Enterprise AI, data, and workflow delivery
AI is an amplifier of your processes: better process leads to much better and faster AI ROI.
But you don't have the time to:
And where are you going to get the people you need to do it all?
You don't have to feel like drowning in an ocean of opportunity.
From strategy down to ML engineering: I get you to your destination faster and avoid unnecessary detours.
< 48 hr response time
Where initiatives stall
AI access rarely fails on tooling alone. Value usually stalls at the operating layer: expectations, workflow design, leadership distance from the work, weak shared context, rising cleanup load, and cost pressure before the system is ready to convert model capacity into trusted delivery.
Teams get access to models and copilots, but no shared standard for what useful AI work should actually produce.
New model capacity gets buried under old approvals, handoffs, reporting loops, and delivery structure.
AI direction weakens when decision-makers are not close enough to the tools to understand real capability, limits, and tradeoffs.
People and agents cannot do trusted work when the systems, documents, and decisions that matter are only partially accessible.
Teams generate more documents, summaries, and drafts, but too much of it adds cleanup, ambiguity, and review load instead of progress.
AI spend gets questioned before expectations, process design, context access, and quality controls are fixed.
How I work
I do best when the team already knows the work matters, but tool access, process design, and delivery expectations have drifted apart. The job is to reduce ambiguity fast enough that execution becomes possible again.
My background is in environments where ambitious programs only count if they hold up in day-to-day operations. Robotics deployments, evidence-heavy work, and enterprise AI initiatives all teach the same lesson: if ownership is vague, context is thin, or the process breaks under pressure, the work stalls. So I focus on making initiatives clear enough to run, govern, and deliver.
Doing the wrong thing the right way helps nobody. Map the people, information, and decision pressure and prioritise, trading off effort and impact.
Success really depends on trust, and often that means the senior expert that has the experience to judge what works and what doesn't must be involved from the start. The delivery team should be cross-functional. Cut scope enough to be able to iterate fast and measurably towards the first milestone with clear constraints.
Implement with the people doing the work, validate outputs against real usage, and leave behind operating knowledge and quality standards for others to adopt.
This naturally leads to a growing shared context, which brings with it security and compliance workloads.
Track Record
Today the focus is enterprise AI delivery. Selected rows below were concurrent advisory or founder-led work, while earlier roles in robotics, telemetry, product architecture, and verifiable digital systems built the operating discipline behind it.
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Solutions Architect & CPTO, Co-Founder
Led solution architecture and delivery for enterprise AI programs in manufacturing and financial services, working directly with senior leadership to define realistic roadmaps and owning engineering execution with my own team, mainly across Microsoft Cloud environments. Strongly invested in the Sovereign Cloud Stack (SCS)
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Technology Consultant · Contract
Worked with the CTO to shape the distributed computer-vision architecture and key parts of the data ecosystem. I assembled two consortia involving six regional partners for EIC grants.
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Product & UX Consultant · Freelance
Led research and design sprint workshops that helped teams turn hard product questions into clear priorities, decision criteria, and a sharper product direction.
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Applied AI Instructor · Volunteer
Guided undergraduate engineers in deploying production-grade LLM architectures, multimodal generation workflows, and modern NLP and Reinforcement Learning.
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Research Engineer, Project Bilbo
Data engineering and Machine Learning work to reduce dimensionality of the control problem. Built low-latency data streams for fuel-cell telemetry.
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Co-Founder, Software Engineering Lead
Led my first engineering team, delivered smart-contract implementation work for external clients and consulted B2G on digital sovereignty.
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Deployment Engineer
Led field deployment of complete autonomous robotic warehouses across European fulfillment centers. I have never seen scale like this since.
Prior experience
Earlier foundations included industrial robotics, SAP BW reporting, and academic lab work before the later delivery roles shown above.
Walltec TechnologyMetric TechnologyUniversity of Tokyo · Intelligent Systems and Informatics Lab
Best fit
I work best with clients that already have budget, compliance constraints, stakeholders, and urgency already named. Rollouts can be tricky, so I leverage my experience and your specific expertise to highlight the icebergs ahead of time.
From there, whether it is hands-on engineering or technical project/program management, I have your back.
Decision point
Don't overthink it: let's get you to crystal clarity on your automation or AI problems, whether its capacity, direction or fundamental "What do we do?".