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Assess thousands of candidate material combinations and optimise in terms of cost-performance-safety-sustainability tradeoffs for your application.
Use our machine learnt models, trained on parameters collected from your manufacturing processes, to identify EVERY outlier in a production batch, instead of using sampling techniques.
Choice Of Component Materials
Explore the cost-performance-safety-sustainability tradeoffs for thousands of combinations of component materials at least 90% faster than experiments
Shorten your QA/QC Cycles
Slash your QA/QC testing times by at least 95% by estimating the distributions of performance and safety for a batch and identify the defective pieces after production
Save up to 80% of the cost to quickly evaluate battery component designs across multiple configurations
Calculate the maximum cell temperature at different charge/discharge rates and other operating conditions
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Battery Component OEMs
Scale Up Your Material
Cut up to 80% of the cost and 90% of the time taken by experiments to seamlessly assess the cost-performance-safety-sustainability tradeoffs of your material in different commercial configurations like prismatic, 18650 and 21700 cells
Performance & Safety Distributions
Understand the possible deviations in the performance and safety pertaining to different design parameter uncertainties at a 95% confidence level