Shubh Yatra

An AI Enhanced Version of the Rail Madad

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Inspiration

The current manual complaint registration process for train passengers often leads to delays and inefficiencies in addressing issues during journeys. This inspired us to create an AI-driven solution to streamline complaint management, making it more efficient, user-friendly, and effective in resolving passenger concerns.

What it does

Shubh Yatra leverages AI to automate and enhance the complaint resolution process. It handles complaints containing photos, videos, or audio by using advanced tools such as image and video recognition to categorize and prioritize issues. Optical Character Recognition (OCR) extracts text from visual content, and metadata analysis, including timestamps and location, adds context for quicker resolutions. AI chatbots acknowledge complaints instantly and gather additional details while smart routing directs issues to the right teams. Machine learning identifies recurring issues for proactive maintenance, and sentiment analysis evaluates feedback to identify areas of improvement.

How we built it

We combined various AI technologies to create a robust system:

AI-powered image and video recognition to understand the nature and urgency of complaints. OCR tools to extract actionable insights from visual data. Metadata analysis for enhanced context and prioritization. AI chatbots for real-time complaint acknowledgment and interaction. Machine learning to predict recurring problems and suggest solutions. Dynamic resource allocation systems for resolving critical issues swiftly. Key performance metrics like detection accuracy and resolution speed guide system optimization. Deployed these in both App and Web platform for App using Flutter, Dart, Firebase and for the Web use of the ReactJS, Node JS and MongoDB and For the Deployment use of Docker, Vercel, Render

Challenges we ran into

Designing a system capable of handling diverse complaint formats like images, videos, and text was technically challenging. Ensuring the accuracy of AI models for categorization and urgency assessment required extensive testing and fine-tuning. Integrating multiple technologies into a seamless and scalable system while maintaining user-friendliness posed architectural challenges

Accomplishments that we're proud of

Successfully implemented a complaint management system with real-time acknowledgment and prioritization. Achieved high accuracy in detecting and categorizing complaints using AI tools. Enhanced user satisfaction by significantly reducing resolution times. Developed AI-assisted training tools for staff, boosting their efficiency in complaint resolution.

What we learned

The importance of integrating multiple AI technologies to address complex real-world problems. How metadata and sentiment analysis can provide deep insights into user needs and system performance. The value of proactive maintenance and dynamic resource allocation in improving service quality.

What's next for Shubh Yatra

Enhancing machine learning algorithms to predict and prevent a wider range of recurring issues. Scaling the solution to address complaints beyond train journeys, covering other public transportation systems.

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