Swedish Deeptech Startup Embedl Secures €5.5M Funding To Boost AI efficiency In Embedded Systems

Embedl helps companies in defence, automotive, and robotics deploy AI models—like CNNs and transformers—directly onto their hardware. Its optimisation technology can cut energy use by up to 83% and reduce hardware costs by half.

A spinout from Chalmers University of Technology, Embedl started commercial operations in 2022 and has since worked with major companies like Bosch and Kodiak Robotics to improve AI inference efficiency.

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As of 2024, inference costs have overtaken training costs, and demand for real-time, low-power AI that runs without cloud connectivity is rising. Embedl’s solution meets this need, offering efficient, on-device AI performance for next-gen products.

Hans Salomonsson, co-founder and CEO of Embedl, said: The world needs to make AI more energy efficient, fast. While the applications and usage of AI continue to skyrocket, we can’t increase energy consumption at the same level. Our solution will also help bring robotics and autonomous vehicles to the market faster, as we can help optimise the hardware’s energy efficiency while assuring the highest quality data being transferred instantly. We are grateful for the new and existing investors for their support.

Shubham Shrivastava, Head of Machine Learning at Kodiak, says that being able to deeply inspect AI models, optimise them for hardware, benchmark performance layer by layer, and deploy across platforms is a game-changer for their work.

This capability is especially useful in sectors like defence, where Embedl's Model Optimization SDK helps AI run efficiently on existing hardware—avoiding the need for expensive upgrades.

The SDK offers tools to prune, quantise and compress models, making them smaller and faster. Its modular setup allows developers to customise the optimisation process and apply domain-specific knowledge. Built-in visual tools make it easy to track model improvements.

In the automotive industry, where safety-critical features need to run on efficient, cost-effective hardware, Embedl’s edge AI tools simplify deploying powerful generative AI models across different hardware platforms—without driving up hardware costs.

The €5.5M round was backed by Chalmers Ventures, Fairpoint Capital, SEB Greentech, Spintop Ventures, and STOAF.

Jonas Bergman, investment director at Chalmers Ventures, said: This funding is a sign that Chalmers has the technical expertise to build great AI solutions. We at Chalmers Ventures are proud to continue backing our portfolio companies that deliver, and we expect great things from Embedl, in addition to the impressive achievements they have already made in such a short time.

With the new funding, Embedl plans to speed up the launch and commercial rollout of its SaaS platform, Embedl Hub.

About Embedl

Founded in 2018, Embedl builds technology to make deep learning inference more efficient. It’s designed for deep learning engineers and researchers who want to optimise their models for deployment on embedded systems (edge AI). Embedl’s main product is its Deep Learning Model Optimization SDK, which helps make AI models smaller, faster, and ready to run on edge devices.