Digital Twin
based on dezentralized AI
Shorter development cycles with greater customization - Validate product and process improvements before implementation using digital twins with our platform.
Get to know the platformCreate self-learning digital twins
Mapping machines and plants up to complete production lines as a digital twin is only the first step. With decentralized AI, you take the next step towards a self-learning and self-optimizing digital twin. A high-quality data basis is essential for training the AI model. Since model training is decentralized and no data needs to be shared, our platform is the perfect foundation for any digital twin.
Simulate manufacturing processes
Lay the groundwork for simulating your manufacturing processes with digital process twins. Simulating manufacturing processes requires high quality data at every step of the process. With Katulu, you can create synergies along value chains or within similar process steps across company boundaries. Benefit from better energy and resource efficiency and shorter time to market. Reverting to simulation across companies also enables you to react faster to changes and volatility in the market.
100% Data Sovereignty
The central collection of machine and process data for simulation in the cloud is viewed critically by many industrial users. It is therefore not surprising that the main problem with the use of AI in industry is the unwillingness of companies to share data. According to a recent study by VDMA, 65% of all AI projects in industry fail due to an insufficient data basis. However, the development of digital twins requires very large amounts of data. Katulu solves this problem and combines digital twins and data sovereignty. With our plattform, process and plant simulation models can be trained in a decentralized manner without sensitive data leaving the users' production facilities. Katulu's innovative approach enables continuous improvement of predictions even without data sharing. Katulu creates data sovereignty for ALL. Machine builders, operators and users benefit from mutual knowledge gain without data sharing.
