AI-based service ecosystem for technical service in industry 4.0

Germany’s industry is undergoing a fundamental shift from products to services in value creation. However, the required service knowledge for industrial facilities exceeds the knowledge of individual service technicians and in part even of companies. Along with the shortage of skilled workers, German SMEs face a tremendous challenge in the coming years to maintain a leading position in the provision of services.

The challenge

Service-Meister is developing an AI-based service platform suitable for all facilities, departments, and companies to support Germany’s SMEs. An important sub-goal is to enable skilled workers with less training to provide complex services with the help of digital advisers such as service bots and smart services. A second sub-goal is to be realized by delivering digitized service knowledge on a platform to enable service scalability across companies. This creates a service ecosystem to counteract the shortage of skilled workers in Germany and makes German SMEs competitive in the long term.


Our contribution

We contribute to the recording of data exchange requirements and the analysis of identified requirements in the project. Fraunhofer ISST also developed the easy to use International Data Spaces connector framework, which will be utilized in this project as well. For the duration of the project and especially for the reference implementation, the connector is being provided to the consortium free of charge and adapted as needed.



Service-Meister digitizes service know-how. Where skilled workers are lacking, the project makes expert knowledge scalable and explainable through digital tools. It is based on interoperability and standards. Interfaces allow all technologies to be quickly integrated into existing IT landscapes while avoiding lock-in effects. Service technicians receive broad access to AI assistance systems. A specialized search feature will be supported along with answering routine questions and identifying systematic errors based on archived data, thereby benefiting from past experience.



You will find the entire consortium here

Project members include the following companies among others:

  • Adolf Würth GmbH & Co. KG
  • Atlas Copco IAS GmbH
  • Beuth University of Applied Sciences Berlin
  • KROHNE Messtechnik GmbH
  • TRUMPF Werkzeugmaschinen GmbH + Co. KG  




  • Subsidized by: Federal Ministry for Economic Affairs and Climate Action (BMWK)
  • Project number: 01MK20008G
  • Term: 01/2020-12/2022