I help companies build durable foundations for AI: through clear prioritisation, sound architecture decisions, and technological sovereignty.
Technological sovereignty does not come from software, but from resilient architecture and your organisation’s own decision-making capability. I build both with your decision-makers and your tech team.
Schedule a conversationAI is on the agenda, but pilot projects are not turning into a scalable operating model. What is missing are priorities, architecture, and a credible target picture.
Business units and analytics teams are experimenting in parallel with different tools, platforms, and vendors. That increases complexity, cost, and governance risk.
The market produces new promises every day. The real question is not what is new, but what is economically, technically, and regulatorily viable in your context.
The potential is high, but guardrails, clear boundaries, and operational governance are missing. When AI generates code or acts autonomously, quality assurance becomes the critical function: architecture, security, maintainability, and compliance must be reviewed substantively — not nominally. Without robust safeguards, AI outputs create risk rather than value.
The direction is clear, but business, technology, and leadership are not operating from the same logic. Decisions stall, and execution slows down.
AI does not create impact by digitising existing routines, but by redesigning how decisions are made and how work gets done.
If you recognise your organisation in any of these points, now is the right time for a sound decision.
I do not chase short-term effects. I work toward the technological and organisational foundations on which resilient AI capability is built: with clear standards, viable architecture, and the discipline to leave out what does not matter. Shaped by more than 20 years of practice — not by trend cycles.
I move credibly between C-level, business functions, and engineering because I do not just present strategy — I assess it technically and translate it all the way into architecture decisions. That reduces friction, accelerates decisions, and prevents costly misunderstandings.
I do not sell software, licences, or implementation. My recommendations are guided solely by your situation and your interests. Open source is not an article of faith for me, but a structural advantage: auditable code, less dependency, stronger auditability, and greater control — especially in regulated environments. Models are obsolete in months, vendors consolidate in quarters. Sovereignty is therefore not a question of model choice, but of architecture: data-flow boundaries, layering, vendor decoupling, and exit paths. A resilient architecture lasts for years — and decides whether a company stays in command or follows a platform.
Strategic lead and architecture decisions — implemented by the internal team and partners
Legacy C# and SAS silos separated research, portfolio management, and engineering. Long release cycles, limited ESG capability, and a lack of traceability put speed, control, and compliance at risk.
Migration of the organisation to Python and open source. Deployment cycles dropped from three months to three weeks; 44 employees were upskilled across four cohorts. The result was a stack that supports traceability, auditability, and regulatory requirements in a financial-services environment.
Learn more →Strategic lead, employee survey & AI prototype — implemented by an external team
More than 30 years of project data, 70% manual processing, and no reliable data foundation. Publicly funded information was trapped in silos; AI potential could not be put to operational use.
Development of a data strategy with a 120-page implementation roadmap. An AI-supported forecasting model for workforce planning achieved 90% accuracy; the planning cycle shifted from annual to continuous.
Learn more →Strategic concept, NLP architecture, and prototype development — further expanded by the internal team
More than 10,000 research documents spanning three decades: unstructured, confidential, and nearly impossible to search. Cloud and API usage were ruled out.
Built an NLP-based knowledge explorer that turned static document storage into search results in seconds. Topics, related contexts, and source documents became directly accessible — fully on-premises, with no external APIs and no LLMs. The internal team subsequently expanded the solution further.
Learn more →Three pieces from what's currently being decided in active mandates — for context, not as required reading.
The most important architectural decision for LLM systems in 2026 isn't the model — it's the evaluation. Three shifts that separate production AI from stalled pilots.
Read more →Six axes where it shows what actually runs sovereign: data, inference, post-training, evaluation, compliance, operations.
Read more →Insights from a closed-door session under Chatham House Rule: the hardest blockers sit ahead of the technology — missing shared language, missing standard, missing mandate.
Read more →
Open source did not become relevant to me because of ideology, but because of responsibility. More than 20 years ago, I saw firsthand what technological dependencies cost companies. As COO of a transatlantic music company that I helped build, I learned that resilient structures emerge where companies retain control over their own ability to execute.
Since then, I have worked at the intersection of open source, AI, and business transformation — initially as a developer, then as an architect, and today as a strategist to companies in regulated industries. I bring together strategic perspective and technical substance: from regulatory requirements through privacy-compliant architecture and IT security to resilient AI systems. When AI generates code or acts autonomously, quality assurance becomes the critical function — and that is precisely where genuine hands-on depth makes reviews substantive rather than nominal.
When I recommend a technology, I know it from practice. More than 50 technologies across two decades — including the Python ecosystem, vector databases, and workflow orchestration. That allows me to speak with boards about risk and priorities, and with tech teams about the architecture that holds up in execution.
Through my work in the European AI and open-source community, I stay close to the developments and debates that matter to companies. My clients benefit from that directly.
The initial conversation lasts around 30 minutes and is meant to provide a clear assessment of your situation. I work with companies that treat AI as a strategic decision — one that affects architecture, organisation, and value creation. If a concrete strategic choice is ahead of you, send me a brief note outlining your industry, starting point, and timeframe. I will reply personally.
What I discuss at international conferences flows directly into my client work: not as second-hand trend commentary, but as direct insight into the debates, technologies, and fault lines that matter to companies in practice.
