The future of AI/ML is Tiny

Advances in AI deployments mean that even the smallest, cost-sensitive devices can be made smarter and more capable. We make development quicker and easier for product manufacturers implementing DSP and ML capabilities on-device reducing the carbon  overall footprint. 

Vision, such as object classification; Voice, including keyword detection; and Vibration, for example sensor fusion are use cases that align to various distinct areas for endpoint AI.


Intelligent Vision

Identify and detect hundreds of multiple objects, including people, activities, animals, plants and places with strict localization, accuracy and semantic labels.


Advanced Voice

Identify speech commands by recognizing keywords. Generate audio using microphones on the edge. Generate music based on the mood of the listener.


Language Processing

Categorize free text into predefined groups for abusive content moderation, tone detection and more. Answer questions based on the content of a given passage of text.


Robotic Automation

Our robotics systems are at the heart of physical automation, computer vision, automotive engineering, and act as a vector for companies to deploy solutions for the future.


IoT Analytics

We enables companies with IoT requirements to rapidly build smart AI sensing algorithms with no data science required. Our compact, energy efficient models can be executed in as little as KBs of memory.


Predictive Modeling

Our industry-agnostic AI solutions enable anomaly detection and signal classification to monitor vibrations and other signal types and predict system failures.

Low Carbon Footprint Solutions

We help develop custom proprietary ML/AI models with optimized results, leveraging our distinctive knowledge of various domains with cutting-edge Machine-Learning algorithms. Our solutions have the industry's lowest carbon footprint both in training and inference.

High Performance

Smallest Devices

Optimized AI/ML workloads processed on microcontrollers no bigger than a pack of potatoes consuming only milliwatts of power.

Envirounmental friendly

ESG Friendly AI

We focus on delivering energy efficient ESG friendly AI technology that can be powered by renewable sources of energy like solar.

Run AI locally on the Edge

No data center or cloud connectivity is required for running these deep learning models. These are so efficient that they can easily run locally, even on battery-operated everyday products.

Intelligence at the Edge reduces the costs associated with running a cloud infrastructure.

Edge computing improves the computing experience by reducing latency and cost while improving resiliency and quality of service.

Scale massively with quick deployment strategies across the spectrum. 

Our recent advances in AI/ML enable more processing, and pre-processing, to be done on-device than ever before.

Reduce latency along with power and efficiency across the curve.

As the hardware gets ever-more powerful and efficient, it will drive even higher performance, which enables more on-device compute power 

Ensure a cloud-native experience with secure edge algorithms.

We enable increased safety and security, while reducing risk of data exposure, along with power savings.


Enabling Intelligence on the Smallest Devices 

Find out how our new algorithms and solutions are enhancing on-device machine learning capabilities to help drive innovation and open up new business opportunities.

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