Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65094
Volume:
10 |
6 |
Jun 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Information Technology
NBN Sinhgad School of Engineering Pune
Information Technology
NBN Sinhgad School of Engineering Pune
Information Technology
NBN Sinhgad School of Engineering Pune
Information Technology
NBN Sinhgad School of Engineering Pune
AI File Assistant with Smart Chart is an intelligent document management and analysis system designed to help users efficiently organize, analyze, and extract insights from various digital files. The primary objective of this project is to provide a smart platform that enables users to upload documents, retrieve relevant information through natural language queries, generate automated summaries, and visualize extracted data using interactive charts. The system integrates Artificial Intelligence and Natural Language Processing (NLP) techniques to understand document content and deliver accurate responses based on user requests. The Smart Chart module automatically converts structured data into meaningful visual representations, allowing users to identify trends, patterns, and key insights effectively. The application is developed using modern web technologies, including ReactJS for the frontend interface, Python-based AI services for document processing and analysis, and a database system for secure data storage and retrieval. Key features of the system include intelligent file search, AI-powered question answering, document summarization, keyword extraction, data visualization, and interactive chart generation. The implementation of the proposed system reduces manual effort in document analysis, improves information accessibility, and enhances decision-making through automated insights and visual analytics. Experimental evaluation demonstrates improved efficiency in document retrieval, faster information extraction, and enhanced user experience through intelligent assistance and real-time chart generation. The study concludes that AI File Assistant with Smart Chart provides a reliable, scalable, and user-friendly solution for intelligent document management and data-driven analysis in academic, business, and organizational environments.
KEYWORDS
This research work developed an AI-powered document management and analysis system, namely AI File Assistant with Smart Chart, to improve the efficiency of document processing, information retrieval, and data visualization. The proposed system integrates Artificial Intelligence, Natural Language Processing (NLP), semantic search, and smart chart generation techniques to provide intelligent assistance for analyzing and understanding digital documents. The implementation of AI-based document processing enables efficient text extraction, document summarization, keyword identification, and context-aware question answering. The integration of Large Language Models (LLMs) and semantic search mechanisms significantly improves the accuracy and relevance of information retrieval compared to traditional keyword-based search approaches. Furthermore, the Smart Chart module automatically transforms extracted data into interactive visualizations, allowing users to identify trends, patterns, and insights more effectively. Overall, the proposed AI File Assistant with Smart Chart system offers a reliable and intelligent solution for document management, knowledge extraction, and data-driven decision-making. The system can be effectively utilized in academic, business, research, and organizational environments where efficient document analysis and visualization are essential.
[1] Tom M. Mitchell, Machine Learning, McGraw-Hill Education, New York, 1997. [2] Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, MIT Press, Cambridge, Massachusetts, 2016. [3] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” Proceedings of NAACL-HLT, 2019. [4] OpenAI, “GPT Models and Large Language Models Documentation,” Available: https://platform.openai.com/docs [5] Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press, 2008. [6] Daniel Jurafsky and James H. Martin, Speech and Language Processing, Pearson Education, 3rd Edition, 2023. [7] React Documentation, Meta Platforms Inc., Available: https://react.dev/ [8] Python Software Foundation, “Python Documentation,” Available: https://docs.python.org/3/ [9] LangChain Documentation, Available: https://python.langchain.com/docs/ [10] ChromaDB Documentation, Available: https://docs.trychroma.com/
Publication Date
6/18/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
3
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...