CREATING VALUE WITH REAL DATA
AI for Electrification, at Scale
Industry is rapidly digitizing and electrifying. AI will play a key role is managing assets but there is currently a shortage of data to train algorithms on, and people to develop solutions. At Sensai Analytics, we are tackling this challenge by developing products that enable companies with limited machine learning expertise to build scalable and robust AI.
We’re developing new AI methods adapted to the unique constraints of industrial AI problems - our solutions overcome the small data problem, and enable companies to rapidly deploy AI at scale.
We enable companies to train AI models that mimic behaviors from past projects, reducing the required data and training time for each new model deployment.
A lack of quality usable data, and a shortage of machine learning talent leads to challenges in scaling AI solutions across business units and products.
We are working with industry-leading partners who are deploying Scalable AI to proactively manage the health of their battery assets.
01 / ACCURATE
We smartly search through all available data to find the most relevant information for your models (including internal company data and external sources).
02 / FAST
Your models are trained and put into production in hours, not months, using our automated machine learning tools.
03 / SCALABLE
Our methods address the small data problem and can be trained by learning from your prior projects - enabling rapid scaling across all of your projects.
A major challenge for companies building battery powered products is a lack of data on their long term value. How can they make decisions on control policies, maintenance, and re-use, without knowing how these decisions will impact a battery's useful life?
A predictive analytics package (API or UI) to manage the long-term value of your lithium-ion assets, built upon a world-leading collection of data sets and machine learning models for lithium-ion prognostics (covering multiple cell chemistries and use cases). Our pre-trained prognostics models can be used as they are, or fine-tuned with just small amounts your data, to create highly accurate and valuable solutions that provide value in days, not months.