About
With a clear mission in mind
Embedl enables a world where physical AI is accessible, safe, and free from traditional computing constraints.
We empower companies to rapidly develop and deploy embedded AI in physical products, without sacrificing performance, reliability, or transparency. Our technology enables easy, fast, and practical AI development for edge and embedded systems, lowering the barrier to bringing intelligent products to market.
Accessibility is at the core of our approach. Embedl simplifies the development workflow, allowing teams to transition from model to product with minimal friction, thereby enabling faster iteration and a shorter time-to-market.
Safety is built in by design. We provide traceable and reproducible AI artifacts, ensuring that models, optimizations, and deployments can be audited, validated, and trusted across the product lifecycle.
We remove computing constraints through highly efficient AI models, optimized, and tailored runtime configurations. This allows advanced AI to run reliably on constrained hardware, unlocking new possibilities for intelligent edge devices.
Founders
Devdatt is a Professor in the Data Science and AI Division of the Department of Computer Science and Engineering at Chalmers University of Technology and co-founder of Embedl. He received his Ph.D. in Computer Science from Cornell University USA and was a postdoctoral fellow at the Max Planck Institute for Computer Science in Saarbrueken Germany. He was with BRICS (Basic Research in Computer Science, a center of the Danish National Science Foundation) at the University of Aarhus and then on the faculty of the Indian Institute of Technology (IIT) Delhi before joining Chalmers in 2000. He has led several national projects in machine learning and has been associated with several EU projects. He has been an external expert for the OECD report on “Data Driven Innovation”. He has published regularly in the premier machine learning and AI venues such as NIPS, ICML and AAAI.
Hans is the CEO and co-founder of Embedl. He received his M.Sc. in Complex Adaptive Systems from Chalmers University of Technology in 2012 and holds double B.Sc. degrees: B.Sc. in Engineering Physics and B.Sc. Industrial and Financial Management. Hans has through his career in industry and academia worked to improve machine learning and deep learning on a range of challenging problems – from industry 4.0 to sub-nuclear physics. He is a two-fold McKinsey Business Case Competition winner and is passionate about high-tech commercialization strategies.
Team
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