How to Use AI for Discovery Review: A Practical Guide for Legal Teams
AI is changing how legal teams handle discovery review. What was once a slow, manual, and costly process can now be streamlined with tools that help review large volumes of documents more efficiently. For law firms, in-house teams, and legal departments managing litigation or compliance matters, AI can reduce review time, improve consistency, and help teams focus on the documents that matter most.
This guide explains how to use AI for discovery review, what the tools can do, which platforms are commonly used, and how to choose the right solution for your practice.
Why AI for Discovery Review Matters
Discovery often involves reviewing emails, internal communications, financial records, contracts, chat logs, and other electronic data. In many matters, the volume is too large for manual review alone. Teams must identify relevant, responsive, and privileged documents while working under tight deadlines and budget pressure.
That creates several problems:
- Manual review is slow and expensive
- Large document sets increase the risk of oversight
- Review quality can vary depending on fatigue or experience
- High-volume matters can consume attorney and paralegal time that could be spent on higher-value work
AI helps legal teams manage this workload more effectively. Using machine learning, natural language processing, and analytics, AI discovery tools can sort, classify, prioritize, and surface documents more efficiently than manual review alone.
The main benefits include:
- Lower review costs
- Faster turnaround
- More consistent document classification
- Better document prioritization
- Improved focus on strategic legal work
- Reduced risk of missing important evidence
AI does not replace legal judgment. It supports the review process by helping teams work faster and more efficiently.
How AI Is Used in Discovery Review
AI discovery review tools can support several parts of the workflow:
- Data processing: Ingesting large volumes of files and preparing them for review
- Document classification: Grouping documents by topic, type, or likely relevance
- Predictive coding or active learning: Training the system based on reviewer decisions
- Conceptual search: Finding documents by meaning, not just exact keywords
- Clustering: Grouping similar documents together
- Deduplication and culling: Removing duplicate or clearly irrelevant material
- Prioritization: Surfacing documents most likely to be relevant or responsive
These features can shorten review cycles and help legal teams focus their time where it matters most.
Best AI Tools for Discovery Review
The right tool depends on the size of the matter, the complexity of the data, and the needs of the legal team. Below are several commonly used platforms and solutions.
1. RelativityOne
RelativityOne is a cloud-based eDiscovery platform with strong AI capabilities for document review, investigation, and analytics. It includes features such as active learning, conceptual search, and clustering to help teams identify relevant documents more efficiently.
Why it is useful:
RelativityOne supports the full eDiscovery workflow and is well suited for large, complex matters. Its AI features help prioritize documents for review, which can reduce the time spent on manual analysis.
Best fit:
Large law firms, corporate legal departments, and government agencies handling high-volume litigation or investigations.
Pros:
- Broad feature set
- Strong AI integration
- Scalable and secure
- Mature eDiscovery ecosystem
Cons:
- Can be complex to implement
- May require training
- Often more expensive than simpler tools
2. Logikcull
Logikcull is a cloud-native eDiscovery platform focused on speed and ease of use. It automates parts of the discovery process, including data processing, review, and production, with AI features that help reduce irrelevant material and highlight useful documents.
Why it is useful:
Logikcull is built for simplicity. Legal teams can get started quickly without extensive technical setup, making it a practical option for fast-moving matters.
Best fit:
Mid-sized firms, small to medium-sized businesses, and legal teams that want a straightforward AI-assisted review workflow.
Pros:
- Easy to use
- Fast processing
- Good collaboration features
- Accessible pricing for many users
Cons:
- Less advanced analytics than some enterprise platforms
- Fewer customization options
3. Disco
Disco offers AI-powered legal technology with a focus on document review. Its platform uses machine learning to support tasks like auto-categorization, identifying similar documents, and predicting relevance.
Why it is useful:
Disco is designed to improve as users interact with the system. Its interface and search tools are built to make document review more efficient while giving legal teams deeper visibility into the data.
Best fit:
Law firms, in-house teams, and litigation support teams looking for a user-friendly AI review platform.
Pros:
- Strong AI and analytics
- Intuitive interface
- Good search performance
- Focus on user experience
Cons:
- Can be premium-priced
- Some features may require training
4. Everlaw
Everlaw is a cloud-based eDiscovery platform that uses AI and machine learning to support document review, organization, and analysis. It includes tools such as predictive coding, clustering, and automated categorization.
Why it is useful:
Everlaw is built for collaboration and transparency. Its AI features help teams identify patterns in large data sets and reduce the amount of material requiring manual review.
Best fit:
Law firms and legal departments that want a flexible platform for litigation, investigations, and compliance matters.
Pros:
- User-friendly interface
- Strong collaboration tools
- Solid AI features
- Transparent pricing approach
Cons:
- Advanced functions may take time to learn
5. Xerox Legal Solutions
Xerox Legal Solutions, formerly associated with Kofax/Nuance document understanding tools, offers AI-powered intelligent document processing features that can support discovery workflows. These tools can extract data from unstructured files, classify documents, and identify key information during the intake and organization stages.
Why it is useful:
This type of technology is especially helpful when working with scanned documents, legacy records, or other formats that require more than standard review tools.
Best fit:
Large organizations, government agencies, and firms handling heavy document processing or complex extraction tasks.
Pros:
- Strong document extraction and classification
- Handles a wide range of formats
- Useful for repetitive preprocessing work
Cons:
- Not a full discovery review platform on its own
- May need to be paired with other eDiscovery tools
6. CASEpeer
CASEpeer is primarily legal practice management software, but it includes AI-powered document management and communication features that can support discovery preparation. It is not a dedicated discovery review platform, but it can help organize case materials and centralize information.
Why it is useful:
Keeping case files organized and searchable can reduce the time spent locating documents during discovery. For firms that already use CASEpeer, its workflow tools can support better document management before review begins.
Best fit:
Small to medium-sized firms, especially in high-volume practice areas such as personal injury.
Pros:
- Helps organize case files
- Improves document access
- Supports client communication
- Integrates with other tools
Cons:
- Not built for deep discovery review
- AI features are more general than specialized
How to Choose the Right AI Tool for Discovery Review
Choosing the right tool depends on your workflow, team size, and case demands. Key factors to consider include:
Case complexity and volume
Large, complex matters with diverse data types may require a more robust platform like RelativityOne or Everlaw. Smaller matters may be better suited to simpler tools like Logikcull or Disco.
Budget
Pricing can vary widely. Some tools charge by data volume, others by user, subscription, or project. Look at total cost, including setup, training, and support.
Ease of use
If your team needs a simple interface and minimal training, prioritize usability. More advanced platforms may offer more control but require more onboarding.
Integration
Check whether the platform works with your existing document management, case management, or review systems. Strong integrations can reduce friction and save time.
AI capabilities
Different tools excel in different areas. Some are strongest in predictive coding, while others are better for search, clustering, or document classification. Match the tool to your typical review needs.
Security and compliance
Legal data is sensitive, so security should be a priority. Look for encryption, access controls, audit features, and relevant compliance certifications such as SOC 2 where applicable.
Pricing and Value
AI discovery review tools are often worth the investment because they can reduce review time and improve efficiency. Still, it is important to understand how pricing works before committing.
Common pricing models include:
- Per gigabyte processed
- Per user license
- Subscription-based pricing
- Project-based pricing
When evaluating value, consider more than the upfront cost. Think about:
- Time saved on manual review
- Reduced billable hours
- Faster case preparation and resolution
- Lower risk of missing important documents
Most vendors offer demos or trials. Testing the platform with your own data is one of the best ways to see whether it fits your workflow.
How to Use AI for Discovery Review Effectively
To get the most out of AI in discovery, legal teams should treat it as part of a broader review strategy, not a stand-alone replacement for human review.
A practical approach includes:
- Defining review goals before data is loaded
- Using AI to prioritize and organize documents early
- Training the system with reviewer decisions where supported
- Monitoring quality throughout the review process
- Keeping attorneys involved in privilege, relevance, and strategy decisions
- Reviewing vendor workflows and security settings before deployment
AI is most effective when paired with clear review protocols and experienced legal oversight.
Frequently Asked Questions
How does AI actually review documents?
AI does not read documents like a person does. It uses natural language processing and machine learning to identify patterns, concepts, relationships, and likely relevance. In active learning workflows, the system improves as reviewers tag more documents.
Can AI replace human reviewers?
No. AI is best used to support human review, not replace it. Legal professionals are still needed for context, privilege calls, and final judgment.
What types of data can AI discovery tools handle?
Most modern tools can handle emails, office documents, PDFs, text files, images through OCR, chat logs, social media content, and sometimes audio or video files with transcription support.
How long does implementation take?
Cloud-based tools may be ready in days or weeks. More complex enterprise platforms can take longer, especially if they require customization or integration with other systems.
Is AI discovery review secure?
Reputable platforms use security measures such as encryption, access controls, and audit logs. Still, firms should review vendor security policies and confirm they meet internal and client requirements.
Can smaller firms use AI discovery tools?
Yes. Many tools are now designed to scale, and smaller firms can benefit from faster review, better organization, and reduced manual work if the pricing fits the matter.
Conclusion
AI for discovery review is no longer an emerging concept. It is already helping legal teams handle document-heavy matters more efficiently. By using the right tools, firms can reduce manual effort, improve consistency, and focus more time on strategy and legal judgment.
The best solution depends on your case size, workflow, budget, and technical needs. Whether you choose a full eDiscovery platform or a more focused AI-assisted tool, the goal is the same: make discovery faster, more accurate, and easier to manage.