6 AI eDiscovery Software Platforms Compared for Legal Teams

AI eDiscovery software uses machine learning and generative AI to help legal teams collect, cull, review and produce electronically stored information.

The six platforms compared here are RelativityOne, Everlaw, DISCO, Reveal, Logikcull and Microsoft Purview eDiscovery. They differ most in how AI review works, privilege support, and where data can be hosted.

AI review still needs lawyer supervision and a validation method you can defend. Use this comparison to build a shortlist, then pilot on your own data.

QC note: Requires attorney review before publishing. This article is general information, not legal advice, and does not guarantee compliance with any court order, rule or ethics obligation.

How We Evaluated These Platforms

We read each vendor's current product pages and help documentation on November 2, 2026, and reviewed the pages ranking for this topic. We did not run tests or receive vendor data. Each platform was compared on the same criteria:

• AI review: generative or predictive review features and whether outputs include explanations or citations

• Privilege support: documented tools for privilege prediction or logs

• Scope: which stages of the EDRM-style workflow the platform covers

• Deployment: cloud, private or in-tenant options documented by the vendor

• Fit: the matter sizes and teams it is commonly positioned for

The order is not a ranking. Pricing is quote-based or changes often, so it is not compared.

Quick Comparison

1. RelativityOne (aiR Review and Privilege)

Relativity's help documentation, last modified October 29, 2026, says aiR for Review is now called aiR Review.

It uses generative AI to examine document text against prompt instructions, explain why a document matters and cite passages. A separate Privilege product predicts whether documents may be privileged and drafts privilege log descriptions.

• Fits: large or complex matters and teams that work with Relativity partners

• Check: administration skills needed and how prompt criteria are documented for validation

• Docs: Relativity aiR Review documentation

2. Everlaw

Everlaw's AI product page lists Deep Dive, Coding Suggestions, Writing Assistant, Review Assistant, Predictive Coding and Clustering, positioned as AI grounded in case evidence. Everlaw lists ediscovery, early case assessment, legal holds and deposition use cases.

• Fits: law firms, corporations and public-sector teams wanting one cloud platform

• Check: how AI suggestions are logged and exported for validation

• Docs: Everlaw AI

3. DISCO

DISCO describes an AI-powered ediscovery platform with Cecilia AI, Auto Review, deposition management, timelines and legal hold.

• Fits: litigation teams that want review, depositions and case timelines in one product

• Check: how Auto Review decisions are sampled and documented

• Docs: DISCO

4. Reveal

Reveal describes AI-driven ediscovery with Reveal AI, AI analytics, legal holds and a Reveal Private Deployment option hosted where the customer chooses. Reveal also lists Logikcull among its products.

• Fits: organizations needing private hosting or complex investigations

• Check: which AI features are available in private deployment

• Docs: Reveal

5. Logikcull

Logikcull describes end-to-end discovery automation, Logikcull AI for fact-finding, legal holds and collection from collaboration sources through Onna. It is part of the Reveal product family.

• Fits: smaller matters, in-house teams and public records requests

• Check: limits on complex workflows compared with larger platforms

• Docs: Logikcull

6. Microsoft Purview eDiscovery

Microsoft Purview eDiscovery identifies, holds, reviews and exports content across Exchange Online, Teams, OneDrive, SharePoint and other Microsoft 365 services. Features depend on licensing.

• Fits: corporate legal and compliance teams preserving and searching Microsoft 365 data before export to a review platform

• Check: license tier and whether its review features meet your matter needs

• Docs: Microsoft Purview eDiscovery

Our own product: Lexintis is LuMay's legal AI agent, so it is not ranked here. LuMay's legal page describes privilege-aware document review with citations preserved for every recommendation. It is not a full eDiscovery platform; check fit before comparing.

What the Rules and Ethics Guidance Expect

• Proportionality: Federal Rule of Civil Procedure 26(b)(1) limits discovery to what is relevant and proportional to the needs of the case

• Privilege claims: Rule 26(b)(5)(A) requires describing withheld documents enough for the other side to assess the claim

• Waiver protection: Federal Rule of Evidence 502 addresses inadvertent disclosure and court orders on non-waiver

• Lawyer duties: ABA Formal Opinion 512 (2024) discusses competence, confidentiality, communication, supervision and fees when lawyers use generative AI

Courts and local rules vary. Confirm the rules for your jurisdiction and any ESI protocol in your matter.

How to Validate AI Review

AI review is only defensible if you can show it worked. A common approach:

Stage 1: Agree the protocol. Document the review criteria, prompts or training rounds, and who signs off.

Stage 2: Sample. Draw random samples from documents the AI marked relevant and not relevant.

Stage 3: Measure. Estimate recall and precision with confidence intervals from attorney-coded samples.

Stage 4: Record. Keep model versions, settings, samples and decisions in case the process is challenged.

Our guide to AI legal document review accuracy walks through a worked recall calculation.

Pilot Plan

Illustrative example. A litigation team pilots two shortlisted platforms on a closed matter with known coding decisions.

• Data: 20,000 documents already coded by attorneys

• Test: each platform runs AI review with the same criteria

• Score: recall and precision against attorney coding, time to first review set, and export quality

• Check: confidentiality terms, data retention and whether client data trains models

Numbers are illustrative. Use a matter where coding is reliable, or the comparison measures your old errors.

Common Mistakes to Avoid

• Buying on demos: vendor sample data rarely looks like your custodians' mail

• Skipping validation: an AI review without samples is hard to defend

• Treating privilege as relevance: privilege needs separate checks and a log

• Ignoring data terms: confirm retention, location and model-training terms for client data

• No ESI protocol discussion: agree on AI use with opposing counsel where needed

Limitations

This comparison relies on vendor documentation, which describes features rather than proving results. Features marked Not verified were not confirmed in documentation we read. Requires attorney review before publishing.

Next Step

If your team needs privilege-aware review with citations alongside your existing platform, see Lexintis legal AI by LuMay. For broader review tools, see our list of AI for legal document review.

• Relativity Privilege documentation

• Everlaw AI

• DISCO

• Reveal

• Logikcull

• Microsoft Purview eDiscovery

• Federal Rule of Civil Procedure 26 (Cornell LII)

• Federal Rule of Evidence 502 (Cornell LII)

• ABA Formal Opinion 512