Edge AI & TinyML
Instant responses, no cloud bill, and data that never leaves the device.
When a product has to react the moment something happens, a round trip to the cloud is too slow, too costly and stops working the instant the connection drops. We put the model on the device itself, so it responds in milliseconds, keeps working offline and never has to send the data it reads out of the customer’s hands. These are the models from our AI Labs, made to fit a microcontroller, a Jetson or the NPU in your SoC.
01 / What it solves
Spot, count and track what matters in the camera feed instantly, from a tiny Cortex-M sensor to a multi-camera Jetson rig.
Wake words, spoken commands and sound alerts handled on the device, with no audio sent anywhere.
Predictive maintenance, gesture and activity recognition read straight from the sensors on board.
One loop from data to training to on-device evals and over-the-air updates, so the product improves in the field.
02 / Stack
Industry-standard, boring in the good way: tools with a future, that the next engineer will know too.
Inference runtimes
TensorFlow Lite / LiteRT
Quantised models on MCUs and mobile SoCs
ONNX Runtime
Portable inference across CPUs, GPUs and NPUs
TensorRT & DeepStream
GPU-accelerated vision pipelines on Jetson
Edge Impulse & TinyML
Rapid model iteration for constrained targets
Target hardware
Cortex-M with CMSIS-NN
Keyword spotting and anomaly detection in kilobytes
NVIDIA Jetson
Multi-camera vision at the edge
Google Coral / Hailo
Dedicated accelerators for low-watt inference
Qualcomm & NXP NPUs
On-SoC acceleration in production devices
Model pipeline
PyTorch
Training, distillation and export
Quantisation & pruning
INT8 / INT4 models that keep their accuracy
Evals on-device
Accuracy, latency and power measured on the target
OTA model delivery
Models shipped and rolled back like firmware
03 / Approach
Latency, memory and current draw are measured on the real device, every model revision.
Duty-cycled inference and accelerators chosen for the power budget the product actually has.
Raw data stays on the device. Only what the product needs leaves it, if anything does.
Data from the fleet feeds training; a better model ships back as an ordinary update.
04 / Standards
We adhere to industry standards and proven methods on every project — they are what makes the estimate hold.
05 / Infrastructure
Good questions
Straight answers to what buyers ask before they start.
Yes, where the hardware allows. WNC starts with the smallest model that does the job (distilled or quantised small models) and deploys it on NVIDIA Jetson and similar NPUs, with latency and power budgets as acceptance criteria.
Related work
Projects and products of ours that involved this service.
Start
Tell us what it has to see, hear or predict, and on what hardware. An engineer writes back with an honest scope and price.
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