Data-Driven Electrification
Machine learning is transforming the electrification industry. Sensai makes it easy by providing a data-first platform to build Scalable AI.
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
Team
Muhammad Rizwan
ML Research Scientist
Georgia Tech Ph.D.
Ian Mathews
CEO | Co-Founder
Erin Looney
Co-Founder
MIT Ph.D.
Advisors
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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