Neuton.AI – a no-code Tiny ML platform.

Neuton.AI was designed to help users automatically build extremely Tiny ML models of optimal 
size and accuracy, and embed them into any microcontroller, even with 8-bit precision.

Neuton's models are extremely compact.
Up to 1,000 times: 

• smaller
• have fewer coefficients
• demonstrate faster inference

in comparison to TensorFlow and other frameworks. 

Evaluate benchmarks https://neuton.ai/frmwrk#benchmarks 
Our team of data scientists has created a unique neural network framework 
Neuton, which is the “brain” of our platform. The framework is based on the neuron-by-neuron model creation principle which allows users to

• automatically create models of optimal size and accuracy
• avoid any manual search for neural network parameters
• exclude the need for model compression, quantization, and pruning after its creation
• build incredibly compact models, ready for embedding into microcontrollers

Neuton models maintain all original characteristics, without any reduction of accuracy. Neuton 
does not reduce the model size after its creation.
Read about our framework: https://neuton.ai/neural-network   

Use our service absolutely free of charge

Build your first extremely tiny ML model with Neuton to make your edge device intelligent:  
https://login.neuton.ai/

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Software

Neuton.AI

Neuton.AI's headquarters in San Jose, CA, United States

This department focuses on identifying new business opportunities and fostering client relationships.

Business Development

The Software Development Team at Neuton.AI is responsible for designing, building, and maintaining the no-code Tiny ML platform. They work collaboratively to develop the unique neural network framework that underpins the platform, ensuring it delivers optimal model size and accuracy for embedding in microcontrollers. This team combines expertise in software engineering, machine learning, and applied science to create intuitive user experiences and maintain high performance benchmarks, enabling users to seamlessly generate compact and efficient ML models.

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