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Foodnest
- Program: Center for Leadership Education
- Course: EN.663.657 Innovation & Design 2
- Year: 2026
Project Description:
Foodnest is a data-driven decision support system designed specifically for small urban enterprises, such as neighborhood convenience stores and cafes. Currently, many independent retailers rely on intuition for critical operations, often overlooking how external factors like weather fluctuations or neighborhood events impact their performance. Foodnest fills this gap by integrating internal Point-of-Sale (POS) data with hyper-local environmental signals, including weather forecasts, 311 service requests, and local crime data.
Our platform provides a real-time KPI dashboard and a unique “Store Health Score” that translates complex data into clear, actionable recommendations. Whether it’s optimizing inventory based on rain forecasts or adjusting staffing for local events, Foodnest empowers owners with enterprise-grade insights. With a straightforward subscription model of $49 per month, we aim to enhance the survival and profitability of neighborhood businesses by providing affordable, context-aware intelligence in a dynamic urban environment.
Project Photo:
The Foodnest team (Weiwen Liu, Chengye Liu, Fuchun Zhan) conducts a live online demo of their “Profit Driver Diagnostic” dashboard, showcasing how their data-driven platform analyzes the operational health and performance of a local Baltimore restaurant.
Project Poster
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Project Poster Summary:
Foodnest is a data-driven decision support SaaS platform designed to empower independent neighborhood retailers—such as cafes and convenience stores—who traditionally rely on intuition rather than analytics. Offered at an accessible $49 per month, the system seamlessly integrates a business’s internal POS data with hyper-local external stressors, including weather forecasts, 311 service requests, and neighborhood crime maps. By processing these variables, Foodnest generates a real-time “Store Health Score” alongside rule-based, actionable recommendations, such as automatically advising owners to adjust perishable stock ahead of heavy rain. Ultimately, the platform transforms complex, fragmented data into practical operational insights, helping small businesses optimize daily management, mitigate external risks, and achieve sustainable growth as the team advances toward its MVP launch and pilot testing in Baltimore.