Fusemachines to built a system that could predict demand at the ingredient level relative to each market location to determine which meals users would be most likely to buy and where the most effective place would be to have their trucks.
Correlated the effects of weather, location, ingredient costs, days of the week, and holidays had on customers spending. Included marketing spend and conversation analytics into models to help improve demand predictions.
The system reduced the burden on their procurement team as they had better insight into which ingredients and quantity needed.
The system was able to reduce waste across all perishable items by more than 40%, while improving prediction demand at a rate 240% more accurate then previous models. Fusemachines built machine learning models using advanced machine learning and Deep Neural Networks.
A web platform that focuses on providing leadership coaching to managers by using feedback from employees regardless of where they’re located.
Fusemachines made it easy for them to scale their team on demand.
AI to be reproducible, accountable, collaborative, and continuously delivered
Fusemachines saved Dotscience $48,000 annually per engineer, enabling Dotscience to confidently take their product to market.