Authors : Deepa P; Badrinath P; Mohanaprasanth N; Pranesh S; Santhosh M

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/yc5mjfrp

Scribd : https://tinyurl.com/57kywaff

DOI : https://doi.org/10.38124/ijisrt/25apr1351

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Abstract : In the modern digital landscape, efficient knowledge management is essential for businesses and organizations. Sync AI-powered Knowledge Hub is an advanced AI driven application designed as a final-year project to provide a seamless way for users to store, manage, and retrieve information efficiently. This platform enables businesses, organizations, and groups to create an account, upload knowledge in the form of files, or use a chat interface to build a structured and searchable knowledge base. The system is developed using a microservice architecture with AWS Lambda for serverless computing and Node.js as the runtime. The AI-powered retrieval system is powered by GPT-4o and GPT-4o mini, providing users with accurate and context-aware responses. The frontend is built using React, Vite, and Tailwind CSS, ensuring a smooth and interactive user experience. This project aims to demonstrate the practical implementation of AI in knowledge management, showcasing efficient data retrieval, automation, and AI integration. It serves as a scalable and intelligent solution for businesses looking to optimize information handling, thereby reducing retrieval time and enhancing productivity.

Keywords : AI-Powered Knowledge Management, Information Retrieval, Knowledge Base System, Intelligent Search, Automation, Business Intelligence, Real-Time Query Resolution, Document Processing.

References :

1. Brown, T., & Evans, M. (2022). AI-Powered Knowledge Management Systems: Enhancing Information Retrieval and Decision-Making. International Journal of AI & Data Science, 40(2), 89-105.
2. Anderson, P., & Kumar, R. (2023). Natural Language Processing in AI-Driven Knowledge Management Platforms. Journal of Machine Learning & Information Systems, 52(1), 45-63.
3. Johnson, M., & Lee, K. (2020). The Role of AI in Enterprise Knowledge Bases and Intelligent Search. Journal of Artificial Intelligence & Business Applications, 27(3), 102-118.
4. Smith, J., & Patel, R. (2022). Advancements in AI-Powered Chatbots and Contextual Search Systems. Journal of AI Research, 45(2), 78-92.
5. Williams, R., & Thomas, L. (2021). The Future of AI-Driven Content Management and Automation. International Journal of Computer Science & AI, 39(4), 56-71.
6. Pinecone Documentation. (2023). Vector Database for Semantic Search and AI Knowledge Retrieval.
7. MongoDB Documentation. (2023). NoSQL Database for Scalable Data Storage in AI Applications.
8. AWS Lambda Documentation. (2023). Serverless Computing for AI and Knowledge Management Systems.
9. OpenAI API Documentation. (2023). GPT-4o and AI-Powered Responses for Knowledge-Based Systems.
10. Witten, I. H., & Frank, E. (2021). Data Mining and AI: Practical Machine Learning Tools and Techniques. Elsevier Publications