90% of retail sales occur in physical stores. In these physical stores 40% of shoppers leave the store based on the wait time. Autonomous stores can remove customer waiting time by providing a receipt without the need for scanning the items. Prior approaches use computer vision only, combine computer vision with weight sensors, or combine computer vision with sensors and human product recognition. These approaches, in general, suffer from low accuracy, up to hour long delays for receipt generation, or do not scale to store level deployments due to computation requirements and real-world multiple shopper scenarios. We present ISACS, which combines a physical store model (e.g. customers, shelves, and item interactions), multi-human 3D pose estimation, and live inventory monitoring to provide an accurate matching of multiple people to multiple products. ISACS utilizes only shelf weight sensors and does not require visual inventory monitoring which drastically reduces the computational requirements and thus is scalable to a store-level deployment. In addition, ISACS generates an instant receipt by not requiring human intervention during receipt generation. To fully evaluate the ISACS, we deployed and evaluated our approach in an operating convenience store covering 800 square feet with 1653 distinct products, and more than 20,000 items. Over the course of 13 months of operation, ISACS achieved a receipt daily accuracy of up to 96.4%. Which translates to a 3.5x reduction in error compared to self-checkout stations.