About

I help organizations build operational intelligence systems.

I help organizations bridge the gap between operational expertise and modern AI-enabled decision systems.

DE

Operational intelligence as the organizing focus

My path runs from industrial engineering and automation through data science, platform delivery, and technical product management.

Across roles, the core problem has stayed the same: operational systems generate data and knowledge, but organizations often struggle to convert both into reliable decision support.

The BridgeOps perspective is to connect this full chain: data foundations, knowledge management, intelligence design, and operational adoption.

This positioning supports work across manufacturing, pharmaceutical operations, healthcare operations, and data and AI platform initiatives while keeping an operational systems mindset at the center.

How I combine disciplines

Industrial engineering + data platforms

I design systems that are technically sound and operationally usable, from raw data capture through governed information products.

Knowledge management + machine learning

I connect explicit operational knowledge, analytics, and model-based recommendations into traceable decision support.

Technical product management + change

I translate across engineering, business, and operations teams so new capabilities are adopted, governed, and sustained.

Selected impact

$5M+cost avoidance / value contribution through data-driven initiatives
$168K/yearautomated savings from analytics and reporting improvements
50%scrap-reduction pilot in an industrial quality context
15+ yearsengineering, automation, analytics, and technical delivery

Bodensee and DACH focus

I am based in the Bodensee region and focus on industrial and technical organizations across Bavaria, Baden-Württemberg, Austria, and Switzerland. The region connects the Mittelstand, manufacturing, MedTech, automation, mobility, and precision engineering — exactly the environments where robust data and AI solutions matter most.

Working principles

  • Start with the operational decision, then the model. AI only matters when it enables a better decision or action.
  • Build the data foundation before scaling AI. Without reliable data flows, models become fragile.
  • Technical depth must be explainable. Stakeholders need to understand why a solution works, what risks exist, and how it will be used.
  • Production readiness matters more than demo effect. Maintainability, monitoring, handoff, and adoption are part of the solution.

More context?

Review my projects, download my resume, or get in touch.

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