Luminal vs Relativity
Side-by-side comparison to help you choose.
| Feature | Luminal | Relativity |
|---|---|---|
| Type | Product | Product |
| UnfragileRank | 30/100 | 32/100 |
| Adoption | 0 | 0 |
| Quality | 1 | 1 |
| Ecosystem | 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Paid |
| Capabilities | 14 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Automatically parses and extracts structured data from unstructured or semi-structured sources like emails, PDFs, forms, and web content. Uses AI to learn formatting patterns and reduce manual mapping requirements by 80%+.
Detects errors, inconsistencies, and data quality issues before information reaches downstream systems. Flags problematic records for review and prevents bad data from corrupting databases or workflows.
Enhances existing data by adding missing information, standardizing formats, and augmenting records with additional context from connected data sources or external APIs.
Enables complex workflow logic with conditional branches, loops, and decision trees that route data and trigger different actions based on specified conditions without coding.
Identifies duplicate records across data sources and merges them intelligently, consolidating information while preserving data integrity and avoiding loss of important details.
Triggers workflows automatically when external systems send webhook events, enabling real-time responses to events from connected applications without polling or manual intervention.
Connects and automates workflows across 500+ integrated applications including Salesforce, HubSpot, and Zapier without requiring custom code. Enables multi-step processes that move data and trigger actions across disconnected systems.
Uses machine learning to automatically understand and replicate data mapping patterns from examples. Learns how to transform and structure data based on sample inputs and outputs provided by the user.
+6 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 Luminal at 30/100. Luminal leads on quality, while Relativity is stronger on ecosystem. However, Luminal offers a free tier which may be better for getting started.
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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