WASABI - White-label shop for digital intelligent assistance and human-AI collaboration in manufacturing
The project demonstrates its approach across the manufacturing life cycle through business cases covering waste valorisation, onboarding and upskilling of factory workers, and assisted quality assurance. These use cases are designed to show how digital assistance can support sustainability, agility, resilience, adoption of intelligent assistance technology, innovation capacity and industrial leadership.
Key objectives
- Minimise avoidable sustainability incidents in manufacturing, including waste, material losses and energy-intensive quality issues.
- Reduce training time for workers by supporting onboarding, upskilling and human-AI collaboration in manufacturing environments.
- Increase the uptake of intelligent digital assistance technology by making DIA solutions practical for SMEs and mid-caps.
- Make digital assistance technologies and related services available through innovation infrastructures and a federated white-label shop.
- Address barriers and scepticism around intelligent digital assistance in industry, including trustworthiness, AI-related legal aspects and user acceptance.
Use cases / business cases
- Augmented waste management and valorisation: Uses digital assistance and the rEUse platform to identify and document production waste and support reuse or circular-economy processes.
- Assisted workforce management: Supports faster onboarding and upskilling of manufacturing workers with knowledge management, learning materials and assistant-based guidance.
- Assisted quality assurance for sustainable products: Supports quality and product testing procedures, end-of-line checks and prescriptive quality analytics to improve efficiency, safety and sustainability.
The project receives funding from the European Union’s Horizon Europe programme under Grant Agreement No. 101092176. Total cost: €8,828,395.19; EU contribution: €7,710,053.53.
TTTECH contributed to the development of the WASABI environment and is responsible for deployment-related activities in the Open Calls. The contribution focuses on container-based deployment on edge and cloud infrastructure, technical coordination for experiments, and support for the practical use of the WASABI stack in manufacturing-oriented AI assistance solutions. This builds on TTTECH’s expertise in cybersecure edge and cloud computing and helps experiments deploy, configure and validate digital intelligent assistants in realistic industrial settings.
