Our client, a prominent provider of store operations technology, aimed to optimise its retail operations and maximise profitability by accurately forecasting sales across various items, departments, and stores. The objective was to develop a robust and reliable forecasting system that considered multiple factors such as promotions, weather conditions, user-specific recommendations, production plans, and other integrated calendar features. This case study highlights the implementation of two AI-powered solution models by SPAR that led to a comprehensive Total Store Operations Experience, enabling increased sales, reduced waste, and optimised labour efficiencies. Through the adoption of two AI-powered solution models, our client successfully developed a robust and reliable forecasting system. This system enabled accurate sales forecasts, actionable insights, and better planning for optimised store operations. Leveraging ensemble learning and advanced gradient boosting techniques, in addition to Azure cloud infrastructure and a neural network architecture, the client achieved a comprehensive Total Store Operations Experience, ultimately leading to increased sales, reduced waste, and optimised labour efficiencies.