Discover NanoEdge AI Studio the market leader for EDGE AI

NanoEdge AI Studio is a search engine desktop software for AI libraries, designed for embedded developers.

With Nanoedge AI Studio, find the best AI library for your embedded project, and start incorporating machine learning capabilities into your MCU C code.

Documentation, tutorials, videos, FAQ, and many more...

NanoEdge Studio

Windows 10 and
Ubuntu versions

Your production data are never sent to a cloud

Dedicated to
embedded devs

Libraries can run on every
Arm© Cortex-M microcontroller

Automatic data
quality check

Automates the search for best AI models

Collect and import data ‘live’ via serial port

Emulator to test a library before embedding

Easy to deploy
C libraries

NanoEdge AI Studio abstracts away Machine Learning and Signal Processing 


Project parameters + signal examples for context


Precompiled static library (.a) to link to your main code

Slide to discover our
in five steps


NanoEdge AI Library

Our machine learning models are developed in-house, ground-up from the algebra. Some are built from scratch, while others take inspiration from the big classics of AI/ML (e.g. kNN, SVM, neural networks…).

Each Nanoedge AI Library is optimised thanks to a few user-imported signal examples. It contains the best ML model for your project, combined with adequate signal pre-processing and optimal hyperparametrization. This Library provides simple and intuitive functions (learn, detect, classify) that bring powerful ML features to any Cortex-M C code.

Signal preprocessing

ML model


Library AI

Because we designed them specifically
for microcontrollers, our models:

Are ultra optimised to run on MCUs (every ARM Cortex-M)

Are ultra memory efficient (1-20kB RAM/Flash)

Are ultra fast (1-20ms inference on M4 80MHz)

Can be trained and used directly within the MCU

Can be integrated into
existing code / hardware

Consume very
little energy

Preserve your stack (static allocation, no dynamic allocation)

Don’t rely
on the cloud

Don’t require any
ML expertise to be created and deployed

2 types of librairies

Anomaly detection library

Based on « in situ » training (directly inside Cortex-M MCU). This kind of ML library can be used to learn a series of nominal signals and detect anomalies compared to that model.

Classification library

Based on « in studio » training. This kind of library can classify different classes of signals inside Cortex-M MCU using ML.

Cartesiam Tech Partners

Cartesiam Technology Partners

AI Partners