Service
AI systems & automation
I help teams turn fuzzy AI ideas into useful systems: internal assistants, document workflows, qualification, business automation and guardrails before anything sensitive reaches production.
Based in Belgium, I work from scoping to prototype to integration with one simple rule: clear sources of truth, explicit human review on sensitive steps, and concrete proof instead of vague AI promises.
Operational proof
The workflow board that makes an AI lane readable before it grows
If the use case, risk or next step is still unclear, I start by adding structure. The goal is not to show abstract “AI capability”, but to make the operating lane readable enough that a team can trust the next decision.
Human review lane
- the risky output is identified before anyone asks too much from the model
- someone owns the go / reframe / stop verdict instead of leaving it implicit
- the next integration step is earned by evidence, not by demo enthusiasm
Stage 01
Scope one bounded use case
Choose the user, source of truth and exact decision the workflow should help with before adding more tooling.
Stage 02
Test on real examples
Run the lane on representative documents, requests or edge cases with traces visible enough for review.
Stage 03
Decide with evidence
Keep what already works, reframe what stays fragile and stop early if the workflow still creates more uncertainty than value.
When I step in
The situations where AI needs structure before scale
This service is useful when AI is moving faster than the framing, trust or decision-making around it.
- Promising but still vague.
You can see potential value, but nobody has clearly decided which use case to prioritize or how success should be judged.
- Prototype that is hard to trust.
A first version exists already, but the sources, limits and review steps are still too implicit to feel safe.
- Need a real verdict.
You need to decide go, reframe or stop quickly instead of keeping an impressive demo alive without an operational future.
First cycle
What you get quickly, and why it feels credible
The goal is not to stack deliverables. It is to produce a first cycle that is useful, readable and easy to decide on, with visible proof of method.
- Short scoping phase.
We choose one priority use case, the right sources, the risks and the exact step where a human should take control.
- Prototype on real cases.
I build a flow that can be tested on your actual examples: assistant, document triage, qualification or internal workflow with visible traces.
- Review & guardrails.
Verification, critic mode, escalation and action limits before anything sensitive happens automatically.
- Visible public proof already exists.
My OpenClaw setup, backed by Rusty Art, already shows a concrete working method without inventing glossy public case studies.
- Scope
- 1 priority use case
- Validation
- Real examples
- Control
- Explicit human review
- Decision
- Go / reframe / stop
What I am really selling here is not “more AI”. It is a clearer, more reliable and more accountable way to use AI in a real operating context.
FAQ
Frequently asked
- Do we need a very precise use case already?.
No. I can help turn an intuition or messy need into a prioritized and testable use case.
- Do our data sources already need to be clean?.
Not necessarily. We can start small, audit what exists and clean only the sources that matter for the first useful cycle.
- Do you only provide prompts?.
No. I can scope, prototype, integrate, document and harden the workflow with real supervision and accountability around it.
- What if the verdict is “stop”?.
That is still a good result. The point is to stop fragile work from consuming more budget and attention than it should.
Send the context, what already exists and what is still unclear. I’ll come back with a concrete first framing and the best next step.