AI that runs on the device, not the cloud.

We optimise and deploy machine learning models to embedded systems, IoT hardware, and mobile — enabling real-time inference without cloud dependency.

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01. Hardware Audit: Assess your target device — memory, compute, power budget, OS — to define exactly what the model can cost and what performance is achievable.

02. Model Optimisation: Quantisation (INT8/FP16), structured pruning, and knowledge distillation to bring the model within hardware constraints with minimal accuracy loss.

03. Runtime Conversion: Convert to ONNX, TFLite, CoreML, or TensorRT based on your hardware target. Handle operator coverage gaps and custom op implementation.

04. On-Device Test: Run the full benchmark suite on real target hardware — latency, throughput, peak memory, and thermal behaviour under sustained load.

05. Deployment Build: Integrate the inference pipeline into your firmware or application. Handle preprocessing, I/O, and postprocessing on-device.

06. Fleet & Monitoring: OTA update pipeline for fleet deployment. Remote monitoring of inference errors and model accuracy metrics.

How We Work

Every engagement follows defined phases — each delivering something tangible you can evaluate before we move forward.

Hardware Audit

Assess your target device — memory, compute, power budget, OS — to define exactly what the model can cost and what performance is achievable.

Memory budget

Compute ceiling

Power envelope

INPUT

Target Device

RAM · TOPS · Power · OS

OUTPUT

Hardware Spec

Step 1 of 6

What We Deliver

Specific capabilities and deliverables — built, tested, and handed over.

Reducing model precision and removing redundant parameters to meet the memory and compute constraints of your target hardware without material accuracy loss.

INT8 / FP16 quantisationStructured & unstructured pruningAccuracy delta measurement

Converting trained models to ONNX, TFLite, CoreML, or TensorRT for deployment on your specific hardware target, including custom operator handling.

Multi-runtime supportCustom op implementationNumerical equivalence testing

Integrating inference pipelines into firmware and embedded Linux environments — handling I/O, preprocessing, and postprocessing on-device.

Firmware integrationC++ inference wrapperPreprocessing on-device

Deploying and managing models across fleets of IoT devices — with OTA update pipelines that do not require physical access to each device.

OTA update pipelineRollback capabilityVersion management

Systematic measurement of latency, throughput, peak memory, and thermal behaviour under sustained load on real target hardware.

Real hardware testingp50 / p99 latencyThermal profiling

Technology Stack

We select tools based on each project's requirements — not trends.

Runtimes

TensorFlow LiteONNX RuntimeCoreMLTensorRTOpenVINO

Hardware

NVIDIA JetsonCoral TPURaspberry PiSTM32Apple Silicon

Optimisation

PyTorchTensorFlowNNCFQuantoAIMET

OTA & Fleet

MenderBalenaAWS IoT GreengrassAzure IoT Hub

Industries We Serve

Sectors where we have applied edge ai deployment — each with specific requirements we understand.

Manufacturing

Visual inspection systems, defect detection at line speed, predictive maintenance sensors — all offline-capable.

Healthcare

Medical imaging at point of care, vital sign monitoring, surgical assistance — without data leaving the device.

Retail

In-store shelf monitoring, cashierless checkout computer vision, traffic counting — no cloud latency.

Agriculture

Crop disease detection, drone-based vision, soil sensor AI — operating in areas with no connectivity.

Automotive

ADAS perception models, in-cabin monitoring, predictive diagnostics — safety-critical, low-latency.

See all industries

Frequently Asked Questions

Common questions about this service, process, and what we hand over at the end.

For most tasks, INT8 quantisation causes under 1% accuracy drop versus FP32. We measure this precisely on your task and data before committing to an optimisation 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.

Building for edge?

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.

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