From image to system.

We build computer vision pipelines that turn image and geospatial data into structured, verifiable results. This page shows how that works: which data we process, what our models do, and how the results reach your systems.

Your experts keep the final say.

Automated detection is only as valuable as its verifiability. That is why the domain expert is a fixed part of the pipeline.

Where standard models fail, our work begins.

Generic vision models are trained on common objects in clean images. Construction sites, open trenches, and industrial environments look different: occlusions, shadows, dirt, perspective distortion. Add object classes that rarely appear in public datasets. Specific components from European manufacturers used in infrastructure are generally not included.

This is what we build our own model architectures and geometry-matching methods for. The underlying long-tail problem, the reliable classification of rare classes, is the subject of our own peer-reviewed research.¹ In practice, this means usable results even with little training data and rare events.

Your data stays your data.

Our solutions are GDPR-compliant and can run on-premise or fully locally on request. Trained models and datasets belong to you. For municipal clients and network operators, this is not just a matter of trust; it is often a procurement requirement.

Point cloud analysis as a standalone solution is in development. Satellite imagery analysis is at the concept stage. We are following Gaussian splatting as a capture and visualization method for future applications.