Edge AI · AutoML · Sensor Intelligence
Automated ML pipeline. Optimized firmware. Runs on any MCU.
The Challenge
General-purpose ML frameworks ignore the unique constraints of sensor data, tight memory budgets, and microcontrollers. SensiML was purpose-built to solve exactly this.
✗ Without SensiML
Months of manual dataset manipulation, feature engineering, and edge model optimization. Cloud frameworks generate bloated models. Engineers spend more time fighting tools than building products. Inference accuracy suffers at the edge.
✓ With SensiML
AutoML automates data labeling, feature extraction, and model optimization end-to-end. Ship deployment-ready code that runs efficiently on edge processors from 64-bit MPUs down to 8-bit MCUs — with best-in-class accuracy.
How It Works
The Analytics Toolkit handles every stage of your ML pipeline — so you focus on your product, not ML infrastructure.
Capture, manage, and label both real and synthetically generated train/test sensor data with Data Studio. Label events with an intuitive visual interface — no scripting needed.
Analytics Studio runs AutoML — testing hundreds of algorithm combinations and automatically selecting the best for your device constraints.
Knowledge Pack outputs optimized C/C++ inference code targeted to your specific MCU, processor, and OS — ready to compile.
Validate in the TestApp, then ship. Continue improving models on-device as new data arrives in the field.
Instruct expert AI agent workers to build your model for you. Via a simple chat interface, utilize SensiML's pre-trained AutoML agents to perform some or all of the Analytics Toolkit workflow steps for you.
Use Cases
SensiML powers recognition tasks across many edge AI use cases where cloud-dependent AI simply isn't an option.
Detect machine faults, glass breakage, infant cries, or custom sounds on battery-powered devices.
Classify movement, gait cycles, ergonomics, and exercise types from wearable or embedded motion sensors.
Catch equipment faults before they fail. Enable predictive maintenance across machinery and infrastructure.
Add intuitive gesture control to automotive, industrial machinery, and smart consumer devices.
Deploy custom wake words and voice commands on ultra-low-power devices without cloud connectivity.
Monitor transportation, heavy machinery, and infrastructure for vibration signatures that signal failure.
Why SensiML
Not adapted from cloud tools — designed from the ground up for microcontrollers and real-world sensor data.
From 8-bit MCUs to ARM Cortex-M, RISC-V, ESP32, AI accelerators, and larger edge processors. Supports 20+ silicon platforms including Arm, Espressif, Infineon, Intel, Microchip, NXP, Nordic, and ST.
AutoML handles feature engineering, model selection, hyperparameter tuning, and firmware generation — eliminating months of manual work.
Knowledge Packs output optimized C/C++ code tailored to your device — not generic Python notebooks you have to convert yourself.
Algorithm libraries built specifically for time-series sensor data — not repurposed from image or NLP models that don't fit your use case.
Piccolo AI offers the industry's first open-source AutoML for edge IoT — hardware-agnostic and freely available via sensiml.org.
Analytics Studio AI Assist provides a natural chat interface to automate model building using expert agents. Data Studio's GenAI voice tool creates hyper-realistic synthetic speech datasets — dramatically accelerating voice model development.
Start free with Piccolo AI or unlock the full Analytics Toolkit.