Autonomous Grievance Redressal System for Government Services

This research focuses on developing an AI-based autonomous grievance redressal system for government services to address and resolve public issues promptly without any delays. The existing systems for grievance redressal often face several problems like manual prioritization, lack of prioritization of high-priority issues, model not being scalable for all forms of devices, inaccurate categorization of problems based on domains like water, electricity, food, earthquake, etc., and slow response times especially when visual evidence of the affected area is involved. To address these problems this research paper proposes a unique AI-driven grievance redressal model that integrates multimodal inputs, combining text and image data, to overcome these challenges. This model is obtained after a deep analysis of all the existing based on severity. In addition to this, domain-based categorization like water, electricity, food quality, etc., is achieved using Natural Language Processing and Computer Vision techniques. A novel feature of this model is the integration of the option to upload images of the affected areas and process them to enhance ranking based on severity. It also integrates multi-language support to enable wide accessibility. This is achieved by leveraging computer vision techniques and pre-trained models to analyse images based on visual patterns. Overall, this research paper proposes a novel solution to address the existing issues in grievance redressal systems.