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Sustaining the future with production systems built for tomorrow.

When done right, predictive maintenance profoundly effects the way we manage the lifecycle of our assets. It aligns untapped economic potentials of increased reliability and optimized service with a more sustainable usage of parts and machinery. We are convinced that machine manufacturers are in a position to create the most far-sighted and interpretable predictive maintenance capabilities, and we are determined to enable them to do so.


Christoph Netsch


Christoph (M.Sc. mechanical engineering) has developed, managed, and executed R&D projects for intelligent maintenance solutions throughout many industries. He draws experiences from working with multiple automotive and machine tool manufacturers. Having founded and grown Aktion Sodis, a non-profit organization for technical development cooperation, he knows what it takes to build an organization from the roots.


Till Schöpe


Till (M.Sc. electrical engineering, M.Sc. Cybernetics and Robotics) worked in different research institutes and the automotive industry. He developed and industrialized new machine-learning based solutions across various industries, spanning from semi-conductors to cheese fabrication. He knows the bits and pieces which make it so challenging to bring new algorithms onto production machines and ultimately define success or failure.

Benedikt Schindele.jfif

Benedikt Schindele


Benedikt (B.Sc. in production engineering), brings valuable machine manufacturer's perspective to our ongoing product and business development. With experience in manufacturers, tool builders, leading research institutes, and as an entrepreneur he emphasizes user experience as a key component of the entire customer journey from analysis to solution rollout.

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