AI agents are remaking software in every industry: users describe the outcome they want, and the software gets them there. Spatial data is next.
I help spatial software vendors bring AI agents into their products - hands-on, with your team, from first prototype to production.
Your users have started asking. The hard part isn't adding AI - it's making it useful:
Find where an agent creates real value in your product and its workflows.
Make it work with GIS data, point clouds, and satellite imagery - not just a chat interface.
Move beyond the demo to something your customers can actually rely on.
I'm an AI engineer who works with spatial software vendors as a contractor - hands-on, embedded with your team, from first experiment to production in products and platforms used by external customers.
We start from your product and your users' workflows, and identify where an agent creates real value - not where the hype says it should.
Agents that drive your existing tools and understand geodata - whether your users manage farmland, forests, satellite imagery, or 3D scans. Deeply integrated, not a chatbot bolted on the side.
Production-grade architecture, evaluation, and fine-tuning - so what ships is something your customers can rely on, not a prototype.
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A long-running agent works through a complete analysis on its own: it takes survey data of an alpine glacier and calculates how the ice has changed - the kind of multi-step task agents can now carry end to end.
Spatial data is more than point clouds. This agent runs Monday triage across a portfolio of fields: it reads crop stress from satellite imagery, reasons about the likely cause field by field, and plans which fields an advisor should visit first.
Agents are most powerful when they can reach for the tools experts already use. Here an AI agent I built drives CloudCompare to run a volume calculation for slope monitoring.
Pairing graph-based point-cloud segmentation with an LLM agent that reasons over a scene and edits it to spec. Presented at the XXV ISPRS Congress 2026 in Toronto and published in the ISPRS Archives.
I'm an independent AI engineer based in Germany. I work hands-on - from rapid prototyping to production-oriented system design.
My focus is LLM agents and orchestration, retrieval pipelines, spatial and 3D data reasoning, and the evaluation and observability that keep them reliable. Before AI, I led software and product teams at BMW, Audi, and DriveNow, and ran my own engineering studio, Kawunu, delivering production systems for enterprise and industrial clients across mobility, manufacturing, healthcare, and logistics.
For the longer version, see my background on LinkedIn.
I take on a small number of engineering and R&D engagements at a time. If you build a spatial, geospatial, or 3D product for external users, get in touch.
Running a surveying, scan-to-BIM or geodata team rather than a software product? There is a workshop for that.
