Inspiration

Long waiting times in hospitals, banks, and service centers often cause frustration and inefficient service management. We were inspired to create a smart solution that makes queue management easier, faster, and more convenient.

What it does

QueueLess AI is a smart queue management system that allows users to book digital tokens, track their queue position, and receive real-time updates. It estimates waiting times using queue information and service history, helping users plan their time more effectively. An admin dashboard allows staff to manage tokens, monitor service counters, and analyze queue performance.

How we built it

We designed the system with a React.js frontend, a Spring Boot backend, and a MySQL database. A Python-based prediction module can analyze queue length and historical service times to estimate waiting periods. The frontend communicates with the backend through APIs to update queue information.

Challenges we faced

The main challenges include estimating waiting times accurately, keeping queue information updated, and coordinating communication between the frontend, backend, and prediction module. These challenges can be addressed through testing, simulated queue data, and continuous improvements.

What we learned

Through this project, we explored full-stack development, API integration, database management, and the application of AI techniques to real-world problems.

Future scope

Future improvements could include WhatsApp or SMS notifications, QR-code-based token booking, multilingual support, and deployment in hospitals, colleges, banks, and other public service centers.