A new predictive meal planning tool developed by MIT interns, Chase Vanias and Andrew Lin, is helping TASK strengthen its ability to deliver reliable, efficient community meals while reducing food waste. Originating from an idea first identified during TASK’s Hack-A-Thon, the solution uses data to better anticipate daily meal demand across community meal sites, the Escher Street location, and the food truck program, supporting smarter planning and smoother operations.

The model was developed in collaboration with TASK’s Chief Strategy & Innovation Officer, Frank Masters, and refined through conversations with key staff, including Chef Adam Livow, COO Paul Jensen, and Food Truck Coordinator Max Gatto. This collaboration ensured the tool aligned with real-world kitchen workflows and on-the-ground operational needs, not just theoretical data models.

“Designed with usability as a priority, the project intentionally balanced data-driven insights with the realities of running a large-scale community meal program. Rather than adding technical complexity, the focus was on building trust in the tool and making it practical for daily decision-making,” says Masters.

The predictive meal model now provides TASK with a flexible framework that can continue to evolve, supporting more efficient meal preparation, minimizing waste, and helping ensure consistent access to meals for the community.