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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".