Products
TOPAS Vision
TOPAS Vision is your end-to-end solution for reliable, production-ready AI in the field of computer vision.
We develop and integrate tailored models that analyze image data robustly, fit seamlessly into existing processes, and deliver strong performance in real-world operation — from the initial feasibility assessment to deployment on edge hardware.
We combine methodological excellence with proven real‑world robustness. Whether tiny‑object detection, advanced segmentation, geometric map fusion, or high‑performance C++ inference: TOPAS Vision delivers not just a model, but a complete, ready‑to‑use solution.
Coverage Path Planning
Efficient coverage of complex areas – automated, robust and adaptable across applications. Our Coverage Path Planning solution automatically generates optimal driving and flight paths for autonomous systems. Obstacles, no-go zones and complex area geometries are reliably taken into account, while resource consumption, mission time and overlaps are minimized. The result is safe, feasible and production-ready coverage paths for applications in robotics, agriculture, drone operations and industrial automation.
TOPAS Model Fitting
TOPAS Model Fitting is a flexible tool for automatically creating digital twins of technical systems using your own datasets. It recognises and adapts complex models either via a simple graphical user interface (GUI) or directly via a modern C++ library.
TOPAS Model Fitting thus supports the real-time prediction, control and optimization of system behaviour, as well as long-term scenarios in fields such as robotics, automotive and aerospace.
WORHP
WORHP is a powerful mathematical software library designed to solve large-scale non-linear optimization problems. It provides precise and efficient algorithms for complex scientific and technical applications such as motion planning and control system optimization. Thanks to its modularity and scalability, WORHP is ideal for highly complex tasks that require high levels of accuracy and computing power.
