Our Process: From Concept to Embedded Intelligence
Our process is structured to de-risk embedded ML development.
We move from concept to on-device reality in four clear, collaborative phases.
Phase 1: Discovery & Scoping
Connect: It starts with a technical conversation. You share your hardware specifications (MCU, sensors, memory), your operational constraints, and the problem you want to solve.
Calibrate: We perform a feasibility study and co-author a detailed Statement of Work (SOW). This document defines the project scope, deliverables, timeline, and the specific, measurable acceptance criteria that will define success.
Phase 2: Development & Validation
Data & Design: We analyze your data and begin designing the model architecture. The focus is on creating a model that not only meets the accuracy requirements but also respects the strict computational budget of your target MCU.
Offline Validation: We present the model's performance on your dataset. You see the accuracy, precision, and recall metrics and validate that the model is solving the right problem before it ever touches your hardware.
Phase 3: Integration & Optimization
Deployment: The validated model is meticulously quantized and optimized. We deliver it as a self-contained C library, complete with API documentation and example code.
On-Target Verification: We work with your team to integrate the library into your firmware and verify its performance on the actual hardware. This includes measuring its true RAM/Flash footprint and execution latency to ensure it meets the acceptance criteria defined in the SOW.
Phase 4: Support & Evolution
Support: We provide ongoing support to ensure your newly intelligent system performs as expected in the field.
Growth: As new data becomes available or your needs evolve, we are ready to iterate, retrain, and redeploy enhanced models to your fleet.