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AI-based Legal Document Summarization and Case Prediction System

by Nagamani Thanuboddi, Mudiga Prachay Kumar, Garikapati Nani, Nayeni Sai Yuvan Chaitanya Reddy, Pasupula Pradeep Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 100
Year of Publication: 2026
Authors: Nagamani Thanuboddi, Mudiga Prachay Kumar, Garikapati Nani, Nayeni Sai Yuvan Chaitanya Reddy, Pasupula Pradeep Kumar
10.5120/ijca5757254729ef

Nagamani Thanuboddi, Mudiga Prachay Kumar, Garikapati Nani, Nayeni Sai Yuvan Chaitanya Reddy, Pasupula Pradeep Kumar . AI-based Legal Document Summarization and Case Prediction System. International Journal of Computer Applications. 187, 100 ( Apr 2026), 1-6. DOI=10.5120/ijca5757254729ef

@article{ 10.5120/ijca5757254729ef,
author = { Nagamani Thanuboddi, Mudiga Prachay Kumar, Garikapati Nani, Nayeni Sai Yuvan Chaitanya Reddy, Pasupula Pradeep Kumar },
title = { AI-based Legal Document Summarization and Case Prediction System },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2026 },
volume = { 187 },
number = { 100 },
month = { Apr },
year = { 2026 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number100/ai-based-legal-document-summarization-and-case-prediction-system/ },
doi = { 10.5120/ijca5757254729ef },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-04-28T21:29:31.375927+05:30
%A Nagamani Thanuboddi
%A Mudiga Prachay Kumar
%A Garikapati Nani
%A Nayeni Sai Yuvan Chaitanya Reddy
%A Pasupula Pradeep Kumar
%T AI-based Legal Document Summarization and Case Prediction System
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 100
%P 1-6
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The increasing volume of digital legal documents such as court judgments, FIR records, contracts, and legal orders has made manual analysis both time-consuming and difficult for legal professionals. This study proposes LawTech AI, an AI-powered legal document intelligence system designed to transform unstructured legal text into structured and searchable legal information. The system follows a multi-stage pipeline where legal documents are first processed to extract key entities using Legal Named Entity Recognition (NER). The extracted information is then analyzed through the FASSI workflow (Fetch, Analyze, Summarize, Store, and Interact) to understand the legal context of the document. To enable efficient legal search and precedent discovery, document embeddings are generated and stored in a FAISS vector database, allowing the system to retrieve the top similar cases. These retrieved cases are used within a Retrieval Augmented Generation (RAG) framework to assist a fine-tuned legal language model in generating structured summaries, identifying legal issues, and providing insights based on relevant precedents. The proposed approach aims to support lawyers, legal researchers, and students by simplifying legal document analysis and improving access to meaningful legal knowledge.

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Index Terms

Computer Science
Information Sciences

Keywords

Legal Document Intelligence Legal Named Entity Recognition (NER) FAISS Vector Database Retrieval Augmented Generation (RAG) Legal AI Case Law Analysis Legal Document Processing

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