AI for Indian Legal Practice

AI is already in your practice. The question is whether you understand it.

Junior associates are using ChatGPT for research. Clients are asking about AI tools. Indian courts have sanctioned lawyers for filing fabricated citations generated by AI. The Supreme Court of India issued a White Paper on AI and the Judiciary in November 2025. The profession is moving fast, with or without a map.

This book is the map.

Written for Indian lawyers and law firms, this practical guide explains how AI tools actually work, where they fail, and how to use them without getting burned professionally or academically.

• Why ChatGPT invents citations and what architecture actually prevents it
• How to build or deploy a private offline legal chatbot for client-confidential documents
• What 2.45 million writ petition records reveal about why Indian High Courts are slow and why the standard prescription of appointing more judges does not work for every court
• What the data shows about quash petition success rates across court levels and what it means for practice
• How to evaluate any legal AI vendor with five questions that cut through marketing claims
• A minimum viable AI policy your firm can adopt today
• The regulatory landscape: DPDP Act, Supreme Court White Paper, Bar Council ethics

No mathematics required. No coding background assumed. The book assumes you can think carefully about reasoning, evidence, and inference. It assumes you are familiar with how law works, especially in India and common law countries.

The empirical findings are original: drawn from the author's own datasets of Indian court judgments, RTI decisions, and High Court case records, not recycled from US or UK legal AI commentary.

Joy Bose holds a PhD in computational neuroscience from the University of Manchester, an LLM from Golden Gate University, and has fifteen years of experience building AI systems at Ericsson, Microsoft, and Samsung. He is the creator of the Falkor-IRAC verified legal reasoning framework and the open datasets IMLJD and RTI-Bench.