The AI Revolution in Legal Discovery: How to Use AI for Discovery Review
Artificial intelligence is changing how legal teams handle eDiscovery. For litigators, in-house counsel, and legal support staff, the volume of data in modern matters can be overwhelming. Emails, chat logs, shared documents, spreadsheets, and other digital files can quickly grow into millions of records.
Manual review alone is slow, expensive, and vulnerable to inconsistency. AI helps legal teams review data faster, reduce review costs, and identify key information earlier in the process. If you are evaluating how to use AI for discovery review, the goal is not to replace legal judgment. It is to make review more efficient, more consistent, and easier to manage.
Why AI for Discovery Review Matters
Discovery can take a large share of a litigation budget. When review is inefficient, teams risk missing deadlines, overlooking important documents, and increasing exposure to error.
AI-powered discovery review tools help legal teams:
- Speed up review by sorting and categorizing large data sets quickly
- Reduce costs by limiting the amount of manual first-pass review
- Improve consistency by applying the same logic across large collections
- Surface responsive or privileged documents earlier
- Identify themes, patterns, and connections that may be hard to spot manually
For legal teams, that means more time for strategy and analysis, and less time spent on repetitive document triage.
How AI Is Used in Discovery Review
AI tools support discovery review in several practical ways:
- Technology Assisted Review (TAR): trains a model on documents reviewed by humans and uses that training to predict relevance across the rest of the dataset
- Concept clustering: groups documents by topic or similarity
- Auto-coding: helps assign issue tags, responsiveness, or privilege indicators
- Duplicate detection: removes repeated files from the review burden
- Prioritization: surfaces likely relevant documents earlier
- Search enhancement: improves search by identifying related terms and concepts
These features do not eliminate human review. They help legal teams focus their attention where it matters most.
Top AI Tools for Discovery Review
The eDiscovery market includes several established platforms with AI-driven features. The right option depends on your case size, workflow, and internal resources.
1. RelativityOne
What it does: RelativityOne is a cloud-based eDiscovery platform with AI capabilities such as TAR, predictive coding, clustering, and document categorization. It supports ingestion, processing, analysis, and review at scale.
Why it is useful: RelativityOne is widely used for complex matters because it offers deep functionality, scalability, and strong integration options.
Best fit: Large litigation, regulatory matters, and investigations involving high data volumes.
Pros:
- Strong enterprise-level feature set
- Advanced TAR and analytics
- Scales well for very large matters
- Broad integration ecosystem
- Strong security and compliance support
Cons:
- Can require more training than simpler platforms
- May be a larger investment for smaller teams
2. Everlaw
What it does: Everlaw is a cloud-native platform focused on collaboration and ease of use. It includes TAR, clustering, and auto-coding features designed to streamline review.
Why it is useful: Everlaw is approachable for legal teams that want strong AI tools without a steep learning curve.
Best fit: Law firms and corporate teams of different sizes that want a modern, collaborative review environment.
Pros:
- Intuitive interface
- Strong collaboration features
- Effective TAR and clustering tools
- Responsive support
- Clear pricing structure
Cons:
- Less customizable than some enterprise-heavy platforms
- May not cover every specialized workflow
3. DISCO AI
What it does: DISCO offers AI-powered tools for eDiscovery, including automated review, clustering, concept searching, and issue identification. It is designed with an AI-first approach.
Why it is useful: DISCO is built to help teams move quickly through large datasets and support early case assessment.
Best fit: Litigation teams that want fast review workflows and early insight generation.
Pros:
- Strong AI-driven review capabilities
- Helpful for early case assessment
- User-friendly design
- Good scalability
- Cloud-based platform
Cons:
- Smaller ecosystem than some legacy platforms
- May be more than some very small matters require
4. Logikcull, now part of CloudNine
What it does: Logikcull is known for simplifying eDiscovery with automation and a straightforward interface. Its AI features support document review, duplicate detection, and redaction workflows.
Why it is useful: It reduces manual effort and is often easier to adopt than more complex systems.
Best fit: Small and mid-sized firms, solo practitioners, and legal teams looking for a simpler review tool.
Pros:
- Easy to use
- Strong automation
- Good for cost-conscious teams
- Streamlines review workflows
- Fits within the broader CloudNine ecosystem
Cons:
- Less advanced than enterprise-focused platforms
- Some functionality may change as the product evolves under CloudNine
5. XDD (Xcelerated Data Discovery)
What it does: XDD combines AI-driven eDiscovery tools with managed services. Its platform supports TAR, concept searching, and privilege identification.
Why it is useful: XDD is a good option for teams that want both technology and expert support.
Best fit: Firms and corporations that want a full-service discovery solution or need help managing complex review projects.
Pros:
- AI technology plus human support
- Effective analytics and TAR
- Strong security and defensibility focus
- Scalable for large matters
- Full-service offering
Cons:
- Less hands-on control for teams that want fully in-house review
- Managed services can increase overall cost
6. Kroll Ontrack
What it does: Kroll Ontrack offers eDiscovery and data services with AI features such as predictive coding, clustering, and analytics.
Why it is useful: Kroll brings long-standing experience and a strong reputation for defensible workflows.
Best fit: Complex litigation, compliance work, and investigations where reliability and defensibility are priorities.
Pros:
- Deep industry experience
- Comprehensive review capabilities
- Strong security and defensibility focus
- Global reach
- Broad service offerings
Cons:
- Can be a premium-priced option
- Often strongest when paired with managed services
How to Choose the Right AI Tool
The best tool depends on your matter, team, and budget. Use these factors to compare options:
- Case complexity and data volume: Large, complex matters may require platforms like RelativityOne or DISCO AI. Smaller matters may be better suited to Everlaw or Logikcull.
- Budget: Review the full cost, including subscriptions, data processing, storage, and support.
- Team experience: Choose a platform your team can use effectively without creating extra training burden.
- Needed AI features: Decide whether you need TAR, clustering, search enhancement, auto-coding, or managed review support.
- Workflow integration: Make sure the tool fits with your existing legal technology and case management process.
- Support and training: Reliable onboarding and customer support can make a major difference.
- Defensibility and security: The tool should support audit trails, consistent review methods, and secure handling of sensitive data.
Pricing and Value
AI-powered discovery review tools are typically priced in different ways:
- Subscription pricing: monthly or annual access to the platform
- Per-gigabyte or per-matter pricing: based on data volume or case scope
- Managed services pricing: includes technology plus expert support
The lowest price is not always the best value. A tool that reduces review time, improves consistency, and lowers the risk of missed documents may save more overall than a cheaper platform with limited capabilities. Always ask for a full quote and confirm what is included.
Frequently Asked Questions
What is Technology Assisted Review (TAR)?
TAR is a review method that uses machine learning to help identify relevant documents in eDiscovery. Human reviewers train the system on sample documents, and the AI then predicts which other documents are likely to be responsive, privileged, or irrelevant.
Can AI replace human reviewers?
No. AI can reduce the amount of manual review, but it should not replace legal judgment. Human reviewers are still needed to evaluate context, close calls, and final production decisions.
How do I make AI-driven discovery defensible?
Use a platform with audit trails, clear training documentation, and strong quality control. Keep a record of your workflow and be prepared to explain how the review process was managed.
What data can AI analyze?
AI can review emails, documents, spreadsheets, presentations, chat logs, social media content, audio files, and video files, depending on the platform.
Is AI difficult to implement in discovery review?
It depends on the platform and your team’s experience. Some cloud-based tools are designed for fast adoption, while more advanced systems may require training or outside support.
Conclusion
AI is now a practical part of modern discovery review. It helps legal teams work faster, reduce costs, and review large data sets more effectively. Platforms like RelativityOne, Everlaw, DISCO AI, Logikcull, XDD, and Kroll Ontrack each offer different strengths, so the right choice depends on your matter size, team structure, and workflow needs.
If you are deciding how to use AI for discovery review, start by defining your goals, reviewing your budget, and identifying the features that matter most. The right tool can make discovery more efficient, more consistent, and more manageable from the first pass through final production.