Published Paper Details:

Efficient urban grievance redressal is crucial for strengthening public trust and ensuring smooth civic operations in rapidly growing cities. However, traditional manual systems remain slow, fragmented, and difficult for citizens to navigate. This work presents an AI- powered Grievance Management Platform that combines Natural Language Processing (NLP) for understanding voice and text complaints, Machine Learning (ML) for automated routing, Computer Vision (CV) for analyzing image-based issues, and Geographic Information Systems (GIS) for accurate location tracking. Citizens can securely submit GPS-enabled images while maintaining privacy, and a public dashboard displays active and resolved complaints to promote transparency. Image-based verification ensures authentic issue resolution. Experimental deployment demonstrates improved speed, accountability, and trust in urban governance, supporting a more data-driven and citizen-centric smart city ecosystem.