Insights on Drafting, AI, and Indian Litigation.

For years, the standard drafting workflow inside Indian litigation chambers has looked the same: open last year's petition, copy the structure, swap the names and dates, and hope nothing from the old matter survived the edit. It works, until it doesn't.

The problem with templates isn't that they save time — they do. A template is a static starting point. Infrastructure is a system that enforces consistency every single time, regardless of who is drafting or how late it is.

For litigation specifically, this matters more than in most legal work, because the cost of an error is not internal — it surfaces in front of a judge. A structured pipeline doesn't just save hours. It removes an entire category of risk that templates were never built to address.

When we started building LexoDesk, the obvious thing to build first was the drafting engine — the part that generates a Writ Petition or a Counter Affidavit. We didn't. We built the Verification Gate first.

The reasoning is simple. An AI system, however capable, only knows what it's given. If a date is wrong in the input, the date will be wrong in the draft. If a party's name is misread from a scanned order, that error doesn't get flagged — it gets formatted, paginated, and printed.

The Verification Gate exists to interrupt that chain. Before any drafting happens, It doesn't generate anything. It just asks you to look closely before anything else happens. That's exactly why it's the most important step we built.

General-purpose AI tools are genuinely capable. Ask one to draft a legal argument, and it will produce something fluent, well-structured, and confident. The issue for Indian litigation isn't fluency — it's procedure.

A Writ Petition before the Bombay High Court follows a different structural discipline than an SLP before the Supreme Court of India, which is different again from a plaint under the Commercial Courts Act before the Delhi High Court. These aren't stylistic preferences. They're procedural requirements with real consequences for a filing.

Generic AI models are trained on a global mixture of legal text, but by encoding Indian court procedure directly into the drafting logic, court by court, document by document.