Company introduces the Secondmind Decision Engine, a new paradigm for people in business to make better decisions
Secondmind debuted as the decisions company, with the mission of empowering people in business to make better decisions. Backed by more than four years of practical, award-winning artificial intelligence (AI) and machine learning (ML) research, Secondmind is closing the gap between people and AI with easy-to-use and understand ML technology, designed to help businesses better predict, plan, compete and manage risk in an increasingly uncertain world. The company has been developing Secondmind for four years under the name PROWLER.io.
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At the core of this announcement is the introduction of the Secondmind Decision Engine, an ML-powered software-as-a-service platform designed to supercharge decision-making across myriad industries, including those in which visibility is low, data is sparse, and uncertainty is high. The Secondmind Decision Engine leans on direct-to-consumer software design best practices to help non-technical people understand Secondmind predictions, influence outcomes with their domain expertise, and make complex decisions with ease and confidence.
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Currently in limited release, the Secondmind Decision Engine is helping decision-makers in leading transportation and logistics companies, including Kuehne+Nagel and Brambles, to make better demand forecasting, planning and asset allocation decisions within their global supply chain operations. The scalability and versatility of the ML technology in the Secondmind Decision Engine is also helping leading automotive companies like Mazda to significantly reduce engine calibration time in production and accelerate time to market for the company’s industry leading fuel-efficient cars.
The Secondmind Decision Engine is powered by a unique combination of Gaussian Process-based probabilistic modelling and decision-making ML libraries. This technology suite is adept at quantifying uncertainty, identifying operational trade-offs and explaining outcomes using sparse and low volume data, capabilities that meet business decision-making demands where other ML techniques like Deep Learning struggle.
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