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Stream/Area:
-
Subject:
-
Paper ID:
IJSREMWP59316
Volume:
9 |
5 |
May 2025
Pages:
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.
The process of creating and managing legal documents is notoriously complex, time-consuming, and prone to human error. Traditional methods rely heavily on manual drafting, which requires specialized legal expertise and often results in inconsistencies, compliance issues, and significant delays. Moreover, the lack of a unified platform for document generation, storage, and collaboration exacerbates these challenges, making legal services inaccessible or prohibitively expensive for many individuals and small businesses. To address these critical gaps, this paper introduces an AI-Assisted Legal Documentation System, a revolutionary platform that leverages cutting-edge technologies such as Natural Language Processing (NLP), Machine Learning (ML), and Blockchain to automate and streamline legal document workflows. The system features an intelligent template repository that dynamically adapts to user inputs, automated compliance checks to ensure adherence to jurisdictional regulations, and a blockchain-based verification system for tamper-proof document integrity. Additional functionalities include real-time collaboration tools, multi-user access controls, and sentiment analysis to refine legal language. By integrating these advanced capabilities, the proposed system reduces document drafting time by up to 80%, minimizes errors, and enhances accessibility to legal services. It represents a significant leap forward in legal tech, offering a scalable, secure, and user-friendly solution for both legal professionals and laypersons.
KEYWORDS
Publication Date
5/15/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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