AI-Powered Complaint Analyzer (AIPCA) Using Unified Framework Distil-BERT for Cross-Domain Grievance Management

Abstract

The AIPCA is the AI-powered Complaint Analyzer that provides a radical approach to the grievance management of the modern institution. The conventional systems lack transparency, delays averaging of 72 hours across the system and high rates of mis-routing because of the use of human intervention. AIPCA focuses on these problems through a smart and web-based platform that has a fine-tuned Distil-BERT model as its core. Multi-Task Learning (MTL) is used to optimize the model to deal with Category Classification (departmental routing) and Urgency Detection (priority assessment). This method has a competitive categorization accuracy (over 92) and has ultra-low computational latency and memory consumption, which is obtained as a result of Knowledge Distillation by a bigger transformer model. The system is designed on a multi-level enterprise system that consists of a secure auditable data layer in order to monitor transparent performances. With priority assessment as an AI-driven system and automated routing, AIPCA will help organizations reduce the average administrative response time (approximately, 72 hours to approximately, 12 hours) and address key issues much faster, thus enhancing operational effectiveness and user experience significantly. The cross-domain functionality of the framework is provided by systematic Domain Adaptation (DA).

Introduction

Effective grievance-redressal systems are essential indicators of institutional health, efficiency, and public trust. Traditional complaint-handling processes—largely manual or based on simple rule-based automation—suffer from slow processing times, inconsistent classification, lack of transparency, and poor strategic insight. These inefficiencies often lead to high Mean Time to Resolve (MTTR), administrative delays, user dissatisfaction, and erosion of trust.

To address these challenges, the paper introduces AIPCA (AI-Powered Complaint Analyzer)—a scalable, web-based, end-to-end automated grievance-management system built using Artificial Intelligence and Natural Language Processing. AIPCA analyzes, categorizes, and prioritizes large volumes of unstructured complaint text. Its design is centered on three key innovations:

1. Distil-BERT for efficient real-time inference, providing low latency with near-BERT-level semantic performance.

2. Domain Adaptation (DA) to ensure accuracy across diverse sectors such as education, healthcare, and corporate environments.

3. Multi-Task Learning (MTL) to simultaneously perform complaint category classification and urgency detection.

The paper’s contributions include justification for using Distil-BERT, new generalization strategies using DA and MTL, a secure multi-tier enterprise architecture, and evidence of substantial operational gains—potentially reducing response time by 85%.

Methodologically, the study compares classical text-classification models with modern transformer-based approaches, emphasizing the superior contextual understanding of transformers. Distil-BERT is validated as the optimal balance between accuracy and computational efficiency.

AIPCA’s data pipeline includes diverse multilingual complaint datasets, structured preprocessing, and rigorous annotation for both categories and urgency levels. The system architecture comprises:

• React-based client layer for submission, tracking, and administrative dashboards

• Python/Flask application layer for workflow control, RBAC, and API management

• AI service layer running the fine-tuned Distil-BERT model

• MongoDB data layer for secure storage and auditing, including MTTR computation

The operational workflow begins with complaint submission, followed by preprocessing, AI-based classification and prioritization, automatic routing to the correct department, and final resolution logging. Model version tracking and support for continuous retraining ensure long-term adaptability and resilience.

References

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Copyright

Copyright © 2025 Sambhav Shastri, Aditya Bhide, Sheeba khan, Tamish Ashtankar, Tajinder Singh Dhillon, Smith Awsarmol. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.