AI that runs on the device.
We bring intelligence to the edge — TinyML models, anomaly detection, computer vision, and predictive analytics deployed directly on your MCUs and SoCs.
Why run AI on the device?
Traditional IoT sends raw sensor data to the cloud for processing. AIoT flips this model — intelligence lives on the device itself.
The result: real-time decisions without round-trip latency, reduced bandwidth costs, continued operation during connectivity loss, and inherently better privacy since raw data never leaves the device.
We handle model selection, quantization, optimization, and deployment to your target hardware — whether that's an ARM Cortex-M4 with 256KB RAM or a more capable edge NPU.
Inference latency
Data transmitted
Cloud dependency (offline)
Where AI runs
TinyML Model Deployment
We optimize and quantize PyTorch/TFLite models to run on Cortex-M and RISC-V targets using TensorFlow Lite Micro, Edge Impulse, and CMSIS-NN.
Anomaly Detection
Unsupervised anomaly detection for predictive maintenance — vibration, current, temperature, and acoustic signatures on MCUs.
Computer Vision at the Edge
Object detection, person detection, and gesture recognition on ESP32-S3, Kendryte K210, and Himax WE-I for always-on vision.
Predictive Maintenance
ML pipelines that detect equipment failures before they happen — reducing downtime and maintenance costs for industrial equipment.
Voice & Keyword Spotting
Always-on wake-word detection running on tiny MCUs (Cortex-M4, M33) with sub-mW standby consumption.
Signal Classification
Time-series classification for vibration, EMG, ECG, and other sensor streams using CNNs and RNNs on edge hardware.
AIoT frameworks & tools
Ready to get started?
Tell us about your project and we'll put together a plan.
Start your IoT projectIoT & AIoT Weekly
Get practical IoT development insights delivered weekly.