• You want to boost competitiveness through digitalisation and automation
• You have to handle large-scale repetitive tasks (e.g., quality assurance)
• You face processes with heterogeneous parameters (e.g., uneven materials, temperature variations)
• You struggle to find qualified staff

• Where do bottlenecks or capacity gaps occur?
• Which tasks are especially time-consuming?
• Where do errors happen most frequently?
• Then you receive an overview of possible AI use cases that could fit your situation. At this stage realistic expectations are set, and rough timelines and budget ideas can be discussed.

Our experience shows that a deep understanding of the technical problem is essential for a value-creating solution. Therefore, our work typically starts with an on-site visit to grasp the materials and manufacturing methods you use and to gather the necessary foundations and precise objectives.

After that we identify potential risks and challenges in implementing an AI solution. Depending on the findings of the fact-finding phase, the next logical step may be a feasibility study with minimal investment, or a roadmap for gradually achieving initial value creation and outlining future expansion opportunities.

• Data: AI models are trained on large data sets that have been generated under standardized conditions.
• Computing hardware: AI applications are computationally intensive, and the hardware must meet your data-protection requirements as well as your IT infrastructure.
• Qualification & standards: An AI model is only as good as the underlying expert knowledge (e.g., process descriptions, error catalogs).