Automate model compression, latency tuning, and secure OTA delivery to run intelligent features on the tiniest hardware.
Everything you need to run ML inference reliably on microcontrollers.
Automatic pruning and quantization tailored to MCU memory and compute limits.
Latency and energy-aware tuning with one-click benchmarks on target boards.
Loss-minimizing compression pipelines including quantization-aware training and distillation.
Signed firmware and model updates with rollback and delta patches to save bandwidth.
A simple, fast pipeline from model to device.
Upload your model or pick a prebuilt architecture. We analyze compute, memory, and accuracy trade-offs for your target MCU.
Apply automated quantization, pruning, and conversion to compact runtime formats with tunable objectives.
Securely publish to devices, track inference metrics, and iterate with OTA updates.
Trusted by teams shipping ML features on tiny devices.
Ultra-low-power keyword spotting for voice control on battery devices.
IMU-based activity recognition with minimal memory footprint.
Anomaly detection and sensor fusion for predictive maintenance.
Plug into your toolchain — C SDKs, Arduino libraries, TensorFlow Lite converters, and CI integrations.
CLI, Python SDK, and CI workflows for automated testing and deployment.
# example: build and deploy mcuai build model.tflite --target esp32 mcuai deploy --device-id 1234
Reference boards: ESP32, STM32, Nordic nRF52; bring-your-own-target with board SDKs and drivers.
Plans for individual developers to teams deploying at scale.
Model conversion, local testing, community support.
Questions about integrations, pricing, or pilot programs? Let's talk.