Food Safety Compliance Monitoring With IoT Sensors and AI Reports

Restaurants, hotels, and food manufacturers live or die by safety compliance, but monitoring still depends on staff manually logging fridge temperatures and cleaning checks. Those records are error-prone, easy to backfill, and painful to compile when an inspection arrives, while regulators only discover problems after violations have already occurred. The intern builds an IoT-plus-AI solution that automates the evidence trail. Simulated or real IoT sensors stream temperature and humidity readings from storage and preparation areas into a Python backend, with MongoDB holding the time-series readings and site configurations. A generative AI layer converts raw telemetry into readable compliance reports and actionable alerts, flagging a warming freezer before stock is lost or drafting exactly the documentation an inspector will request. Secure user management separates roles for staff, managers, and auditors, and every report exports as a shareable document. The project teaches sensor data pipeline design, threshold and trend-based alerting, and one of the most useful generative AI patterns in industry: turning machine data into human-ready documentation for a regulated business.