How to Use AI for Discovery Review: Streamlining Legal Investigations
Discovery has always been one of the most resource-intensive parts of litigation. As the volume of digital data has grown, manual review methods have become harder to sustain. AI offers a practical way to speed up discovery review, improve consistency, and help legal teams focus on the documents that matter most.
For lawyers, paralegals, and legal operations teams, understanding how to use AI for discovery review is increasingly important. The right tools can reduce review time, support privilege screening, and improve overall case strategy without replacing attorney judgment.
Why AI Matters in Discovery Review
AI can make discovery review more efficient in several ways.
First, it helps teams manage volume. Instead of manually sorting through thousands or millions of documents, AI can organize and prioritize materials quickly. That frees attorneys to spend more time on analysis, strategy, and client service.
Second, AI can improve consistency. Human reviewers can miss documents when they are tired, rushed, or working across large datasets. AI applies rules and learned patterns more consistently, which can help reduce oversight and improve review quality.
Third, AI can support privilege and sensitivity review. Many platforms can flag likely privileged documents, sensitive communications, or responsive material for closer human review. That helps legal teams protect confidential information and keep review workflows moving.
AI can also uncover patterns that are difficult to spot manually. Search trends, document clusters, related entities, and recurring concepts can provide helpful context during investigations and litigation preparation.
Best AI Tools for Discovery Review
The AI legal tech market includes a range of eDiscovery platforms with different strengths. The best choice depends on your matter size, budget, workflow, and team structure.
1. RelativityOne
RelativityOne is a cloud-based eDiscovery platform with robust AI capabilities, including Technology Assisted Review (TAR), also known as Continuous Active Learning (CAL).
What it does:
RelativityOne supports data processing, hosting, review, and analytics in one platform. Its TAR functionality helps train the system by identifying relevant documents, then uses that feedback to predict relevance across the broader dataset. It also includes clustering, concept searching, and related analytics.
Why it is useful:
It centralizes the review workflow in a secure cloud environment and supports collaboration across distributed teams. Its AI tools can reduce review time and help prioritize likely relevant documents.
Best fit:
Large-scale litigation, regulatory matters, and complex investigations with significant document volumes.
Pros:
Highly scalable, strong security, broad feature set, mature TAR functionality, and extensive integrations.
Cons:
Can involve a steeper learning curve and may be expensive for smaller firms or smaller matters.
2. Everlaw
Everlaw is a cloud-based eDiscovery platform known for its user-friendly design and strong analytics.
What it does:
Everlaw provides tools for ingesting, processing, reviewing, and analyzing electronically stored information (ESI). Its AI features include TAR 2.0, clustering, sentiment analysis, and concept highlighting.
Why it is useful:
The interface is designed to be accessible to legal teams with varying levels of technical experience. Its AI tools help users identify key information faster and collaborate efficiently.
Best fit:
Mid-sized to large firms that want a balance of usability, collaboration, and advanced review features.
Pros:
Intuitive interface, strong collaboration tools, advanced analytics, responsive support, and transparent pricing.
Cons:
May not offer the same depth of niche workflow integration as some enterprise-focused platforms.
3. DISCO AI
DISCO AI is a cloud-native eDiscovery platform built to accelerate legal document review with AI-driven automation.
What it does:
DISCO AI uses TAR and related analytics to classify documents, identify relevant and privileged material, and reduce the number of documents requiring manual review. It also offers AI-powered search and clustering.
Why it is useful:
The platform is designed for speed and scale, which can be especially valuable in time-sensitive matters.
Best fit:
Law firms and corporate legal departments managing large discovery requests or internal investigations.
Pros:
Fast processing, strong AI performance, easy-to-use interface, and scalable cloud-native architecture.
Cons:
As a specialized eDiscovery platform, it may offer fewer broader practice-management features than all-in-one legal software.
4. Logikcull, now part of CloudSquare
Logikcull, now integrated into CloudSquare, focuses on making eDiscovery more accessible and easier to use.
What it does:
The platform supports data processing, review, and production, with features such as auto-redaction, intelligent categorization, and concept/entity identification.
Why it is useful:
It simplifies AI-powered review for teams that need efficiency without a heavy implementation burden.
Best fit:
Small to mid-sized firms, solo practitioners, and in-house teams looking for an accessible and cost-conscious solution.
Pros:
Easy to use, affordable relative to enterprise platforms, fast processing, and approachable for smaller teams.
Cons:
May not offer the same level of customization or advanced analytics as more complex enterprise tools.
5. XERA by UnitedLex
XERA is an AI-powered platform developed by UnitedLex for discovery and contract analysis.
What it does:
XERA uses AI and natural language processing (NLP) to analyze documents, flag responsiveness, identify privileged information, and automate related legal workflows.
Why it is useful:
It is designed to automate repetitive tasks and extract insights from unstructured data, with an emphasis on operational efficiency.
Best fit:
Corporate legal departments and firms looking for an end-to-end platform that supports discovery as well as adjacent legal workflows.
Pros:
Strong AI capabilities, broad workflow support, focus on measurable efficiency gains, and continuous development.
Cons:
May be more complex to implement and more expensive than narrower, specialized tools.
6. LexisNexis eDiscovery Platform
LexisNexis offers eDiscovery tools that incorporate AI for review and analytics.
What it does:
The platform supports data processing, review, and analytics, including predictive coding, concept clustering, and semantic search capabilities.
Why it is useful:
It can fit well for teams already using LexisNexis products and looking for a more integrated legal technology stack.
Best fit:
Law firms and legal departments that want eDiscovery capabilities within the broader LexisNexis ecosystem.
Pros:
Strong brand recognition, useful analytics, predictive coding, and integration potential with other LexisNexis tools.
Cons:
The platform can feel complex because of its breadth, and pricing may vary significantly based on usage and services.
How to Choose the Right AI Tool
Choosing an AI platform for discovery review depends on the matter and the team using it.
Volume of data:
If you regularly handle very large datasets, prioritize scalability and processing speed. Platforms like RelativityOne and DISCO AI are designed for that kind of workload. For smaller matters, a simpler tool may be more efficient and cost-effective.
Budget:
Pricing can vary widely. Enterprise platforms often cost more because of their depth, support, and infrastructure. If cost is a priority, look for transparent pricing and tools that fit your matter size.
Team expertise:
Some platforms are built for advanced eDiscovery teams, while others are easier for general legal users to adopt. Consider how much training and internal support the tool will require.
AI features:
Most platforms offer some form of TAR, but capabilities differ. If your workflow would benefit from clustering, sentiment analysis, entity extraction, or advanced search, compare those features carefully.
Integration:
The tool should fit into your existing workflow, including document management, case management, and review processes. Better integration usually means less friction and faster adoption.
Client expectations:
Some clients and opposing counsel may have preferences around review platforms, data handling, or production workflows. It is worth factoring those requirements into your selection process.
Pricing and Value Considerations
AI discovery tools may use different pricing models:
- Per-gigabyte hosting or processing
- Per-user licensing
- Per-matter or project-based pricing
- Hybrid pricing models that combine platform and usage fees
When comparing cost, do not focus only on the sticker price. Consider the time saved, the reduction in manual review, and the value of improved accuracy. A platform that costs more upfront may still be worthwhile if it reduces attorney hours, shortens timelines, and lowers the risk of missing important documents.
Always request a clear quote and confirm what is included, such as onboarding, support, analytics, storage, and production features.
Frequently Asked Questions About AI for Discovery Review
How accurate is AI in discovery review compared to human review?
AI, especially through TAR, can be highly accurate and may outperform manual review in identifying relevant documents. It works best when paired with human oversight.
Do I need to be a technology expert to use AI for discovery review?
No. Many modern platforms are built for legal users and include interfaces that simplify the technical side of the process. Some familiarity with eDiscovery workflows is helpful, but deep technical expertise is not always required.
Can AI handle all types of legal documents and data?
AI can process many common file types, including emails, Word documents, PDFs, spreadsheets, and images. Performance depends on the platform and the quality of the source data.
How does AI help identify privileged documents?
AI tools can flag documents based on patterns, metadata, keywords, and known privilege indicators. Those documents can then be reviewed more closely by humans.
What is TAR or CAL?
Technology Assisted Review (TAR), also called Continuous Active Learning (CAL), is a machine-learning approach that uses human-coded examples to train the system. The model learns from reviewer decisions and updates its predictions as review continues.
Will AI replace lawyers in discovery?
No. AI is a support tool, not a replacement for legal judgment. Lawyers still make the final decisions on relevance, privilege, and case strategy.
Conclusion
AI is now a practical part of modern discovery review. For legal teams dealing with large volumes of data, it can reduce manual effort, improve consistency, and surface important information faster.
The key is choosing a platform that fits the matter, the team, and the workflow. Whether you are evaluating RelativityOne, Everlaw, DISCO AI, Logikcull, XERA, or LexisNexis eDiscovery, the goal is the same: make discovery more efficient without losing legal judgment and control.
For firms and legal departments focused on how to use AI for discovery review, the opportunity is clear. The right tools can streamline investigations, improve review quality, and help legal teams work more effectively in a data-heavy environment.