Embedded Artificial Intelligence (AI) paves the way for more autonomous, responsive, and intelligent systems. Our expertise helps you integrate AI directly into your devices, taking into account the real-world constraints of embedded environments: energy consumption, cost, computing power, and size.

We support you in finding the right balance between AI model accuracy and system requirements. With tailored tools like TinyML, a proven methodology, and a structured workflow, we ensure a smooth and optimized integration from development to deployment.

Each embedded application requires specific algorithmic choices. We have strong expertise in adapting major model families—traditional machine learning, convolutional neural networks (CNNs), and recurrent neural networks (RNNs)—to deliver reliable performance, even on resource-constrained platforms.

Finally, the efficiency of embedded AI also depends on the choice of hardware architecture. Whether it’s on a microcontroller (MCU), processor (MPU), FPGA, NPU, or RISC-V-based solutions, we implement architectures capable of running advanced algorithms directly in the field.