AI Tools Are Reshaping Legal Research in India — But Look at What the Numbers Actually Say
Opinion | June 2026
When the discourse about AI in Indian legal practice sticks to strategy and aspiration, it sounds like every other tech announcement of the last decade: transformational, imminent, important. The profession should pay more attention to the actual deployment numbers, because they tell a more specific and more honest story about what AI is doing to Indian courts right now.
The Backlog is a Constitutional Failure.
India's courts have been in structural collapse and that has been normalised into bureaucratic language. As of early 2026, India's district courts held over 50 million pending cases.¹ High Courts added 6.2 million more. The Supreme Court's own docket exceeded 90,000 matters.² In 2018, the Niti Aayog calculated that at the prevailing rate of disposal, it would take 324 years to clear the judicial backlog.²
Against that scale, AI is not a productivity enhancement for busy professionals. It is a structural necessity. Every tool that accelerates case processing, reduces transcription disputes, or makes research faster is, in a direct and non-trivial sense, restoring access to justice for people who have waited years for it.
What Is Actually Deployed
SUPACE (Supreme Court Portal for Assistance in Court Efficiency) is the judiciary's AI research tool, operational at the Supreme Court level, built on Indian judicial data rather than adapted from a generic model.¹
SUVAS (Supreme Court Vidhik Anuvaad Software) has translated over 36,271 Supreme Court judgments into Hindi.¹ This is not a pilot. It is a running translation operation on India's most important jurisprudence, aimed at the straightforward but consistently ignored problem that most Indians cannot read their own case files.
Adalat AI is the most operationally significant deployment. The software transcribes court proceedings in real time. In 2025, the Kerala High Court issued a mandatory deployment order covering all subordinate courts in the state. The results: a 30–50% reduction in case timelines.¹ That is not a projected efficiency gain. That is a measured outcome from actual court operations.
LegRAA (Legal Research Analysis Assistant), developed by the National Informatics Centre in Pune, and Digital Courts 2.1, which integrates ASR-SHRUTI and PANINI translation, complete the judiciary-facing picture.¹ The government has committed Rs. 53.57 crore (~$6.4 million) for AI and blockchain across High Courts through 2027, within a Rs. 7,210 crore e-Courts Phase III budget.¹
What Private Practice Is Doing
SCC Online — serving over 150,000 legal professionals across 4 million+ judgments and 400+ databases — has built a conversational AI assistant on Microsoft Azure OpenAI.² A first-year associate in a district court town who has never learned a Boolean search string can now ask a question in natural language and get a relevant, sourced answer. That is access expansion, not just efficiency improvement.
Trilegal, India's most prominent early-adopter, has moved past the pilot stage entirely. As of late 2025, Partner Nikhil Narendran described AI use as "well beyond the pilot stage" — embedded in document review, contract summarisation, due diligence, multilingual translation, and chronology building.³ Kuruvila Jacob, Senior Associate, noted that date-extraction tasks consuming entire evenings now take minutes. The time has been reallocated to mentoring, pro bono work, and higher-order legal thinking.
The Research AI That Isn't Good Enough
A January 2025 study from the National Law School of India tested GPT-4, Claude, and ChatGPT on actual Indian legal tasks and found that while LLMs performed well on drafting and issue spotting, they "frequently generated hallucinations" on specialised legal research.⁴ Stanford's Human-Centered AI lab found something more alarming: legal AI tools specifically built for legal research hallucinate in more than one out of every six benchmarking queries.⁵ Not occasionally. Structurally, at a 16–17% base rate.
This is not an argument against using AI for legal research. It is an argument against using it naively. The Supreme Court's draft AI regulations require verification of AI outputs before use in judicial processes and mandate disclosure at filing.⁶ That regulatory structure institutionalises what responsible practice already requires: AI is a first-pass tool, not an end product.
The Access Gap That the Productivity Story Obscures
There are two parallel trajectories in Indian legal AI. One is the productivity story — elite law firms deploying sophisticated tools. The other is the access story — ordinary litigants in district courts who have no lawyer, cannot read the orders in their cases, and interact with a justice system designed for educated, English-speaking professionals.
AI could close that gap. SUVAS, LAWFYI's Legal Aid chatbot, and tools that explain judgments in regional languages are pointed in the right direction. But they receive a fraction of the attention and resources that go toward platforms serving professional users. Kerala's Adalat mandate is the model: a High Court deciding AI deployment in subordinate courts is infrastructural, not optional, measuring the outcome, and publishing the result. Thirty to fifty percent shorter case timelines means people waiting years for justice are now waiting significantly less.