faster because they are no longer guessing where the lines are.
Who typically participates in an engagement?
Usually a small group representing:
- Executive or operational leadership
- Product, platform, or workflow owners
- Risk, compliance, or operations stakeholders
The goal is alignment, not broad attendance.
How long do engagements last?
Engagements are short and focused.
The objective is to produce:
- Clear automation boundaries
- Defined decision ownership
- Explicit escalation and recovery paths
This is not a long-term advisory retainer.
Do you need access to our systems or data?
No.
The work is based on:
- Process descriptions
- Decision flows
- Operating assumptions
No production access is required.
Is this suitable for organizations early in AI adoption?
Yes; often especially so.
This work is most valuable before automation decisions are locked into systems and contracts. It helps organizations avoid rework later.
Can this be done alongside internal teams?
Yes.
This work is designed to support and amplify internal data, engineering, operations, and compliance teams—not override them.
What is the tangible output?
Outputs typically include:
- Documented decision boundaries
- Named ownership and authority models
- Defined human oversight structures
- Clear failure and rollback conditions
These artifacts are designed to be usable, not theoretical.
What problem does this ultimately solve?
It reduces surprises.
Organizations gain:
- Faster scaling of automation
- Clear accountability when outcomes change
- Greater trust in automated decisions
- Fewer high-stakes escalations
What if we’re not sure this is the right fit?
That’s expected.
If your primary question is:
“What tool or model should we use?”
This work is likely premature.
If your question is:
“Are our decisions clear enough to automate responsibly?”
This work is designed to help