LegalHelp: Intelligent Legal Document Analysis using NLP
Nehali Mhatre
Assistant Professor
Computer department Universal College of Engineering
Kaman, Vasai
nehalipatil9@gmail.com
Aryan Patil
Department of Computer
Engineering Universal College of Engineering
Kaman, Vasai aryanis7399@gmail.com
Aadya J Samant
Department of Computer Engineering Universal College of Engineering
Kaman, Vasai
aadyasamant14@gmail.com
Janhavi Parab
Department of Computer Engineering Universal College
of Engineering
Kaman, Vasai
janhavi6005@gmail.com
Mitali Tangadi
Department of Computer Engineering Universal College
of Engineering
Kaman, Vasai mitalitangadi03@gmail.com
Abstract— The legal domain is heavily dependent on large volumes of complex textual data, where the use of specialized language and intricate syntax creates significant accessibility challenges for non-expert users [1][2]. Traditional manual analysis of such documents is time-consuming and prone to human error, motivating the adoption of data-driven approaches in the emerging neural era of Legal Natural Language Processing (NLP) [3].
This paper presents Legal Help, a web-based platform designed to simplify legal document understanding through automated analysis, summarization, and conversational interaction. The system addresses the long document problem by applying semantic segmentation to extract key information from unstructured legal texts [10].
To enhance reliability, the platform incorporates Retrieval-Augmented Generation (RAG), enabling dynamic retrieval of relevant legal context and reducing hallucination in generated responses [2]. Additionally, a Video KYC (VKYC) module is integrated to ensure secure user authentication and data privacy in compliance with legal standards [4].
The system is developed using a modular full-stack architecture, supporting scalable deployment and efficient processing of user queries and documents [10]. Experimental evaluation demonstrates that the proposed approach achieves moderate-to-high accuracy in clause identification, summarization, and query response, outperforming traditional rule-based methods in contextual understanding and semantic search [5].
The results indicate that combining transformer-based NLP techniques with retrieval mechanisms and secure infrastructure can significantly improve legal accessibility and user interaction.
Keywords: LegalTech, Natural Language Processing (NLP), Al Chatbot, Video KYC, Retrieval- Augmented Generation (RAG),
