LexSetuX: AI-Driven Legal Case Classification and Lawyer Recommendation System

  • Unique Paper ID: 197482
  • Volume: 12
  • Issue: 11
  • PageNo: 7725-7733
  • Abstract:
  • Access to accurate legal information and appropriate legal assistance remains a significant challenge for citizens due to the complexity of legal language, lack of awareness of rights and difficulty in identifying suitable legal professionals. This paper presents LexSetuX, an AI-Powered Legal Case Classification and Lawyer Recommendation System designed to bridge this gap using advanced Natural Language Processing and deep learning techniques. The proposed system employs LegalBERT, a domain-specific transformer model, to automatically analyze and classify legal case descriptions into relevant legal domains such as criminal, civil, family, property and corporate law. In addition to classification, the system maps cases to applicable constitutional rights, legal provisions and judicial precedents to enhance legal understanding. Furthermore, LexSetuX integrates an intelligent lawyer recommendation engine that suggests suitable legal professionals based on case type, specialization, experience and semantic relevance. The platform provides role-based outputs, offering simplified legal explanations and guidance for citizens, while delivering detailed legal insights, case precedents and risk assessments for lawyers. Experimental evaluation demonstrates that the proposed system achieves 92% classification accuracy, outperforming traditional machine learning approaches such as TF-IDF with SVM. The system also exhibits efficient response time and high recommendation relevance, validating its practical applicability in real-world legal assistance. LexSetuX contributes toward improving accessibility, efficiency and intelligence in legal support systems through AI-driven automation and decision assistance.

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{197482,
        author = {Deepali Jawale and DHRUVA GRAMOPADHYE and HARSHAL INGALE and OM BABAR and ATHARVA BHISE},
        title = {LexSetuX: AI-Driven Legal Case Classification and Lawyer Recommendation System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {7725-7733},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197482},
        abstract = {Access to accurate legal information and appropriate legal assistance remains a significant challenge for citizens due to the complexity of legal language, lack of awareness of rights and difficulty in identifying suitable legal professionals. This paper presents LexSetuX, an AI-Powered Legal Case Classification and Lawyer Recommendation System designed to bridge this gap using advanced Natural Language Processing and deep learning techniques. The proposed system employs LegalBERT, a domain-specific transformer model, to automatically analyze and classify legal case descriptions into relevant legal domains such as criminal, civil, family, property and corporate law. In addition to classification, the system maps cases to applicable constitutional rights, legal provisions and judicial precedents to enhance legal understanding. Furthermore, LexSetuX integrates an intelligent lawyer recommendation engine that suggests suitable legal professionals based on case type, specialization, experience and semantic relevance. The platform provides role-based outputs, offering simplified legal explanations and guidance for citizens, while delivering detailed legal insights, case precedents and risk assessments for lawyers. Experimental evaluation demonstrates that the proposed system achieves 92% classification accuracy, outperforming traditional machine learning approaches such as TF-IDF with SVM. The system also exhibits efficient response time and high recommendation relevance, validating its practical applicability in real-world legal assistance. LexSetuX contributes toward improving accessibility, efficiency and intelligence in legal support systems through AI-driven automation and decision assistance.},
        keywords = {},
        month = {April},
        }

Cite This Article

Jawale, D., & GRAMOPADHYE, D., & INGALE, H., & BABAR, O., & BHISE, A. (2026). LexSetuX: AI-Driven Legal Case Classification and Lawyer Recommendation System. International Journal of Innovative Research in Technology (IJIRT), 12(11), 7725–7733.

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