During lunch hours, canteens face overcrowding, long waiting times, and inefficient table management, leading to frustration for students and staff. QuickEat solves this by automating food ordering and AI-powered table allotment for a seamless dining experience.

describe what can people use it for, or how it makes existing tasks easier/safer e.t.c (markdown supported)

The problem it solves

tell us about any specific bug or hurdle you ran into while building this project. how did you get over it? (markdown supported)

Challenges we ran into

InnovoCon 2025

React

Flask

Redux

AWS Lambda

Python

DynamoDB

MERN

AWS rest api

AWS WebSockets api

QuickEat

✅ AI-Powered Table & Queue Management – Uses AI/ML for smart table allotment and queue handling.
✅ Real-Time System – AWS WebSockets API ensures instant updates for menu, seating, and queue status.
✅ Cloud & Serverless Architecture – AWS Lambda, GCP, DynamoDB enable scalability and automation.
✅ Seamless User Experience – React & Redux for a responsive interface; profile-based table authentication.
✅ Scalable & Impactful – Can expand to corporate cafeterias, food courts, and other institutions.

QuickEat embodies HackNEX innovation by solving real-world problems with AI, cloud, and automation. 🚀

HackNex

System 404

QuickEat

Eat Fast, Eat Eas

QuickEat

Eat Fast, Eat Eas

The problem QuickEat solves

HackNex

Discussion

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