Given that real-world data is not inherently structured, traditional AI designed for the cloud often falls short when applied to real-time, device-specific applications. In addition, many AI models are intended for use with, and dependent on, constant access to the cloud and do not fit on resource-constrained edge devices with limited power, memory, and processing capacity. What if we could process data and leverage AI directly at the source? That is where edge AI steps in. Edge AI can substantially reduce latency and mitigate bandwidth limitations and security risks tied to the cloud, whether the source is a sensor, a smart device, or an autonomous vehicle. Edge AI enables devices to make instantaneous, autonomous decisions, driving faster, smarter, and more efficient operations. Leading this movement is Edge Impulse, which is pioneering solutions that make AI more accessible to developers worldwide. By simplifying edge AI development, the Edge Impulse platform empowers engineers to build, train, optimize, deploy, and monitor machine learning models directly on edge devices. With Edge Impulse, real-time intelligent decision-making is no longer a distant dream—it is a reality available to developers seeking to unlock the power of edge AI. Breaking Down the AI Adoption Barriers Founded in 2019 by former Arm executives Zach Shelby and Jan Jongboom, Edge Impulse was born out of a desire to democratize machine learning and AI development for global developer and engineer communities. Traditional edge AI development often requires teams of specialized data scientists and embedded engineers. Edge Impulse streamlines edge AI development processes and workflows, enabling enterprise teams and developers of all skill levels to create, train, and deploy AI models tailored to the specific needs of their industries and use cases. The company continues to deliver on and expand its mission: to break down the barriers that have historically limited AI adoption and empower engineers to solve real-world problems at the edge of the network. “Our purpose-built platform enables engineers to bring intelligence to any edge device, from simple microcontrollers to powerful GPUs, without the need for specialized expertise in machine learning or expensive cloud infrastructure,” says Shelby, co-founder and CEO. Making AI More Accessible and Deployable Since its inception, Edge Impulse set out to disrupt the traditional approach to AI development. At the time the company was founded, typical AI workflows relied on large, centralized systems for training AI models and depended heavily on expensive cloud-based infrastructure to run those models. As the Internet of Things (IoT) and edge devices proliferated, the need to bring AI closer to where data was generated and used became increasingly apparent. Edge Impulse recognized both the demand for processing data at the source—closer to the devices that generate it—and the opportunity to reduce latency and reliance on costly cloud investments. Shelby recalls the initial vision: “We wanted to democratize AI development for engineers, enabling them to build AI solutions on edge devices without the need for specialized machine learning expertise. The goal was to make AI more accessible and deployable across various industries.” Edge Impulse began with a clear focus on enabling easy access to machine learning tools and capabilities, ensuring that AI development could scale beyond elite data scientists and machine learning engineers. The company envisioned a platform that would allow developers across manufacturing, healthcare, automotive, industrial automation, research, and education to build AI models with real-world data—even without deep machine learning expertise.