AI systems advisory
Turn a vague mandate to ‘use AI’ into a bounded workflow with explicit context, tools, verification, cost, and ownership.
- → Workflow and risk mapping
- → Context and tool architecture
- → Pilot design and evaluation
Advisory · architecture · delivery
Bring structure to
the hard problem.
I work best where product, engineering, emerging technology, and organizational judgment meet; especially when the real problem has not yet become a clean ticket.
Engagement principle: begin with the smallest useful commitment, make the evidence visible, and expand only when the work earns it.
Ways to work together
Focused help, built around an outcome.
These are shapes, not rigid packages. Scope should follow the consequence and uncertainty of the problem.
Architecture and rescue
Understand a complex or struggling product, identify the decisions that matter, and create a path teams can actually execute.
- → System assessment
- → Architecture and modernization plan
- → Performance and delivery recovery
Fractional technical leadership
Add experienced product-engineering judgment during an important build, transition, or capability gap without pretending a permanent hire appears overnight.
- → Technical direction
- → Team and delivery systems
- → Stakeholder translation
A responsible start
Discovery before dependency.
The first engagement should reduce uncertainty even if it reveals that I am not the right long-term solution.
01 · ORIENTOne focused conversation
Clarify the outcome, current state, consequence, and people involved.
02 · FRAMEA written problem model
Define constraints, unknowns, decision owners, and the smallest evidence-producing step.
03 · PROVEA bounded engagement
Assessment, prototype, architecture plan, or delivery intervention with an explicit finish line.
04 · DECIDEContinue, hand off, or stop cleanly
The work should create value and clarity without manufacturing dependence.
Fit
The conditions for good work.
Technical range matters. The working environment matters more.
Usually a strong fit
An important product or workflow; real access to decision-makers and evidence; willingness to name constraints; respect for engineering and user reality; desire to leave the team stronger.
Usually not a fit
A request for unreviewed AI-generated volume; authority without accountability; hidden stakeholders; a predetermined solution disguised as discovery; or urgency that requires concealing risk.
Practical questions
Before we begin.
A useful first conversation should answer these quickly.
Do you only work with Unity or XR?
No. Those are deep parts of the track record, but the work increasingly centers on systems architecture, AI workflows, product judgment, technical leadership, and tools across stacks.
Can you join an existing team?
Yes; the role, decision authority, and exit or handoff condition need to be clear. The goal is contribution, not territorial ownership.
Will you build the product?
When hands-on implementation is the right leverage. Some engagements should end with a tested architecture and team plan; others benefit from direct delivery.
How do you price work?
After the problem is framed. A bounded assessment can be fixed around a deliverable; ongoing leadership or delivery usually needs a clear cadence and capacity model.
Start small
Describe the decision you cannot make yet.
A short, honest problem statement is a better beginning than a polished request for proposal.