Data-Driven Electrification

Machine learning is transforming the electrification industry. Sensai makes it easy by providing a data-first platform to build Scalable AI.

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Our first product is an end-to-end platform to maximize the value of lithium-ion assets using machine learning. The underlying technology enables companies to learn how batteries are degrading in the field using active learning. Our data-centric platform makes it easy for data scientists to create  machine learning models, for edge or cloud deployment, that maximize value at every stage of a battery's lifecycle.

Sensai Lithium provides a full solution for companies to: extend first-life, manage warranty liabilities, calculate residual value, and develop degradation aware controls for second-life or V2G projects.

Sensai Lithium

Helping Electrification companies build and scale  AI solutions

We provide an end-to-end machine learning solution to tackle sparse data challenges in the electrification industry, starting with battery lifecycle management. Sparse data challenges are preventing machine learning from providing massive value to the industry. Sensai enables easier, faster and scalable deployment of AI by developing methods that work with less data, and are going to market with a product that enables electrification OEMs to use active learning to understand the aging of their lithium-ion products, and make data driven decisions on battery life and value. 


What is Data-Centric AI?

Building Data-Centric AI means developing end-to-end machine learning solutions that focus on data, not models. A shortage of Big Data, or difficulty using it, is restricting the majority of companies from creating massive value with AI. By developing methods that overcome sparse data challenges, smartly transfer data and knowledge across projects, and simplify model deployment and management, Sensai Analytics makes it easy for companies to use and scale AI across their electrification projects.

01. Faster model building

We've helped companies reduce the time to build AI solutions for their products and operations by 85%

02. Faster time to value

This leads to an almost 10x improvement in 'time-to-value' for AI projects

03. Increased productivity

And massively increases the productivity of in-house data science teams - a group currently over-stretched by the demand for solutions but shortage of talent available



Muhammad Rizwan

ML Research Scientist

Georgia Tech Ph.D.

Ian Mathews

CEO | Co-Founder

Erin Looney




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Prof. Bolun Xu

Columbia University

Bruce Lawler

Director | MIT Machine Intelligence for Manufacturing & Operations

​Paul King

Battery Scientist

Trinity College Dublin Ph.D.



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Winner of Schneider Electric's Bold Ideas Challenge 2020

CTO at Fortune 500 OEM

“Sensai Analytics ability to machine learn from small data sets provides insight and foresight into new products, accelerating the discovery of new growth opportunities for manufacturers”


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