I bring clarity before AI initiatives lock in vendors, platforms, and investments.
20+ years of practice — strategy and architecture in one mind.
Get your situation assessedWith the Python Software Association Germany, I published a survey of software professionals; Handelsblatt covered it in July 2026. The finding: accountability for AI is often unassigned and defaults to the individual developer. I have distilled what follows for decision-makers: the path from diagnostic to operating model, plus the full analysis as a decision paper, 16 pages, PDF. I’ve also published a detailed blog post covering the survey findings.
What this means for your organisationAI 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.
Guardrails, boundaries, and operational governance are often missing. 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.
I move credibly between leadership, business functions, and engineering because I do not just present strategy — I assess it technically and translate it all the way into architecture decisions.
Models are obsolete in months, vendors consolidate in quarters. Sovereignty is not a question of model choice, but of architecture: data-flow boundaries, vendor decoupling, auditability, exit paths. A resilient architecture lasts for years — and decides whether your company stays in command or follows a platform.
No software, no licences, no commissions. My recommendations follow your situation, not my revenue.
A survey of AI-using developers shows the real risk is not replacement but control debt: automation outpaces oversight, accountability and governance.
Read more →Three architectural shifts separate 2026 programmes that ship from those on 2024 logic: model to harness, test to architecture, budget to sovereignty lever.
Read more →Six axes where it shows what actually runs sovereign: data, inference, post-training, evaluation, compliance, operations.
Read more →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.
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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. When I recommend a technology, I know it from practice: the Python ecosystem, vector databases, workflow orchestration. I bring together strategic perspective and technical substance — from privacy-compliant architecture and IT security to resilient AI systems.
When AI generates code or acts autonomously, quality assurance becomes the critical function. Hands-on depth makes reviews substantive, not nominal.
Practice is based in Heidelberg, Germany. Engagements across DACH and Europe — on-site with clients or remote, depending on phase and confidentiality.
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.
