Relevance Detection

AI-Powered Relevance Detection

Automatically classify documents as responsive, non-responsive, or privileged using AI-driven analysis against your case-specific definitions.

Book a Demo →

How It Works

DecoverAI ingests your case definitions — responsiveness criteria, privilege rules, scope parameters — and applies them across your entire document corpus. The AI classifies each document based on the actual legal standards, not just keywords. It understands context: a forwarded news article between attorneys isn't privileged just because lawyers are on the thread.

Definition-Driven Classification

You provide the definitions for your matter — what constitutes responsiveness, what falls within scope, what's privileged. DecoverAI applies these consistently across every document, eliminating the inconsistency that plagues manual review where different reviewers apply different standards.

Beyond Keywords

Traditional tools rely on keyword matching. DecoverAI uses semantic understanding to identify documents that are responsive even without exact keyword matches, and to exclude documents that contain keywords but aren't actually relevant. The system distinguishes between a document discussing legal strategy (privileged) and one that merely mentions an attorney's name (not privileged).

Scale Without Compromise

Tag 30,000 documents in days, not weeks. In the Tax Credit Investigation, DecoverAI processed the entire corpus with consistent tagging in 3 days — work that would have taken traditional review 4 weeks.

Ready to see Relevance Detection in action?

Book a demo and learn how AI-powered tagging can accelerate your document review.

Book a Demo →