Stilled Kinetics

Interpretable and verifiable machine learning for tabular data, optimized for minimal computational footprint on edge hardware.

Our engine, your data. Built for

High-stake applications where models need to be fully transparent (no black box elements).
Edge analytics & control where low latency and low CPU/Memory footprint is paramount.
R&D: uncovering non-linear and inter-coupled parameter relationships.

Backstory

Our core engine was developed at Chalmers University of Technology, and initially deployed in Fusion Power research. The project is now part of Chalmer Entrepreneurial Fellowship, backed by Chalmers innovation office and Chalmers Ventures.

Industry

Our initial pilot cases are in the mobility sector, where we produce closed-form analytical expressions designed to run directly on the edge to avoid challenges related to cloud analytics. That said, the technology can be used for any tabular data problem in any domain.

Becoming a pilot partner

I'm Andreas Gillgren, founder of Stilled Kinetics. If you're interested in becoming a pilot partner, please fill out the form below. If you have questions, feel free to reach out to me directly at [email protected].