A Symbiosis of Code and SilicoN
Our entire service model is built around a single philosophy: your existing hardware is the foundation for future intelligence. We work as a seamless extension of your engineering team to integrate machine learning into the heart of your product.
Our services.
-
Legacy System Augmentation
This is our core offering. You have a deployed product with a Cortex-M microcontroller; you need it to be smarter. We analyze your hardware constraints, power budget, and performance requirements to develop and integrate a machine learning model that adds new, valuable functionality. This could be adding a simple off-the-shelf anomaly detector or a fully custom vision model.
Target MCUs: ARM Cortex-M4, Cortex-M33, Cortex-M7
Integration: Bare-metal firmware or RTOS (e.g., FreeRTOS, Zephyr) integration
Focus: Real-time binary classification and anomaly detection
-
Field Data Acquisition & Model Training
If a pre-built model isn't enough, we engineer the complete data-to-model pipeline. We'll help you code a robust, GDPR-compliant data collection system for your deployed devices. The real-world data gathered is then used to train a bespoke ML model, optimized specifically for your hardware and your unique operational environment.
Process: Data Strategy → Secure Collection → Cleansing & Annotation → Custom Model Training → Validation
-
Performance & Footprint Optimization
Every byte of RAM and flash memory is precious. We specialize in the art of model quantization, pruning, and algorithmic optimization to ensure our ML solutions fit within the tight constraints of your microcontroller. The result is a model that runs efficiently without compromising the primary function of your device.
Techniques: Post-Training Quantization (PTQ), Quantization-Aware Training (QAT), model architecture refinement.
Goal: Minimal footprint, maximum performance-per-watt.