An Advanced AI-Driven Complaint Management System for RailMadad
Bibliographic record
Abstract
With the growing number of passengers using Indian Railways, handling customer complaints efficiently has become a critical challenge. RailMadad, the official grievance redressal platform of Indian Railways, currently manages thousands of complaints daily, but categorizing, prioritizing, and routing these complaints manually often leads to delays. This study presents an AI-driven complaint management system designed to enhance the efficiency of RailMadad by leveraging Natural Language Processing (NLP) and Machine Learning (ML) techniques. The proposed system automatically classifies complaints based on predefined categories, assigns priority levels, and routes them to the relevant departments in real time. The system employs advanced algorithms such as BERT and RoBERTa for intent classification and sentiment analysis to assess the urgency of complaints, and the model is trained using publicly accessible datasets and complaint records. The system is very scalable and flexible for widespread use in Indian Railways, as evidenced by the experimental results, which show notable increases in customer satisfaction and complaint response time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".