Imagine having a team of AI experts who help you set up smart tools in your workshop without you needing to be a coder. They provide the blueprints and the tech to help your machines run more efficiently and stop wasting materials. It is like a guided upgrade for your factory to make it greener and more reliable.
SMEs in manufacturing often lack the internal expertise and data infrastructure to adopt AI. This prevents them from reducing waste and lowering their carbon footprint effectively.
An ecosystem of AI experts and building blocks (Algorithms, Knowledge, Data) to create edge-AI applications for factories.
If you are a precision component manufacturer dealing with high material scrap rates — this project developed AI building blocks that reduce waste and carbon footprint. By using these tools, you can ensure your operations remain resilient while cutting down on environmental impact.
If you are a PCB assembly shop dealing with unpredictable machine downtime — this project developed AI-enabled applications at the edge that ensure resilient operation. This allows you to maintain steady production cycles with less manual oversight.
If you are a custom tool maker dealing with inefficient energy use during fabrication — this project developed a system to support more than 500 cases of AI applications. This helps you optimize your power consumption and lower your carbon footprint.
Based on available project data, the project uses Open Calls to provide SMEs access to AI expert competence, but specific pricing or service costs are not listed.
The project aims to support more than 500 cases from SMEs and mid-caps, indicating a high capacity for scaling across many small and medium-sized industrial sites.
Based on available project data, there is no specific information regarding IP ownership or licensing models for the AI building blocks.
The project focuses on AI applications at the edge and provides support for IoT and connectivity to ensure the AI integrates with manufacturing hardware.
SMEs receive expert assistance through Digital Innovation Hubs, including data analysis, data mining model creation, and application building.
The consortium is heavily weighted toward research and academic expertise, consisting of 3 universities and 7 research organizations. While there are 14 partners across 11 countries, the industry ratio is 0%, meaning the project relies on 2 SMEs and 4 other entities to bridge the gap to commercial application. This suggests the project is a technology-push initiative designed to transfer academic AI knowledge into the SME sector.
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