AI for Indian Legal Practice: Graphs, Not Word Clouds

This book provides a practical guide to artificial intelligence in Indian legal practice, written for lawyers, law firms, and legal technologists. It addresses a gap in existing legal AI literature: most resources focus on US or UK contexts, use general-purpose AI tools without addressing their failure modes, and do not engage with Indian court data, Indian statutes, or Indian access-to-justice challenges.

The book argues that legal reasoning is structurally different from text retrieval, and that AI systems designed for law must reflect this. It introduces a graph-constrained generation framework (Falkor-IRAC) that grounds AI outputs in a verified knowledge graph of Indian court judgments, rejecting hallucinated citations before they reach the user. It surveys retrieval-augmented generation tools at four tiers of sophistication, from offline laptop deployments to firm-level server systems, and provides a practical decision framework for evaluating and procuring legal AI tools.

The empirical chapters draw on original datasets: 2.45 million writ petition records across ten Indian High Courts, revealing that courts fail in structurally different ways (Input, Capacity, and Output bottlenecks) with median case duration varying 26-fold; 3,613 Indian matrimonial court judgments (IMLJD dataset) showing a 19.6 percentage point quash petition success rate differential between the Supreme Court and Karnataka High Court; and 1,516 Central Information Commission decisions (RTI-Bench dataset) showing that Section 8(1)(j) personal information exemptions account for 34% of all exemption citations.

The book also covers documented cases of AI hallucination in Indian courts from 2024 to 2026, the Supreme Court White Paper on AI and the Judiciary (November 2025), the Digital Personal Data Protection Act and Rules, and Bar Council professional ethics obligations as they apply to AI use in legal practice.

All datasets, code, and companion materials are openly available. The book is licensed under CC BY-NC 4.0.