How to Use AI for Discovery Review: A Practical Guide for Legal Teams
Discovery review often means working through massive volumes of emails, documents, chat logs, and other electronically stored information. In complex matters, that process can be slow, expensive, and difficult to manage manually.
AI is changing that workflow. Used well, it can help legal teams sort, prioritize, analyze, and search large datasets faster and more consistently. That does not eliminate the need for human review, but it can make the review process more efficient and more strategic.
This guide explains how to use AI for discovery review, what it can do, and how to choose the right tool for your practice.
Why AI Matters in Discovery Review
Discovery review carries real risk. Missed documents can weaken a case, create privilege issues, or lead to unnecessary cost. Manual review can also be difficult to scale when a matter involves thousands or millions of files.
AI helps legal teams manage that volume by automating repetitive tasks and improving document analysis. The result is often faster review, better organization, and more time for attorneys to focus on legal strategy.
Key benefits include:
- Faster review cycles: AI can process large volumes of documents much more quickly than manual review alone.
- Lower review costs: Reducing the amount of manual document-by-document work can significantly cut expenses.
- More consistent results: AI can apply the same review logic across a dataset, which helps reduce inconsistency.
- Better prioritization: Relevant documents can be surfaced earlier, so teams can focus on the most important material first.
- Stronger insights: AI can reveal patterns, relationships, and themes that may be harder to spot during manual review.
- Scalable workflows: AI tools can support large matters without requiring a proportional increase in review staff.
How AI Is Used in Discovery Review
AI can support discovery review at several stages of the process:
- Early case assessment: Quickly identify likely relevant data and understand the scope of the matter.
- Document culling: Remove obvious duplicates, near-duplicates, and clearly irrelevant material.
- Categorization and clustering: Group similar documents together by topic or concept.
- Predictive coding and TAR: Use machine learning to help prioritize responsiveness or privilege review.
- Search and retrieval: Improve search results using natural language queries and concept-based matching.
- Privilege detection: Flag documents that may require closer attorney review.
- Summarization: Create short summaries to help teams evaluate documents more efficiently.
Best AI Tools for Discovery Review
The right platform depends on the size of the matter, the amount of data involved, and how your team works. Below are several widely used tools with AI capabilities that support discovery review.
1. RelativityOne
What it does: RelativityOne is a cloud-based eDiscovery platform with AI features for data processing, review, analysis, clustering, and predictive coding.
Why it is useful: It offers an end-to-end environment for handling large and complex discovery projects. Its AI tools help organize documents, surface likely relevant material, and support multi-stage review workflows.
Best fit: Large law firms and corporate legal departments managing high-volume litigation or investigations.
Pros:
- Comprehensive eDiscovery platform
- Strong AI capabilities, including clustering and predictive coding
- Cloud-based and scalable
- Built for collaboration
- Strong security and compliance features
Cons:
- Can be expensive
- Requires training to use effectively
- Broader eDiscovery platform, not just a standalone AI review tool
2. DISCO AI
What it does: DISCO offers a cloud-native eDiscovery platform powered by AI and natural language processing. It supports search, analysis, anomaly detection, and document summarization.
Why it is useful: DISCO is designed for speed and usability. Its AI features help users search by meaning rather than relying only on keywords.
Best fit: Firms and legal teams that want an intuitive, AI-driven review platform.
Pros:
- Easy to use
- Strong AI search and analysis
- Cloud-native and scalable
- Helpful for quick insights and review acceleration
- Good support and training resources
Cons:
- May not include every niche feature found in larger legacy platforms
- Pricing may still be a consideration for smaller firms
3. Everlaw
What it does: Everlaw is a cloud-native eDiscovery platform with AI features for predictive coding, clustering, concept search, and document organization.
Why it is useful: Everlaw supports fast review and collaboration while helping teams identify themes and organize large data sets.
Best fit: Law firms of all sizes that want a modern platform with strong usability and collaboration tools.
Pros:
- User-friendly interface
- Strong AI-driven review tools
- Good collaboration features
- Cloud-based and scalable
- Emphasis on security and data integrity
Cons:
- Can be costly for smaller firms
- Some specialized eDiscovery needs may require other tools
4. Casetext
What it does: Casetext is best known for its AI legal research tools, including CARA, which analyzes legal documents and suggests relevant authority.
Why it is useful: While it is not a primary eDiscovery platform, it can help attorneys connect factual issues in discovery with relevant legal arguments, cases, and statutes.
Best fit: Lawyers who want AI to support legal research and help frame discovery review around the issues in the case.
Pros:
- Strong AI legal research capabilities
- Useful for analyzing legal arguments and related authority
- Helps speed up research and drafting
- Fits into existing legal workflows
Cons:
- Not a full eDiscovery review platform
- More useful for informing review than for processing large document sets
5. X1 Search
What it does: X1 Search is an enterprise search tool that indexes and searches across email, cloud storage, local files, and other data sources.
Why it is useful: It can help legal teams quickly locate relevant information across disconnected sources, especially at the early stage of an investigation or matter assessment.
Best fit: Teams that need fast cross-platform search before formal eDiscovery processing begins.
Pros:
- Fast search across multiple data sources
- Natural language search capabilities
- Useful for early case assessment
- Helps with targeted collection and review planning
Cons:
- Not a full eDiscovery review platform
- Requires proper setup and indexing to work well
How to Choose the Right AI Tool for Discovery Review
The best tool depends on your case type, budget, and workflow. A careful evaluation should focus on the following factors:
Case size and complexity
- For large, complex litigation, full eDiscovery platforms like RelativityOne or Everlaw are often the strongest choices.
- For early case assessment or fast search across multiple systems, X1 Search may be more useful.
- For research-driven review, Casetext can help connect facts to legal authority.
Ease of use
- If your team needs a simple interface and fast onboarding, DISCO AI and Everlaw are strong options.
- If you have experienced eDiscovery users, a more complex platform may be worth the added depth.
AI functionality
- For clustering, predictive coding, and large-scale review, look at RelativityOne or Everlaw.
- For natural language search and document analysis, DISCO AI is a strong option.
- For legal research tied to evidence review, Casetext is useful in a different way.
- For cross-system search and early data location, X1 Search stands out.
Budget and pricing
- Enterprise platforms can require a significant investment.
- Some tools may be better suited to firms that want flexible subscription or matter-based pricing.
- Always factor in setup, training, and support costs, not just the base subscription.
Integration
- Check whether the tool works with your document management system, case management software, or other legal tech tools.
- Smooth integration can reduce friction and improve adoption.
Team size
- Smaller firms may benefit from simpler, more intuitive tools.
- Larger firms and legal departments may need broader platform capabilities and stronger administrative controls.
A practical approach is to define your must-have features, demo a short list of products, and test them against a real matter or sample dataset before committing.
Pricing and Value Considerations
Pricing for AI-powered discovery review tools varies widely. Some tools are accessible on a subscription basis, while enterprise platforms may involve larger monthly or annual commitments.
Common pricing models include:
- Subscription pricing: Based on users, data volume, or both
- Per-matter pricing: Tied to a specific case or project
- Tiered plans: Higher tiers unlock advanced AI features
- Implementation fees: Setup, migration, and training may be billed separately
- Support packages: Premium support may cost extra
When comparing value, look beyond the headline price. Consider:
- Time saved during review
- Reduction in manual review costs
- Fewer errors and missed documents
- Ability to handle more matters or larger matters with the same team
The right tool should fit your workflow and provide measurable efficiency gains, not just a new interface.
Frequently Asked Questions
Is AI replacing human reviewers in discovery?
No. AI is best used to assist human reviewers, not replace them. It can handle repetitive and high-volume tasks, but attorneys still need to make final judgments on relevance, privilege, and strategy.
Can AI identify privileged documents?
Many tools can flag potentially privileged material based on patterns or review training. However, final privilege determinations should still be made by qualified legal professionals.
What is Technology Assisted Review?
Technology Assisted Review, or TAR, is a method that uses machine learning to help classify documents. Human reviewers train the system on sample documents, and the model then helps predict how the rest of the dataset should be reviewed.
Can AI tools work with existing eDiscovery workflows?
Yes. Many modern platforms are designed to integrate with broader legal technology stacks and support established discovery workflows.
Do these tools require technical expertise?
It depends on the platform. Some are built for ease of use, while others may require more setup or administrator support. User-friendly tools like DISCO AI and Everlaw are generally easier for legal teams to adopt.
Conclusion
AI is becoming an important part of discovery review for law firms and legal departments that need to manage large volumes of data efficiently. Used properly, it can speed up review, reduce costs, improve consistency, and help teams focus on the documents that matter most.
Tools like RelativityOne, DISCO AI, Everlaw, Casetext, and X1 Search serve different needs, so the best choice depends on your case volume, review goals, and budget. The key is to treat AI as a practical support tool that improves the discovery process while preserving attorney judgment where it matters most.