Stream Analyze operates in the dynamic field of edge analytics, with a focus on delivering an advanced edge AI platform to the manufacturing and automotive industries. Our overall objective is to enable organizations to leverage the power of edge analytics and AI by providing an end-to-end low-code platform that empowers data scientists, engineers, and domain experts, even those with limited coding skills. We address the increasing challenge of handling and analyzing the vast amount of data generated in real-time at edge devices. Sending all this data to the cloud for analysis is impractical and unsustainable, leading to a growing trend of performing analytics and AI at the edge, where the data is produced. Stream Analyze provides a groundbreaking solution to this problem.
By developing and refining our cutting-edge platform, based on extensive academic research from Uppsala and Stanford Universities, we are working towards becoming the preferred platform for large manufacturing and automotive companies seeking to bring efficient edge AI solutions to market. Our platform’s impacts are significant, as we anticipate facilitating faster, more efficient, and scalable development and implementations of edge AI solutions, ultimately contributing substantial value and cost savings to our customers.
Through our platform, we strive to address identified problems and needs in the manufacturing and automotive sectors. By enabling real-time AI and analytics models on massive fleets of edge devices and microcontrollers/MCUs, we empower our customers to extract valuable insights, optimize operations, enable servitization, and drive innovation. With Stream Analyze, organizations can capitalize on the full potential of edge computing and AI to enhance efficiency, productivity, and competitiveness.
Overall, our project sets the stage for a transformative journey in edge analytics, providing a robust foundation for organizations to navigate the challenges and seize the opportunities presented by the rapidly evolving digital landscape. More specifically the work involved include how to widen the target range of hardware to a greater number of platforms, and a methodology to quickly add new targets as they emerge on the market. The project also adds plug-in technology for common development tools, such as Microsoft's WS Code so developers can continue working with the tools they know and love, yet with new functionality.
