Predictions you can put a number on
Simulations and lab tests are slow and expensive. We learn from the results you already have, then predict the next one in seconds.
Every prediction comes with a range, so you can see how sure the model is. Move past what it has seen and it tells you, instead of guessing quietly.
STOCHOS, the engine · STOCHOS Flow, the workflows · How it works
Backed by the CADFEM Group, partnered with Ansys
Official Ansys Technology Partner
STOCHOS Flow connects to Ansys workflows: it reads Ansys Workbench parameters and calls the Ansys Solver node for selective validation, so the solver stays the source of truth. See the integration
Part of the CADFEM Group since 2024
The CADFEM Group acquired a stake in PI Probaligence. PI continues to develop STOCHOS and STOCHOS Flow as its own products, with the reach of a simulation group behind it.
Together with our partners and a global channel network, we bring probabilistic AI to engineering and R&D workflows around the world.
What got us here
February 2026First prize at the Robert Bosch Foundation Health Award
For AI-supported prevention of diabetic foot syndrome, with Niederrhein University of Applied Sciences and CID GmbH.
January 2026Introducing STOCHOS Flow
A visual workflow editor that runs locally, with an AI assistant that builds and wires the node graph.
November 2025Official Ansys Technology Partner
Formal partner status, on a collaboration that had been running since 2022.
November 2025Automated verification processes at GEMÜ
Konstruktionspraxis on GEMÜ automating the strength verification of metallic valve bodies.
September 2024From AI startup to global player
Our managing director's account of the path from first research projects into the CADFEM Group.
January 2024PI Probaligence becomes part of the CADFEM Group
CADFEM acquired a stake and made PI its network partner for AI in simulation.
Where you will meet us
Conferences, trade fairs, and customer floors across Europe and Asia. Based in Grafing bei Munich.
PI stand at the CADFEM Conference in Rapperswil, 2026
PI booth at the European Coatings Show, 2025
CADFEM Conference in Austria, 2024
CADFEM Konferenz Rapperswil, 2025
Talking chemistry at the European Coatings Show, 2025
STOCHOS Flow on stage in Rapperswil, 2026
Visiting CADFEM SEA in Singapore, 2025
PI stand at the Lacktagung in Aachen, 2025
The team at the European Coatings Show, 2025
Exhibition floor, CADFEM Conference 2024
STOCHOS at the CADFEM AI Conference Singapore, 2026
The office in the CADFEM building, Grafing
One office, in the CADFEM building
- Address
- PI Probaligence GmbH
Am Schammacher Feld 37
85567 Grafing bei München, Germany - Getting here
- About 30 minutes by train from Munich East Station. We moved into the CADFEM building in July 2025. Read the story
Aerial: Digitales Orthophoto, Bayerische Vermessungsverwaltung, CC BY 4.0. Hosted locally, so this page calls no external service.
What this looks like in practice: AI for Engineering, AI for Paint and Coatings, AI for Chemical R&D.
Common questions
01Where is PI Probaligence based?
PI Probaligence is based in Grafing bei Munich, Germany. It was founded in 2018 as a spin-off of the Institute for Modeling and High Performance Computing at Niederrhein University of Applied Sciences, and works with engineering and R&D teams internationally.
02Is PI part of CADFEM?
Yes. The CADFEM Group acquired a stake in PI Probaligence, and PI has been part of the CADFEM Group since 2024. PI continues to develop STOCHOS and STOCHOS Flow as its own products for engineering and R&D.
03Is PI an Ansys partner?
Yes. PI Probaligence is an official Ansys Technology Partner. STOCHOS Flow connects to Ansys workflows, reading Ansys Workbench parameters and calling the Ansys Solver node for selective validation, so the solver stays the source of truth.
04How do I request a demo?
Use the Request a Demo button or the contact page. Bring one use case, bottleneck, or dataset, and we can outline where STOCHOS fits and what a realistic proof of concept could look like. PI often works with teams that have no in-house machine learning team.
