Embedl develops advanced tools and algorithms for Edge AI. Our mission is to make AI models run faster, more energy-efficient, and reliably across diverse hardware platforms, while significantly reducing development time.

We help teams deploy high-performance AI on real-world, resource-constrained devices.

Get deployable models that are compatible, quantized for, and run on any edge hardware

Eliminates late-stage deployment failures by making PyTorch models hardware-compatible and optimized from the start.

Secure MLOps platform for compliant Edge AI workflows

Let teams compile and verify models for embedded devices together, with full traceability across their workflow

Pre-optimized models that can be used off-the-shelf or customized for specific hardware target supported by the embedl-models package.

• Founded: 2018 (spin-out from Chalmers University of Technology)
• Commercial Operations: Since 2022
• Headquarters: Gothenburg, Sweden
• US Office: Palo Alto, California
• Recognition:
  • Named to CB Insights “AI 100” (2025) list of the most promising private AI companies

Embedl is used where real-time performance and efficiency are critical:

• Automotive & Autonomous Systems

  • Autonomous driving
  • ADAS
  • Driver monitoring
  • Predictive maintenance
  • Example: Kodiak Robotics

• Defense & Aerospace

  • Secure, energy-constrained AI inference
  • Example: Airbus

• Mobile & Edge AI

  • Running deep-learning models directly on phones and embedded devices
  • No cloud dependency via Embedl Hub

• Upload your model (PyTorch or ONNX)
• Quantize, compile, and benchmark on supported devices
• No physical hardware required

• Integrate Embedl directly into your training and deployment pipeline
• Works with TensorRT, TIDL, and more toolchains
• Access advanced hardware-aware optimization and performance insights
• Deploy to your own infrastructure

For tailored optimizations, specialized hardware support, and engineering collaboration, contact Embedl for full SDK access and support.

Headquarters (Sweden)
Gamla Almedalsvägen 39
412 63 Gothenburg, Sweden

Email: [email protected]