Every legal team knows the pattern: an NDA lands in the queue, a team member reads it, classifies it, drafts redlines, and posts the analysis back to the ticket. For standard mutual NDAs with boilerplate terms, this is repetitive work that consumes time without requiring real legal judgment.
The costs are turnaround delays, inconsistent classification across reviewers, and context-switching overhead. The team spends more time formulating redlines than reviewing them.
The workflow runs on a schedule (every 30 minutes), picks up new NDA tickets, and processes each one through a classification and redline pipeline.
Query the ticketing system for un-triaged NDAs. Download the attachment, convert to .docx if needed, and extract text.
An LLM reads the NDA alongside your team's playbook and classifies it using a traffic-light system.
For YELLOW/RED, a second LLM pass generates clause-specific redlines with exact quotes, replacement language, and playbook citations. Applied as tracked changes to the .docx.
Classification, summary, and redlined doc are posted as an internal comment. The ticket is labeled to prevent reprocessing.
NDA matches your standard template. No redlines needed. Ready to sign.
Contains non-standard clauses that need redlines. AI generates tracked changes for review.
Significant deviations or non-standard structure. Flagged for manual review with detailed analysis.
Eliminated reviewing and redlining all new NDAs on the legal service desk. Reduced review time significantly. The team now spends more time reviewing redlines rather than formulating them. Classification consistency improved across the team, and turnaround time on standard NDAs dropped substantially.