AI makes building faster. It does not make understanding optional.
Most AI projects don't fail because the technology is wrong. They fail because nobody understood the work, the people, or the constraints before choosing the technology. 80% of AI projects fail to deliver business value. The difference is almost always in how the work was understood before the first model was chosen.
How I work
I write code, review PRs, design data pipelines, and make the architectural decisions your team will live with after I leave. But I start by spending time with the people who do the work. I map the workflows, identify where things break down, and figure out where AI creates genuine value and where it adds cost without return. Not every process benefits from AI. The most expensive mistake is building the wrong thing faster. Less time in meetings, more time shipping solutions that have real impact.
This way of working was shaped by formative years at frog and Intuity Media Lab, where understanding the problem came before the technology decision, and refined through 15 years of enterprise delivery at Siemens, Deutsche Telekom, and Mercedes-Benz.
What I Don't Do
- Slide decks and steering committees
- Staff augmentation or body-shopping
- Open-ended retainers with no exit plan
- Technology recommendations without implementation
- Engagements where I can't write code
- AI workshops that end with a report nobody acts on
Start here: the AI Opportunity Diagnostic
Two days. Three deliverables. A clear picture of where AI helps and where it doesn't.
I talk to your operational staff, map the workflows, assess the data landscape, and identify which problems AI can solve and which it can't. The output is specific and actionable, not a strategy deck.
Prioritized AI opportunities
Ranked by business impact, technical feasibility, and data readiness. Each opportunity includes what it would take to validate it.
Workflow friction report
Where manual effort, handoffs, and decision bottlenecks slow your operation. Includes quick wins that don't require AI at all.
Honest next-step recommendation
What to pursue, what to defer, and whether I'm the right person for the next phase. Sometimes the answer is no.
The diagnostic might conclude that AI is not the right answer for your situation. That has happened. If it does, you keep the deliverables and we shake hands. A diagnostic that always concludes "yes, you need me" is not a diagnostic. It is a sales pitch.
Starting at EUR 9,500. Fixed price, no hourly billing, no surprises.
Who this is for
- Companies with real operational complexity, whether that is a 10-person team scaling to 200 or an established organization with 1,000 employees
- Leadership teams under pressure to "do something with AI" but unclear on what
- Organizations that have tried an AI pilot that stalled, or spent money on a strategy deck that went nowhere
- CTOs and product leaders who want senior technical judgment, not a team of juniors
This is not for companies that want a chatbot demo in two weeks. It is for companies that want to know where AI will still be creating value in two years.