Intelligence at the Edge

This is our core service. We analyze your hardware constraints, data, and project goals to build and integrate an ML model that provides a new, specific capability to your product.

Custom Model Architecture: We design and train models (e.g., CNNs, RNNs, SVMs, Decision Trees) tailored to your task, whether it requires real-time classification or long-term stateful analysis.

Multi-Sensor Inputs: We develop models that can intelligently combine data streams from various sensors (e.g., IMU, microphone, temperature, current) to achieve a more accurate and robust understanding of the system's state.

Hardware-Specific Optimization: We specialize in optimizing for 32-bit MCUs, leveraging FPU and vector extensions (like ARM Helium) to maximize performance within a minimal power and memory budget.

Your device's reliability is non-negotiable. Our solutions are engineered to be safe and deterministic.

Our process involves a rigorous analysis of your device's real-time constraints. We profile your existing application's CPU load and memory usage to engineer a solution that fits within the available margins. Using techniques like intelligent scheduling and deferred computation, we provide Worst-Case Execution Time (WCET) guarantees for our inference routines. This ensures that our ML feature will always execute within its allotted time slice, never causing a deadline miss in your primary control loops or communication stacks.

High-quality data is the foundation of a high-performing model. If a dataset doesn't exist, we help you build one. We assist in developing robust, GDPR-compliant data collection firmware for your devices. This real-world data is then used to train a bespoke model that is perfectly tuned to your hardware and operational environment.