Everlaw
Everlaw is a cloud-native ediscovery and litigation platform designed to help legal teams, corporations, and government agencies manage large-scale document reviews and internal investigations. The platform moves beyond simple document …
About Everlaw
Everlaw is a cloud-native ediscovery and litigation platform designed to help legal teams, corporations, and government agencies manage large-scale document reviews and internal investigations. The platform moves beyond simple document …
Use Cases
Real-world scenarios where Everlaw saves time.
Use Case 1: Large-Scale Document Review
Problem: Legal teams face millions of documents in discovery, making manual review of every page impossible.
Solution: Everlaw uses predictive coding and clustering to prioritize relevant files and visualize data patterns.
Example: In a complex litigation case, a team identifies the most critical 5% of documents out of 10 million files.
Use Case 2: Rapid Information Extraction
Problem: Finding specific facts or citations across a massive data set takes hours of manual searching.
Solution: The 'Deep Dive' feature navigates the corpus to surface insights with direct citations for verification.
Example: A lawyer asks the system to find all mentions of a specific contract clause across multiple email threads.
Use Case 3: Persuasive Argument Drafting
Problem: Lawyers need to compile disparate facts into cohesive narratives for case strategy or trial prep.
Solution: An AI writing assistant synthesizes evidence and brainstorms case strategies to create first drafts.
Example: A legal team uses Storybuilder to draft a trial brief that compiles evidence from 50 different sources automatically.
Key Features
What you get out of the box.
- Deep Dive citation-backed data extraction
- Automated first-pass coding suggestions
- AI-powered Storybuilder writing assistant
- Predictive coding for document prioritization
- Visual data clustering for pattern discovery
- Integrated multi-language translations
- Cloud-native ediscovery infrastructure
- Agentic workflows via Anthropic MCP integration
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