AI-Powered Public Complaint and Governance Intelligence System

Author

Barre Nikitha, Mr A Rajeshwar


Abstract

 The urban environment complaint management is an essential element of urban governance; however, the current systems may have several issues such as delayed reaction, incorrect classification of the complaint, poor allocation of the officer and no supervision of not resolved complaints. This research proposes the artificial intelligence-based civic complaint management and governance intelligence system called CivicPulse. The proposed system allows citizens to submit complaints along with the complaint description, location and images on the web-site. To classify complaints, natural language processing using DistilBERT and the emergency detection rule-based approach are used. Prioritization of complaints is done by applying XGBoost algorithm that includes complaint category, detection of the emergency situation, images relevancy and complaint proximity as features. The system allows to allocate the officers based on departments, monitor Service Level Agreement, perform automatic escalation, provide evidence of the solution and verify the solution by citizens. All information about the complaint, complaint assignment, complaint status history, evidence and audit information are kept in the centralized MongoDB database. The role-based dashboards are provided for Citizens, Officers, Governance, and Administrators.


Keywords

Public Complaint Management, Artificial Intelligence, DistilBERT, XGBoost, Natural Language Processing, Emergency Detection, Automatic Escalation, Governance Intelligence, Citizen Verification.



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References


  1. Raphael Benadit, S. Thirumoorthy, N. B. Vinoth Kanna, & C. Raj Kannan, "GrievAssist: AI-Powered Public Grievance Redressal System," International Research Journal on Advanced Engineering Hub (IRJAEH), Vol. 04, Issue 04, pp. 2320–2324, 2026.
  2. Anurag Saha et al., "Automated Complaint Classification and Routing Using NLP and Machine Learning," 2024.
  3. Shreyas Patil et al., "CitizenConnect: AI and Blockchain Powered Mobile Grievance Redressal System," 2025.
  4. Yasaswini et al., "Smart City Vision: A Geo-tagged AI System of Automated Road Damage Detection and Civic Workflow Management," 2026.
  5. Rajesh Kumar et al., "Smart Complaint System Using Generative AI," 2025.
  6. Karthik et al., "AI-Powered Petition Tracking and Grievance System," 2025.
  7. Sharma et al., "AI-Based Solution to Enable Ease of Grievance Lodging and Tracking," 2025.
  8. Rahul Sharma et al., "CivicSense: AI-Based Citizen Complaint Analyzer," 2026.
  9. Akhil Reddy et al., "GrievAssist: AI-Powered Public Grievance Redressal System," 2025.
  10. Prakash et al., "Autonomous Grievance Redressal System for E-Governance Using AI and Automation," 2025.
  11. David Thompson et al., "Talk, Snap, Complain: Validation-Aware Multimodal Expert Framework," 2025.
  12. Goldberg, "A Primer on Neural Network Models for Natural Language Processing," Journal of Artificial Intelligence Research, Vol. 57, pp. 345–420, 2016.
  13. Conneau & G. Lample, "Cross-Lingual Language Model Pretraining," Advances in Neural Information Processing Systems, Vol. 32, 2019.
  14. Rogers et al., "A Primer in BERTology: What We Know About How BERT Works," Transactions of the Association for Computational Linguistics, Vol. 8, pp. 842–866, 2020.
  15. R. Quinlan, "Induction of Decision Trees," Machine Learning, Vol. 1, No. 1, pp. 81–106, 1986.
  16. Centralized Public Grievance Redress and Monitoring System (CPGRAMS), "Public Grievance Redress Portal."
  17. in, "Indian Citizen Engagement Platform."

 

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