We offer cutting-edge software that enable ultra-low power IoT endpoints that implement AI to transform raw sensor data into meaningful insight at the device itself. Our flagship solution, the SensiML Analytics Toolkit, provides an end-to-end development platform spanning data collection, labeling, algorithm and firmware auto generation, and testing.

SensiML Analytics Toolkit

The solution to building intelligent endpoint devices quickly and easily


If you can capture what you seek to detect, SensiML's machine learning optimizer can automatically generate a predictive algorithm. Inordinate time and energy spent in iterative coding, testing, and re-coding can now be focused on extending algorithm capabilities making your smart device truly smart.


SensiML creates optimized algorithms that execute locally on the embedded sensor node, not a gateway or in the cloud. Thus, your applications benefit from true real-time processing where the sensing take place.


With tools that address the entire workflow for data-driven algorithm design, SensiML Analytics Studio can streamline all aspects including initial prototyping and proof-of-concept work. Our tools allow developers to quickly test sensor applications on hardware in a matter of days or weeks rather than months.


Get your devices out of the lab and into the hands of customers sooner with ongoing learning mechanisms that allow you expand model sophistication over time through crowd-sourced data feedback and regular updates. Your users can enjoy more engaging products as they contribute to solving their own corner case exceptions.

The SensiML Endpoint AI Workflow

Raw Signal Capture to Data Insight Labeling to Algorithm Generation to Firmware Code Generation to Test, Validation and Support

The Solution for Developing High Performance IoT Applications

SensiML Toolkit provides a set of developer applications that automate each step of the process for creating optimized endpoint sensor algorithm code. The overall workflow supports use of a growing library advanced ML and AI algorithms to devise code that adapt and learn from new data as it is collected in either the development phase or once deployed. Unlike many tools, SensiML focused on the COMPLETE process providing tools to streamline collection and labeling of data on the front-end, optimization and hardware specific acceleration of core algorithms, and validation and testing of resulting code both in backend bit-exact server emulation and empirical testing of endpoint IoT devices during product testing.


The Solution for Developing High Performance IoT Applications

The latest IoT applications being deployed are far more demanding than those just a couple product generations ago. Increasing expectations for more, better, and quicker intelligence strains existing cloud-based analytics typically used to gain meaningful insight. As developers strive to build richer sensing technology into smart IoT devices, system bottlenecks are more often a result of network dependence. The time for bringing more intelligence to the endpoint IoT devices where the sensor data originates is here and possible today.

Comparison: Cloud-Centric loT Network vs. Extreme Edge AI Enabled Networks

Cloud-Centric IoT Network

Acceptable solution when analysis needs are not real-time, sensors are simple binary switches or slow varying signals, and/or network bandwidth is plentiful.

Latency tolerant applications communicate with raw signals sent to the cloud for analysis from physical sensors and wired simple sensors with unconstrained network bandwidth available
Network traffic demand is high with rich sensors and many endpoints.
Latency is too great for many time critical applications
No insight if network fails or throughput is sufficiently impaired.
Security and data privacy breaches require end-to-end coverage

Extreme Edge AI Enabled Networks

Rapid cloud application integration with battery powered wireless sensors, data-rish sensors, and algorithm identified meaningful events enables viable use of low power Internet of Things networks with complex sensors
Sensor need only send useful insight data, not voluminous raw signal data.
Less network traffic allows use of cellular and IoT networks for remote endpoints
Zero network latency with local AI insight at endpoint
No network dependence with local AI insight at endpoint
Partitioned security/data privacy with local AI pre-processing and filtering

SensiML Enables AI on Endpoint Devices Without Data Science and Hand-Coding

SensiML Toolkit allows teams of all sizes and capabilities to be far more efficient in the development and commercialization of intelligent endpoint algorithms. Whether a small team with modest data science and firmware skills creating a niche smart device, or a large high-volume product team looking for application scale, SensiML can allow accurate, learning sensor analytics to be implemented in a fraction of the time and cost of hand-coded algorithms.


SensiML Adds Rapid Evaluation Capabilities to its Analytics Toolkit for Building Intelligent IoT Endpoints

Nov 25, 2019 - SensiML Corporation today announced the availability of a free trial version of its SensiML™ Analytics Toolkit and introduction of its Data Depot sample dataset repository.

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Free Software Toolkit Trial for AI-Based Wearable and Industrial IoT Applications

Nov 25, 2019 - If you’re developing AI-based consumer wearable or industrial IoT products, you’ll likely be evaluating various software tools to help implement AI on your wearable or IoT device.

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SensiML Analytics Toolkit Supports AI for Industrial IoT Predictive Maintenance Applications

May 23, 2019 - SensiML Corporation today announced that its Analytics Toolkit supports Artificial Intelligence (AI) for industrial IoT predictive maintenance applications.

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