Grievassist: AI-Powered Public Grievance Redressal System

Abstract

Efficient grievance redressal plays a vital role in improving transparency and citizen satisfaction in modern digital governance systems. However, existing public grievance platforms largely depend on manual processing, resulting in delayed responses, improper complaint routing, and lack of prioritization. This paper proposes GrievAssist, an Artificial Intelligence (AI) driven framework designed to automate grievance handling using Natural Language Processing (NLP) and machine learning techniques. The proposed system processes unstructured citizen complaints submitted through a web-based interface and performs automated semantic classification, urgency detection, and intelligent prioritization of reported issues. The framework integrates automated departmental routing, real-time notification services, and role-based administrative dashboards to enable efficient interaction between citizens and government authorities. A scalable AI-supported architecture is employed to ensure reliable data management, continuous system learning, and improved adaptability for handling large volumes of complaints. By analyzing contextual patterns within complaint descriptions, the system assists in identifying relevant service departments and assigning appropriate urgency levels for faster response. Experimental evaluation demonstrates that the proposed approach achieves high classification performance while reducing complaint processing delays compared to conventional manual systems. The results highlight the potential of AI-enabled grievance management platforms in enhancing operational efficiency and supporting transparent, citizen-centric governance in modern smart city environments.