We optimise and deploy machine learning models to embedded systems, IoT hardware, and mobile — enabling real-time inference without cloud dependency.
Scope follows the real workflow, environment, failure modes, and ownership model rather than a fixed vendor package.
Reducing model precision and removing redundant parameters to meet the memory and compute constraints of your target hardware without material accuracy loss.
Converting trained models to ONNX, TFLite, CoreML, or TensorRT for deployment on your specific hardware target, including custom operator handling.
Integrating inference pipelines into firmware and embedded Linux environments — handling I/O, preprocessing, and postprocessing on-device.
Deploying and managing models across fleets of IoT devices — with OTA update pipelines that do not require physical access to each device.
Systematic measurement of latency, throughput, peak memory, and thermal behaviour under sustained load on real target hardware.
Assess your target device — memory, compute, power budget, OS — to define exactly what the model can cost and what performance is achievable.
Target Device Hardware SpecQuantisation (INT8/FP16), structured pruning, and knowledge distillation to bring the model within hardware constraints with minimal accuracy loss.
Full Model Slim ModelConvert to ONNX, TFLite, CoreML, or TensorRT based on your hardware target. Handle operator coverage gaps and custom op implementation.
Optimised Weights Device RuntimeRun the full benchmark suite on real target hardware — latency, throughput, peak memory, and thermal behaviour under sustained load.
Device Runtime Benchmark ReportIntegrate the inference pipeline into your firmware or application. Handle preprocessing, I/O, and postprocessing on-device.
Benchmark Report Device BuildOTA update pipeline for fleet deployment. Remote monitoring of inference errors and model accuracy metrics.
Device Build Deployed FleetTechnology follows environment, operating constraints, team ownership, and long-term maintainability.
TensorFlow Lite · ONNX Runtime · CoreML · TensorRT · OpenVINO
NVIDIA Jetson · Coral TPU · Raspberry Pi · STM32 · Apple Silicon
PyTorch · TensorFlow · NNCF · Quanto · AIMET
Mender · Balena · AWS IoT Greengrass · Azure IoT Hub
Visual inspection systems, defect detection at line speed, predictive maintenance sensors — all offline-capable.
Medical imaging at point of care, vital sign monitoring, surgical assistance — without data leaving the device.
In-store shelf monitoring, cashierless checkout computer vision, traffic counting — no cloud latency.
Crop disease detection, drone-based vision, soil sensor AI — operating in areas with no connectivity.
Quantisation impact varies by architecture, dataset, hardware, and target precision. We benchmark FP32 against the proposed optimised model on your own evaluation set before agreeing a deployment target.
NVIDIA Jetson, Coral TPU, Raspberry Pi, various ARM Cortex-M devices, Apple Silicon (CoreML), and Android/iOS. The approach differs significantly by hardware class.
Yes. We integrate into your existing firmware and build system rather than requiring you to change your hardware stack.
We have implemented custom inference engines for non-standard targets. The feasibility depends on your device's compute and memory — we assess this in the hardware audit before committing.
Tell us your target hardware and the task you need to run on it. We will come back with an honest assessment of what is achievable.