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Through a comparative study of simulated data and real world IT service desk logs, this study identifies critical inefficiencies in ticket resolution workflows. The results are that priority-based ticket assignment, automated workload leveling, and predictive analytics yield significant efficiency improvements, reducing average ticket resolution time by up to 40% and improving user satisfaction ratings. Scenario-based performance evaluation also identifies staff shortage, ticket spikes, and lack of automation as primary drivers of backlog accumulation.

The findings of this study have practical implications for IT service management by providing data driven strategies to enhance operational performance. Future upgrades may involve real-time integration with live IT service desks, machine learning driven optimization models, and further automation of routine ticket resolutions.
]]> ]]> It implements a Retrieval-Augmented Generation (RAG) system designed to streamline information access at International Burch University. The system features a multi-layered architecture combining semantic search and generative AI. Vespa.ai serves as the vector database enabling high-performance similarity search, while OpenAI’s GPT models handle natural language understanding and generation. MongoDB is used for session tracking and user state management. Documents in PDF, TXT, and web-based formats are ingested through a pipeline that performs scraping, text extraction, and chunking using LangChain’s RecursiveCharacterTextSplitter. The system is accessible via a modern web interface built with React.js, Vite, and Tailwind CSS, and also exposes a FastAPI-based REST API for backend interaction. Secure access is enforced using JWT-based authentication and authorization. The chatbot component retrieves relevant context through semantic search and maintains coherent multi-turn conversations using conversation history tracking.
The developed system significantly improves the accessibility and efficiency of retrieving university information. It provides fast, context-aware responses to user queries by combining robust retrieval techniques with generative language models.

The web-based interface ensures ease of use for both technical and non-technical users, while the modular backend supports scalability and maintainability. Intelligent query rephrasing and optimized chunk retrieval contribute to improved precision and user satisfaction. Overall, this solution demonstrates how RAG-based systems can transform information access in academic environments by offering a centralized, intelligent platform tailored to users' needs.
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