Everlaw AI Assistant for Responsive Review
Description
Everlaw AI Assistant’s Coding Suggestions lets OTAR and DO set natural-language criteria (case, category, and code descriptions) to assess document responsiveness. By tagging documents using keywords, account numbers, and contextual clues, it reduces manual review and speeds document triage
Detailed example
The AI assistant provides one of four coding suggestions: Yes (direct match), Soft Yes (plausible relevance), Soft No (weak relevance), or No (not relevant). DOI reviewers can filter by category, validate samples, and refine code descriptions. Output is advisory; reviewers decide whether to apply the ‘Responsive’ code.
AI / analytics pattern
Agentic AI: AI systems that perform tasks or make decisions autonomously with minimal human intervention.
Automation level / stage
b) Pilot – The use case has been deployed in a limited test or pilot capacity.
Expected benefit
Reduced manual labor cost and cycle time for responsive review; more consistent and rapid identification of responsive documents; meet discovery deadlines; large scale document search; and improved classification for historical accounting.
Controls / human review
ATO: Yes; PIA: Not published
Data needed
digitized documents such as contracts, account records, correspondence, and financial records