SMART PUBLIC GRIEVANCE REDRESSAL SYSTEM

Inspiration: Public grievances are often delayed due to incorrect department assignment, duplicate complaints, lack of prioritization, and limited transparency in the resolution process. We were inspired to develop a smart platform that uses AI, geospatial technology, and automation to make grievance reporting more transparent, efficient, and accessible while helping authorities resolve issues faster.

What it does: Smart Public Grievance Redressal System is an AI-enabled platform that allows citizens to submit, track, and manage public grievances efficiently. Citizens can submit complaints with descriptions, images, and location details. The system uses AI/NLP to classify complaints, identify duplicate complaints, predict priority levels, and assist in assigning complaints to the appropriate department. Location-based services help identify administrative details such as ward, taluk, city, and district. Officers can manage assigned complaints, update their status, and provide resolution details, while administrators can monitor complaints through dashboards and analytics.

How we built it: We developed the system using a modular full-stack architecture with separate interfaces for citizens, government officers, and administrators. The frontend is developed using React.js, while the backend is built using FastAPI and REST APIs. PostgreSQL is used for database management. Python-based NLP and machine learning techniques are used for complaint classification, duplicate detection, and priority prediction. Map and GIS technologies are integrated to capture location coordinates and identify relevant administrative information. JWT-based authentication and role-based access control are implemented to ensure secure access for different types of users.

Challenges we ran into: One of the major challenges was finding reliable and up-to-date administrative boundary data for Tamil Nadu, especially at the ward level. We also faced challenges in accurately mapping latitude and longitude coordinates to the corresponding ward, taluk, city, and district. Integrating AI models with the backend while maintaining good performance, implementing secure authentication, handling duplicate complaints, and designing a scalable architecture were also key challenges during development.

Accomplishments that we're proud of: We successfully designed an end-to-end AI-assisted public grievance management system that combines full-stack development, artificial intelligence, machine learning, and GIS technology. We implemented separate workflows for citizens, officers, and administrators, along with automated complaint classification, duplicate detection, priority prediction, location-based identification, complaint tracking, and analytics. The system provides a strong foundation for improving the efficiency and transparency of public grievance management.

What we learned: Through this project, we learned how to convert a real-world social problem into a complete software solution. We gained practical experience in full-stack development, REST API development, database management, authentication, NLP, machine learning, GIS and geospatial processing, and system integration. We also learned the importance of data quality, security, scalability, usability, and effective integration between different technologies when developing a real-world application.

What's next for Smart Public Grievance Redressal System: Our future goal is to make the system more intelligent, scalable, and accessible. We plan to add multilingual complaint submission, including Tamil language support, voice-based complaint registration, improved severity and urgency prediction, advanced duplicate and spam detection, real-time SMS/email/push notifications, and predictive analytics for identifying recurring public issues. We also plan to expand the GIS functionality across Tamil Nadu, integrate the platform with relevant government departments and existing grievance systems, and deploy the solution as a scalable cloud-based platform capable of supporting large numbers of citizens and government officials.