AI for Discovery Review: Streamlining Your Legal Workflow
The discovery phase of litigation is often one of the most time-consuming and expensive parts of a case. Legal teams must review large volumes of documents, emails, spreadsheets, and other electronically stored information to find relevant evidence, identify privileged material, and prepare for production.
That work can quickly become overwhelming when datasets grow large. Manual review is slow, repetitive, and vulnerable to inconsistency. AI tools for discovery review are designed to reduce that burden by helping legal professionals process data faster, find relevant documents more efficiently, and manage review with greater consistency.
This guide explains how to use AI for discovery review, what it can do, which tools are commonly used, and what to consider before adopting it in your practice.
Why AI Matters in Discovery Review
In modern litigation, the volume of data often exceeds what human reviewers can handle efficiently within typical deadlines and budgets. Reviewing thousands or even millions of documents by hand can lead to fatigue, missed issues, and uneven coding decisions.
AI-powered discovery review tools help address these problems by:
- Processing large datasets much faster than manual review
- Identifying patterns, themes, and likely relevant documents
- Classifying documents by relevance, responsiveness, privilege, or similarity
- Reducing repetitive review work
- Improving consistency across large document sets
For legal teams, the practical benefits include:
- Lower review costs by reducing manual hours
- Faster turnaround times during discovery
- More consistent coding decisions across reviewers
- More time for strategy, analysis, and client communication
- Better risk management by helping teams surface important documents earlier
Used well, AI does not replace legal judgment. It supports it by handling the high-volume, repetitive work that slows discovery teams down.
Best AI Tools for Discovery Review
The best tool depends on your case size, workflow, and review goals. Some platforms are built for end-to-end eDiscovery. Others are better suited for targeted document analysis or contract review.
1. RelativityOne
RelativityOne is a cloud-based eDiscovery platform with advanced AI capabilities, including Technology Assisted Review (TAR), also known as Continuous Active Learning (CAL). It supports processing, organizing, review, and production in one platform.
Why it is useful:
RelativityOne is built for full-scale discovery workflows. Its AI learns from reviewer input and improves its predictions as review continues. It also includes analytics, clustering, and concept searching to help teams uncover patterns in large datasets.
Best for:
Law firms and legal departments handling complex, data-heavy litigation that requires precision, scalability, and strong review controls.
Pros:
- Comprehensive eDiscovery platform
- Strong TAR/CAL functionality
- Robust security and compliance features
- Broad integration ecosystem
- Cloud-based access
Cons:
- Can take time to learn
- May be costly for smaller firms
2. Everlaw
Everlaw is a cloud-based eDiscovery platform known for usability, collaboration, and strong analytics. It includes AI-assisted review features and tools for organizing case narratives and reviewing large document sets.
Why it is useful:
Everlaw’s AI helps teams identify responsive documents with less manual effort. The platform is designed to be intuitive, making it easier for teams to get started while still supporting more advanced review needs.
Best for:
Firms that want a user-friendly platform with strong AI support and collaboration features.
Pros:
- Intuitive interface
- Effective TAR implementation
- Strong collaboration tools
- Fast performance
- Good customer support
Cons:
- May not offer as many niche features as some enterprise platforms
- Pricing may be a consideration for very small practices
3. Logikcull
Logikcull, now part of Disco, focuses on speed and simplicity. It supports AI features such as auto-tagging, auto-redaction, and document similarity identification.
Why it is useful:
Logikcull is designed to help teams ingest, cull, and review data quickly. It is useful for early case assessment and fast-moving matters where streamlined review matters more than deep customization.
Best for:
Small to mid-sized teams that need a fast, accessible solution for early review and document culling.
Pros:
- Fast processing and review
- Easy to use
- Useful automation for common tasks
- Good value for many teams
Cons:
- Less control over model training than dedicated TAR platforms
- Advanced analytics may be more limited
4. ZyLAB ONE
ZyLAB ONE is an integrated legal discovery platform that uses AI for document review, intelligent search, and automated redaction. It can identify keywords, concepts, and sentiment to help teams analyze large document sets more efficiently.
Why it is useful:
ZyLAB ONE supports more nuanced discovery than keyword search alone. Its AI features help teams understand content and context across varied data types within a single review environment.
Best for:
Teams that need an all-in-one platform with strong search and content analysis capabilities.
Pros:
- Strong AI for search and content analysis
- Handles a wide range of data types
- End-to-end eDiscovery workflow
- Scalable
Cons:
- Can be complex to learn
- May be expensive for some firms
5. Kira Systems
Kira Systems is not a full eDiscovery platform. It focuses on AI-powered contract analysis and due diligence, using machine learning to extract provisions, clauses, and key data points from legal documents.
Why it is useful:
Kira is highly effective for reviewing contracts at scale. It can help identify specific clauses and terms much faster than manual review, making it valuable for M&A due diligence, compliance work, and contract management.
Best for:
Transactional lawyers, in-house legal teams, and due diligence workflows focused on contract review rather than litigation discovery.
Pros:
- Strong contract review and data extraction
- Highly effective in its niche
- Reduces manual review time
- Can integrate with other platforms
Cons:
- Not designed for broad litigation review
- Focused mainly on contracts
- Requires setup and tuning
6. CASEpeer
CASEpeer is primarily a legal practice management platform, but it has incorporated AI features that can assist with document summarization and identifying key case elements.
Why it is useful:
For smaller firms, having AI features inside a familiar practice management system can make adoption easier. It can help surface important details for case assessment and client communication without requiring a dedicated eDiscovery platform.
Best for:
Small to mid-sized plaintiff firms or general practice firms that want basic AI-assisted document organization and insights.
Pros:
- Integrated into a practice management workflow
- Easy for existing CASEpeer users
- Useful for basic document analysis and case insights
Cons:
- Less sophisticated than dedicated eDiscovery tools
- Not built for complex or large-scale discovery
- Limited advanced review functionality
How to Use AI for Discovery Review
If you are evaluating how to use AI for discovery review in your own workflow, the process usually follows a few core steps:
1. Define the review objective
Start by clarifying what you need AI to help with. Common goals include:
- Finding responsive documents
- Identifying privileged material
- Prioritizing review
- Detecting duplicates or near-duplicates
- Grouping documents by topic or theme
- Extracting specific clauses or data points
The clearer the objective, the easier it is to choose the right tool and workflow.
2. Prepare and organize the data
AI works best when the underlying data is cleaned and organized. Before review begins, make sure the dataset is properly collected, processed, and de-duplicated where appropriate.
This step helps reduce noise and improves the quality of AI-assisted results.
3. Train the system with reviewer input
For tools that use TAR or CAL, human reviewers code a sample set of documents. The system then uses those decisions to predict which other documents are likely relevant.
As review continues, the model learns from new coding decisions and refines its predictions. This allows the team to focus on the most promising documents first.
4. Use AI to prioritize and narrow review
AI can help rank documents by likelihood of relevance, surface similar materials, and highlight themes across the dataset. This lets legal teams prioritize high-value documents and reduce time spent on low-probability material.
5. Validate results with human review
AI should support review, not replace it. Lawyers and legal reviewers should validate key results, check for missed documents, and make final calls on privilege, responsiveness, and production.
Choosing the Right AI Tool for Discovery Review
The right platform depends on the size of the matter, the complexity of the data, and how your team works. Consider the following:
- Scale and complexity of the dataset: Large litigation matters usually require a robust end-to-end platform such as RelativityOne or Everlaw. Smaller matters may work well with simpler tools like Logikcull.
- Type of work: Litigation discovery and contract review are not the same. Kira Systems is better suited to contracts and due diligence.
- Team experience: Some tools are built for ease of use, while others require more training and configuration.
- Budget: Pricing varies widely, so it is important to evaluate both upfront cost and long-term value.
- Integration needs: Consider how the tool will fit with your document management, case management, and review workflow.
- Collaboration requirements: If multiple reviewers are involved, look for strong permissions, audit trails, and version control.
In many cases, the best approach is to start with a platform that balances usability and capability. Demos and trials can be useful for testing a tool with real data before making a decision.
Pricing and Value Considerations
AI discovery review tools may be priced in several ways:
- Per-matter fees: A fixed price for a specific case
- Per-user subscriptions: Recurring fees based on the number of users
- Consumption-based pricing: Charges based on data volume processed, stored, or reviewed
Each model has tradeoffs:
- Per-matter pricing can be predictable but may be less efficient for very large matters
- Per-user pricing works well for teams with stable staffing
- Consumption-based pricing may be attractive for smaller datasets but can become expensive at scale
When comparing tools, look beyond the sticker price. The real value comes from time saved, reduced review labor, improved consistency, and the ability to handle more matters with the same team.
Frequently Asked Questions About AI for Discovery Review
How accurate are AI tools for discovery review compared to human reviewers?
AI tools that use TAR or CAL can be highly effective for identifying relevant documents across large datasets. They apply consistent logic and are not affected by fatigue in the same way human reviewers are. That said, AI still depends on human input for training, oversight, and final review decisions.
Is it difficult to learn and implement AI discovery review tools?
It depends on the platform. Some cloud-based tools are designed to be easy to adopt, while more advanced platforms may require additional training. Most vendors provide onboarding support, training materials, and demos.
Can AI tools handle all types of electronic data?
Most modern discovery platforms can work with emails, word processing files, spreadsheets, PDFs, images, and other common ESI formats. However, data compatibility and extraction quality can vary, especially with complex or corrupted files.
Does using AI for discovery review mean lawyers are no longer needed?
No. AI is a support tool, not a replacement for legal judgment. Lawyers remain essential for strategy, privilege decisions, relevance analysis, and final review sign-off.
How can I protect confidentiality when using cloud-based AI tools?
Look for vendors with strong security controls such as encryption, access controls, audit logs, and recognized compliance standards. Review the platform’s security and privacy policies carefully before use.
What is Technology Assisted Review?
Technology Assisted Review, or TAR, is a process in which AI learns from human coding decisions. Reviewers label a sample set of documents, and the system uses that input to predict which remaining documents are likely relevant. As the review continues, the model improves its predictions based on additional feedback.
Conclusion
AI is now a practical part of discovery review for many legal teams. It can reduce manual workload, improve consistency, and help reviewers focus on the most important documents sooner.
If you are evaluating how to use AI for discovery review, the key is to match the tool to the task. Consider your dataset size, workflow, budget, and team experience before choosing a platform. The right solution can make discovery faster, more efficient, and easier to manage without losing the legal judgment that review still requires.