Vol. 11, Issue 5, Part D (2025)
Legal eagle: A digital law assistant
Legal eagle: A digital law assistant
Author(s)
Sukrati Chaturvedi A, Vineet Bhatt B, Disha Adak C and Umang Saini D
AbstractPeople from marginalized backgrounds often struggle to receive legal services due to low resources, language barrier and limited accessibility. Also, finding a lawyer can be a challenging task for both businesses and individuals. Many encounter significant obstacles in locating a suitable attorney that meets their specific needs, often due to a lack of accessible information. Typically, hiring a lawyer and Notary services requires an in-person meeting, which can be both time-consuming and costly. This study proposes designing and developing a digital law assistant specifically for underprivileged communities. The main goal is to efficiently address consumer’s legal concerns, like traffic tickets and criminal allegations matter, by offering clear instructions and necessary steps.
Legal Eagle is a web-based, AI-driven legal assistant designed to streamline access to legal services. The platform utilizes Natural Language Processing (NLP) and Term Frequency-Inverse Document Frequency (TF-IDF) to interpret complex legal terminology and offer user-specific guidance. Through these technologies, it can identify relevant legal protocols and enhance user decision-making by analyzing legal documents and predicting possible case outcomes. Moreover, it supports Chat bot by Gemini that understands intricate legal terminology and provides personalized guidance, incorporating applicable legal sections and processes. This advanced system improves decision-making by evaluating legal documents, forecasting case results, and categorizing records. The paper discusses ethical implications for Al, emphasizing its revolutionary role in providing quick and accessible legal support.
How to cite this article:
Sukrati Chaturvedi A, Vineet Bhatt B, Disha Adak C, Umang Saini D. Legal eagle: A digital law assistant. Int J Appl Res 2025;11(5):263-271. DOI:
10.22271/allresearch.2025.v11.i5d.12570