Extract vs Relativity
Side-by-side comparison to help you choose.
| Feature | Extract | Relativity |
|---|---|---|
| Type | Product | Product |
| UnfragileRank | 29/100 | 32/100 |
| Adoption | 0 | 0 |
| Quality | 0 | 1 |
| Ecosystem | 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Paid |
| Capabilities | 9 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Converts scanned or image-based legal documents into machine-readable text using AI models trained specifically on legal language, terminology, and formatting conventions. Achieves higher accuracy than generic OCR tools on contracts, filings, and legal correspondence.
Automatically identifies and extracts specific legal clauses, obligations, dates, parties, and key terms from contracts. Uses legal domain knowledge to recognize clause types and their significance within contract structures.
Automatically classifies legal documents into categories (contracts, NDAs, employment agreements, etc.) and subcategories based on content analysis. Enables intelligent routing and organization of document workflows.
Identifies and extracts all obligations, deadlines, renewal dates, and time-sensitive requirements from legal documents. Structures this information for calendar integration and compliance tracking.
Automatically identifies and extracts all parties, signatories, and entities mentioned in legal documents. Recognizes company names, individual names, roles, and contact information with legal context awareness.
Analyzes legal documents to identify potential risks, unfavorable terms, liability limitations, and unusual clauses that may require legal review. Flags high-risk language patterns based on legal domain knowledge.
Compares multiple versions of legal documents to identify changes, additions, and deletions. Highlights differences and analyzes how modifications affect key terms and obligations.
Processes large volumes of documents in batch mode, applying OCR, extraction, and classification to hundreds or thousands of documents simultaneously. Enables scalable document management workflows.
+1 more capabilities
Automatically categorizes and codes documents based on learned patterns from human-reviewed samples, using machine learning to predict relevance, privilege, and responsiveness. Reduces manual review burden by identifying documents that match specified criteria without human intervention.
Ingests and processes massive volumes of documents in native formats while preserving metadata integrity and creating searchable indices. Handles format conversion, deduplication, and metadata extraction without data loss.
Provides tools for organizing and retrieving documents during depositions and trial, including document linking, timeline creation, and quick-search capabilities. Enables attorneys to rapidly locate supporting documents during proceedings.
Manages documents subject to regulatory requirements and compliance obligations, including retention policies, audit trails, and regulatory reporting. Tracks document lifecycle and ensures compliance with legal holds and preservation requirements.
Manages multi-reviewer document review workflows with task assignment, progress tracking, and quality control mechanisms. Supports parallel review by multiple team members with conflict resolution and consistency checking.
Enables rapid searching across massive document collections using full-text indexing, Boolean operators, and field-specific queries. Supports complex search syntax for precise document retrieval and filtering.
Relativity scores higher at 32/100 vs Extract at 29/100.
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Identifies and flags privileged communications (attorney-client, work product) and confidential information through pattern recognition and metadata analysis. Maintains comprehensive audit trails of all access to sensitive materials.
Implements role-based access controls with fine-grained permissions at document, workspace, and field levels. Allows administrators to restrict access based on user roles, case assignments, and security clearances.
+5 more capabilities