Cloud and AI architecture
I help teams structure Azure and AI solutions so they are understandable, secure, and realistic to operate. The interesting work usually starts after the demo.
I work on cloud, data, and AI systems, mostly where ideas need to become something teams can actually run.
I like working on the messy middle: when an idea sounds promising, but the team still has to figure out the data, architecture, security, cost, user workflow, and actual delivery path.
I help teams structure Azure and AI solutions so they are understandable, secure, and realistic to operate. The interesting work usually starts after the demo.
I have worked across many ML and data science use cases. That breadth helps me connect architecture decisions with the actual data, models, and user workflows behind them.
I like building small, concrete products and experiments. They are the fastest way to find out which assumptions are solid and which ones only worked on slides.
Stopped
An experiment around scheduled agent runs, heartbeats, monitoring, and more complex autonomous tasks.
Active
A blog I started while working as a machine learning consultant, with practical articles about ML, data science, and analytics use cases.
Stopped
A real-estate data and AI pipeline for exploring new-building projects, subprojects, and project imagery.
Exploring
A growing collection of practical AI use cases, demos, and implementation patterns.
More recently I have been exploring algorithmic portfolio management: how to structure signals, evaluate strategies, and keep the system explainable instead of turning it into a black box.
I have also been looking at agentic legal use cases on Swiss legal data, especially how retrieval, reasoning, and citations could support legal research workflows.
I rebuilt this site around projects I worked on over the past years, plus current experiments and writing.
My writing and experiments increasingly focus on the practical side of agentic systems: state, boundaries, observability, and usefulness.
My talks and workshops are centered on practical AI adoption, agentic delivery, and architecture trade-offs.