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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM65268

JURISAI: SMART LEGAL ADVISOR AND DOCUMENT ASSISTANT

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM65268

Volume:

10 |

6 |

Jun 2026

Pages:

1-5

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM65268

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

RD

Riya Deshmukh

Computer Engineering

Sinhgad College Of Engineering

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SD

Samruddhi Deokar

Computer Engineering

Sinhgad College Of Engineering

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MD

Manas Dhondge

Computer Engineering

Sinhgad College Of Engineering

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SD

Shweta Desai

Computer Engineering

Sinhgad College Of Engineering

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Mentors

PSB

Prof. S.P. Bholane

Computer Engineering

Sinhgad College Of Engineering

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Abstract

In today’s digital era, the legal sector remains difficult for ordinary people because of complex legal terminology, lack of awareness, and expensive consultation processes. Individuals and small businesses often face challenges in understanding legal contracts, preparing agreements, and validating legal clauses. Traditional legal consultation methods are often time-consuming and costly, making legal assistance inaccessible for many users. This paper presents JurisAI, an AI-powered legal assistance platform developed using Artificial Intelligence (AI), Natural Language Processing (NLP), and Retrieval-Augmented Generation (RAG). The proposed system provides four major functionalities: constitution-based legal advisory, multilingual legal document generation, legal document summarization, and agreement clause verification. The frontend of the system is developed using React.js, while the backend integrates Django, OpenAI APIs, FAISS retrieval, and NLP frameworks. Experimental results demonstrate fast response generation, scalable document processing, and high module accuracy. JurisAI improves accessibility, transparency, and efficiency in legal services for users with limited legal expertise.

KEYWORDS

Artificial Intelligence NLP Legal Technology Retrieval-Augmented Generation Document Summarization Clause Verification.

Cite This Article

Riya Deshmukh, Samruddhi Deokar, Manas Dhondge, Shweta Desai, Prof. S.P. Bholane, "JURISAI: SMART LEGAL ADVISOR AND DOCUMENT ASSISTANT," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 6, pp. 1-5, Jun 2026. https://doi.org/10.55041/IJSREM65268

Full Article/Conclusion

JurisAI successfully demonstrates how Artificial Intelligence and Natural Language Processing can improve legal ac-cessibility and transparency through intelligent automation. The proposed system integrates legal advisory, multilin-gual document generation, document summarization, and clause verification into a unified platform. Experimental analysis confirmed high accuracy, scalable performance, and efficient response generation. JurisAI represents an important step toward accessible and trustworthy AI-driven legal systems capable of supporting both individuals and or-ganizations.Furthermore, the proposed framework highlights the growing potential of AI-assisted legal ecosystems in reducing legal complexity and improving public engagement with judicial processes. Thus, JurisAI establishes a strong foundation for future intelligent legal platforms that promote faster, more reliable, and user-friendly legal assistance.

References

[1]    LawPal Team, “RAG Legal Chatbot: Constitution-grounded AI Legal Advisor,” arXiv preprint arXiv:2402.01896, 2024. [2]    OpenAI, “GPT Models and Retrieval-Augmented Generation Framework,” OpenAI Technical Overview, 2024. [3]    Hugging Face, “Transformers and NLP Pipelines,” Hugging Face Documentation, 2024. [4]    Django Software Foundation, “Django Framework Documentation,” Version 4.2, 2024. [5]    FAISS Team, “Efficient Similarity Search using FAISS,” Meta AI Research, 2024. [6]    Legal NLP Researchers, “Proprietary Document Generation using MAW and NyayaShilp Model,” arXiv preprint arXiv:2503.04125, 2025.

Editorial & Review Transparency

Publication Date

6/21/2026

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.

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