AI expansion module AE 550 awarded special prize
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- AI
- 7.3.2025
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Artificial intelligence is on the rise – including in the field of industrial manufacturing. More and more applications are relying on AI, whether in construction, quality control or intralogistics. Thanks to constantly improving algorithms and decreasing implementation costs, it is increasingly finding its way into production processes and related areas, enabling companies to further increase efficiency and precision.
In view of the increasing importance of artificial intelligence in industry, a special prize for artificial intelligence was awarded for the first time this year as part of the Products of the Year competition – and the AI extension module AE 550 was able to convince the jury.
The AE 550 AI extension module was designed specifically for industrial AI applications and offers a variety of advanced features. These include local processing of neural networks, which allows companies to run AI functions directly on their machines independently of cloud or network connections. The open system architecture, real-time capability, data security and scalability make the module a future-proof solution for a wide range of industrial applications.
In addition, the AE 550 scores points for its easy integration into existing KEBA products, long-term industrial availability and high energy and cost efficiency. Thanks to its compact, high-performance design, it is suitable for a wide range of industrial applications and stands out clearly from common consumer products due to its durability and resilience. A special toolchain enables companies to efficiently capture and process data and make it available for AI-supported decision-making.
The module is used in particular in industrial image processing and supports AI-supported quality inspections and object recognition, which optimizes production processes and reduces scrap rates.
Find out more about object detection and how to improve it in our white paper “Training an Object Detection AI Model on a Custom Dataset”.