On-device inference, predictive maintenance, computer vision and in-app copilots — where AI earns its place in the product, not where it makes a good slide.
Not a bolt-on chatbot — AI woven into the product itself, and into how we build every other layer above.
Pulled from our case-study library — same AI/human split shown in full, industry context included.
An in-vehicle edge-AI system analyzes driving behavior locally and detects defined safety events in real time, without streaming raw video to the cloud.
A wearable analyzes motion data locally to identify movement patterns associated with potential falls, without continuously transmitting raw sensor data to the cloud.
A wearable captures sleep and physiological signals and uses AI to turn longitudinal data into personalized sleep insights and recommendations.
Both, chosen by the constraint — real-time and privacy-sensitive workloads run on-device; fleet-wide pattern detection (predictive maintenance) runs in the cloud.
TensorFlow Lite and ONNX Runtime most often for edge inference, standard PyTorch/TensorFlow for training — chosen by target hardware, not by default preference.
AI accelerates model-integration code and data pipelines; a senior engineer owns model selection, accuracy/recall tradeoffs and false-positive management — the calls that actually determine whether the feature works in the field.
Yes — this is one of our most common standalone engagements: adding predictive maintenance or on-device inference to hardware and firmware that already ships.
Bare-metal to RTOS, matched to your power and timing budget.
Boards designed for manufacturability, not just the bench.
The interface your customer actually touches, paired to the fleet underneath.
Fleet-scale telemetry and APIs behind the screen.
