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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM57117

AI Research Intelligence Platform: A Multi-Modal Document Analysis and Code Intelligence System

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM57117

Volume:

10 |

2 |

Feb 2026

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM57117

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.

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Authors

RDP

R Dada Peer1

MDK

M Dharani Kumar2

BVS

B Varsha Sree3

VS

V Soumya4

DM

D Manasa5

KM

K Meghana6

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Abstract

This paper presents the AI Research Intelligence Platform, a comprehensive web-based application built on Streamlit that integrates natural language processing, semantic vector search, and code intelligence into a unified research assistance ecosystem. The platform supports multi-format document ingestion (PDF, DOCX, TXT), semantic indexing via ChromaDB using the SentenceTransformer all-MiniLM-L6-v2 model, conversational question-answering, extractive summarization, auto-generated multiple-choice quizzes, cross-document comparison, and multi-language code analysis with real-time execution and quality scoring. The system operates entirely on local CPU resources without external API dependencies, ensuring data privacy and reproducibility. Experimental results demonstrate 82% Q&A accuracy, ROUGE-1 of 0.48 for summarization, and 0.79 Spearman correlation for code quality scoring, confirming the system as an effective and cost-efficient research productivity tool.

KEYWORDS

Semantic Search ChromaDB SentenceTransformer Streamlit Document Intelligence Code Analysis NLP.

Cite This Article

R Dada Peer1, M Dharani Kumar2, B Varsha Sree3, V Soumya4, D Manasa5, K Meghana6, "AI Research Intelligence Platform: A Multi-Modal Document Analysis and Code Intelligence System," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 2, Feb 2026. https://doi.org/10.55041/IJSREM57117

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Publication Date

1/2/2026

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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