The Complaint Grievance Redressal System (CGRS) was developed to improve the traditional method of managing student complaints by incorporating artificial intelligence for smarter complaint handling. In many institutions, the existing complaint process is manual, time-consuming, and difficult to track. To overcome these issues, the proposed system introduces semantic duplicate detection, automatic complaint routing, and priority-based escalation. The backend runs on Node.js and Express, with MongoDB handling data storage. When a student submits a complaint, the system uses Google Gemini 1.5 Flash to compare it against existing complaints in the database. If the similarity score hits 80 or above, the new submission is logged as a duplicate and the original complaint\\\'s duplicate count goes up by one. Once that count reaches three, the complaint is automatically bumped to high priority so it gets attention faster. Routing is automatic too. Based on the department and complaint type the student selects, the complaint goes straight to the right person either the Head of Department or the Training and Placement Officer. Students also get email notifications whenever their complaint status changes or gets resolved, so they\\\'re not left guessing. Access is split across four roles: Student, HOD, TPO, and Admin. Authentication uses JWT tokens, and each role only sees and does what it\\\'s supposed to nothing more. This paper presents the overall system architecture, artificial intelligence integration methodology, duplicate detection algorithm, and system performance evaluation.
Reddy et al. (Thu,) studied this question.