The Definitive Guide: How to Use AI for Discovery Review
The legal landscape is changing fast, and discovery is one of the areas feeling the pressure most. Review teams are expected to process massive volumes of emails, chats, documents, and other electronically stored information quickly, accurately, and cost-effectively. Manual review alone is often too slow, too expensive, and too inconsistent.
That is where AI can help.
If you are researching how to use AI for discovery review, the goal is not to replace legal judgment. It is to reduce review volume, improve prioritization, and help your team focus on the documents that matter most. In this guide, we cover why AI matters, leading tools to consider, how to choose the right platform, and what pricing typically looks like.
Why AI for Discovery Review Matters
Discovery review is one of the most resource-intensive parts of litigation. Teams often have to sort through millions of records to identify relevant, responsive, and privileged material. That work is repetitive, expensive, and prone to human inconsistency.
AI-powered discovery tools can make the process more manageable by:
- ranking documents by likely relevance
- clustering similar content
- identifying concepts instead of relying only on keywords
- flagging potentially privileged material
- speeding up early case assessment
- reducing the amount of manual review required
The practical benefit is straightforward: your team spends less time on repetitive document sorting and more time on case strategy, legal analysis, and client service. Used well, AI can improve efficiency without sacrificing quality.
Best AI Tools for Discovery Review
The right platform depends on your workflow, team size, and the type of matters you handle. Below are some of the leading tools used in discovery review.
1. RelativityOne
What it does: RelativityOne is a full eDiscovery platform with built-in AI features such as Active Learning and Technology Assisted Review (TAR). It supports data processing, document organization, review workflows, and analytics in one cloud-based environment.
Why it is useful: RelativityOne is designed for complex, high-volume matters. Its AI tools help prioritize documents for review, learn from reviewer decisions, and reduce the volume of documents needing manual attention.
Best fit: Large law firms, corporate legal departments, and government teams handling complex litigation, investigations, or regulatory matters.
Pros:
- Broad feature set beyond AI review
- Strong Active Learning and TAR capabilities
- Scalable cloud infrastructure
- Powerful analytics and visualization tools
- Mature ecosystem and support network
Cons:
- Can take time to learn and implement
- Often more expensive than narrower tools
- May be more platform than some smaller teams need
2. Everlaw
What it does: Everlaw is a cloud-native eDiscovery platform known for its user-friendly design and AI-assisted review features. It offers conceptual search, clustering, predictive coding, timeline tools, and collaboration features.
Why it is useful: Everlaw makes sophisticated discovery tools easier to use. Its AI features help reviewers find relevant material faster and work more efficiently across teams.
Best fit: Mid-sized to large firms and in-house teams that want a modern interface with strong AI functionality.
Pros:
- Intuitive interface
- Effective clustering and conceptual search
- Strong timeline and visualization tools
- Good collaboration features
- Responsive support
Cons:
- Some specialized workflows may require more customization than the platform offers
- Pricing can rise with large datasets or longer matters
3. Logikcull, now part of Disco
What it does: Logikcull, now integrated into DISCO’s platform, focuses on fast processing and intelligent document triage. It supports deduplication, near-duplicate detection, and AI-assisted sorting to reduce review volume early.
Why it is useful: It is especially effective for getting large datasets into a reviewable state quickly. That makes it a strong option for early case assessment and first-pass document filtering.
Best fit: Teams that need fast data ingestion and early-stage triage, especially where speed matters more than deep customization.
Pros:
- Fast ingestion and processing
- Strong deduplication and near-duplicate handling
- Useful for early case assessment
- Easy to adopt
Cons:
- More focused on triage than deep analytical review
- Feature set may continue to evolve within the larger DISCO ecosystem
4. DISCO AI
What it does: DISCO AI uses machine learning and natural language processing to support search, clustering, and predictive coding across eDiscovery workflows.
Why it is useful: DISCO AI is built to help teams identify relevant documents faster and surface themes that may be missed in manual review. Its interface is designed to be approachable, even for teams that are new to AI-assisted discovery.
Best fit: Firms of any size looking for a modern, AI-forward platform for culling, relevance review, and thematic analysis.
Pros:
- Strong relevance and thematic analysis tools
- User-friendly design
- Fast cloud-based workflow
- Good fit for teams new to AI discovery tools
Cons:
- Some advanced customization may be less extensive than in older enterprise systems
- Pricing varies by usage and feature set
5. Onna
What it does: Onna is a data integration and knowledge management platform that pulls information from cloud apps, local drives, and communication tools into a unified search environment. Its AI helps identify and contextualize data across multiple sources.
Why it is useful: Many discovery problems start with data fragmentation. Onna helps legal teams locate and connect information across systems, which is valuable during early case assessment and custodian identification.
Best fit: Organizations with data spread across multiple platforms, especially in regulated industries or complex investigations.
Pros:
- Strong data-source integration
- Useful for contextual search across silos
- Good for early case assessment
- Scalable and secure
Cons:
- Often used alongside a separate review platform
- Integrations can take time to configure
6. Luminance
What it does: Luminance is an AI-powered legal document review platform best known for contract review and due diligence, though it can also support discovery-related review tasks. It identifies clauses, anomalies, and legal-language patterns across large sets of documents.
Why it is useful: Luminance is especially helpful when the review involves structured legal documents. It can highlight deviations from standard language and reduce the time spent on repetitive analysis.
Best fit: Law firms and in-house teams handling contract review, due diligence, or other structured document-heavy work.
Pros:
- Strong at legal-language analysis
- Excellent for contract review and due diligence
- Fast review of large document sets
- Helps reduce manual effort
Cons:
- Less suited to broad, unstructured litigation review than dedicated eDiscovery platforms
- Pricing may depend on volume and feature usage
How to Choose the Right AI Tool for Discovery Review
There is no single best tool for every team. The right choice depends on the type of matters you handle, the size of your data sets, and how your team works.
Consider these factors:
1. Case complexity and document volume
For large, complex litigation, a full-featured platform such as RelativityOne may be the best fit. For teams that want ease of use and strong built-in AI, Everlaw or DISCO AI may be more practical. If the challenge is finding relevant data across multiple systems, Onna can help during the early stages.
2. The type of AI functionality you need
Some tools focus on predictive coding and relevance ranking. Others are better at clustering, conceptual search, or contract analysis. Match the tool to the task rather than choosing based on brand alone.
3. Workflow integration
The platform should fit into your existing process for ingestion, review, quality control, and production. If it adds unnecessary friction, the efficiency gains can disappear quickly.
4. Ease of use and training
Some platforms require more setup and training than others. If your team is new to AI-driven discovery, prioritize a platform with a clear interface and good support.
5. Scalability and deployment model
Most modern tools are cloud-based, but not all are built the same. Make sure the platform can handle your expected data volumes, number of users, and security requirements.
6. Pricing structure
Pricing can be based on data volume, user licenses, subscriptions, or project fees. Understanding the model early helps you avoid surprises later.
7. Vendor support and reliability
Look for a vendor with a strong track record, responsive support, and ongoing product development. Discovery projects move fast, and platform support matters.
Pricing and Value Considerations
AI discovery review tools can range from relatively affordable point solutions to enterprise platforms with significant implementation and usage costs. Pricing often depends on:
- per-gigabyte or per-terabyte storage and processing
- per-user licensing
- per-project or flat-fee engagement
- monthly or annual subscriptions
The real question is not just what the tool costs, but what it saves.
AI can reduce review hours, shorten timelines, and lower the amount of manual work required. That can translate into lower overall discovery spend, fewer errors, and faster case progression. For high-volume matters, those savings can be substantial.
When evaluating cost, consider:
- time saved on first-pass review
- reduction in reviewer hours
- decreased risk of inconsistent coding
- faster identification of key documents
- potential savings from earlier case resolution
A platform with a higher upfront cost may still deliver better value if it significantly reduces review time and improves workflow efficiency.
Frequently Asked Questions About AI for Discovery Review
Is AI capable of replacing human reviewers entirely?
No. AI can help identify patterns, rank documents, and surface potentially relevant material, but human review is still necessary for legal judgment, context, and privilege determinations.
How accurate is AI in identifying relevant documents?
Accuracy has improved significantly, especially with machine learning and Active Learning workflows. Results depend on the quality of the data, how the system is trained, and how well reviewers validate outputs.
What training is needed to use AI for discovery review?
Training needs vary by platform. Some tools are easy to adopt with basic instruction, while more advanced systems may require admin-level training and workflow setup. Most vendors provide onboarding and support.
Can AI help identify privileged documents?
Yes. AI can flag documents that may contain privilege indicators or attorney-client communication patterns. Final privilege review should still be performed by qualified legal professionals.
How do I ensure data security when using cloud-based AI tools?
Choose a vendor with strong encryption, access controls, and recognized security practices. Ask about data handling, compliance standards, and audit procedures before uploading sensitive material.
What are the ethical considerations?
Use AI in a way that supports, rather than replaces, professional responsibility. Be mindful of confidentiality, transparency with clients, and the need to check for bias or workflow errors.
Conclusion
If you are evaluating how to use AI for discovery review, the key is to treat AI as a practical workflow tool, not a replacement for legal judgment. The right platform can help you reduce manual review, improve document prioritization, and manage large data sets more efficiently.
Tools like RelativityOne, Everlaw, DISCO AI, Onna, and Luminance each serve different discovery needs. The best choice depends on your case type, team structure, budget, and workflow goals.
Used thoughtfully, AI can make discovery review faster, more consistent, and more cost-effective, while helping your team focus on the work that drives better outcomes for clients.