On-Device Intelligence • TinyML • Real-Time Inference

Edge AI Solutions

We design and deploy machine learning systems that run efficiently at the edge — on microcontrollers, NPUs, FPGAs and edge GPUs — delivering low latency, high privacy and minimal power consumption.

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AI That Runs Where It Matters

Cloud AI is powerful, but many applications require intelligence to run locally: industrial inspection, autonomous machines, wearable devices, smart sensors and safety-critical systems. We specialise in bringing advanced machine learning models to resource-constrained hardware without sacrificing accuracy or reliability.

Our Edge AI services cover the full pipeline — from model selection and training, through optimisation and quantisation, to final deployment, validation and integration with your product hardware.

Technologies We Offer

A complete set of Edge AI capabilities for real-world products and systems.

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TinyML & Microcontroller AI

Machine learning on 8-bit, 16-bit and 32-bit microcontrollers. Keyword spotting, anomaly detection, simple vision and sensor classification with extremely low power budgets using TensorFlow Lite Micro, Edge Impulse and CMSIS-NN.

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Model Optimisation

Quantisation (INT8, INT4), pruning, knowledge distillation, operator fusion and architecture search to shrink models for edge deployment while preserving accuracy.

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Computer Vision at the Edge

Object detection, classification, pose estimation, OCR, visual inspection and tracking optimised for cameras connected to Jetson, Coral, OpenVINO or custom NPU boards.

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Hardware Accelerators

Integration and custom firmware for NPUs, TPUs, DSPs and FPGA-based accelerators. Support for NVIDIA Jetson, Google Coral, Intel Movidius / OpenVINO, Hailo, and custom AI accelerator chips.

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Audio & Sensor AI

Audio event detection, voice interfaces, vibration analysis, predictive maintenance models and multi-sensor fusion running locally on edge devices.

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Secure & Reliable Deployment

Model encryption, secure boot integration, runtime monitoring, fallback strategies and validation frameworks for production Edge AI systems.

Platforms & Frameworks

Frameworks

PyTorch, TensorFlow / Keras, ONNX, TensorFlow Lite, TensorRT, OpenVINO, TVM, Apache MXNet

Edge Runtimes

TensorFlow Lite Micro, CMSIS-NN, TensorRT, OpenVINO Runtime, ONNX Runtime, Edge Impulse

Hardware Targets

STM32, ESP32, nRF, i.MX RT, NVIDIA Jetson series, Coral Dev Board / USB Accelerator, Intel NUC + OpenVINO, custom FPGA/NPU boards

Typical Applications

Industrial visual inspection, predictive maintenance, smart sensors, autonomous equipment, wearables, smart cameras, quality control

Our Edge AI Delivery Process

1. Use-Case & Constraints

Define accuracy targets, latency, power, memory and hardware constraints together with your team.

2. Model Development

Data strategy, model selection or custom training, baseline evaluation and iterative improvement.

3. Optimisation & Porting

Quantisation, pruning, conversion to target runtime and validation on real hardware.

4. Integration & Validation

System integration, performance testing, reliability validation and knowledge transfer.