E-Discovery Software in Legal (2026)

What e-discovery software covers

E-discovery software (also written eDiscovery) helps a legal team find, preserve, collect, process, review and produce electronic documents for a lawsuit, investigation or regulatory request. The industry's reference model, the EDRM, divides that work into stages such as identification, preservation, collection, processing, review, analysis and production. Products differ in how many of those stages they cover. Many records in this ranking are about document review, where vendors have added AI.

The ranking mixes four kinds of vendor, so check which kind a row describes. Some rows fit more than one.

Review platforms. Cloud platforms that take processed data through review and production, now with AI that suggests or predicts coding calls. The rows for Everlaw, Relativity and DISCO describe platforms of this kind.

Self-service tools. Logikcull, which Reveal owns, describes "self-service automation" that lets a legal team run a matter from collection through production, and its Delicato page says the team worked "without technical expertise or outside support". The Delicato record below involved more than 2,100 documents.

Managed services and implementation partners. Consilio and Epiq, in the partner table above, sell e-discovery services alongside their own AI tools. Lighthouse lists both managed services and a self-service offering. With these vendors, the people running your matter are part of what you buy.

Data-risk suites. Exterro puts e-discovery inside a wider data risk platform with legal hold and privacy tools.

What the documented deployments report

Five records, with figures as each source states them. They measure different things, and each entry says who wrote the source.

Arnold & Porter used Relativity aiR for Privilege on an antitrust matter. Bryan Marra, a senior attorney at the firm, wrote the account for the firm's blog on April 8, 2026. He reports savings of "approximately 75% in this case" against the estimated cost of a full linear review with contract attorneys, on a dataset of roughly 500,000 documents. A firm attorney spent close to two weeks up front classifying the law firms, attorneys and third parties in the set. He adds that Relativity charges per document run through the AI model, so it "may not be cost-effective in matters with large document sets or where significant human review and QC will be necessary" (case study).

Orrick tested Everlaw's Coding Suggestions on about 10,000 documents in an IP case. Everlaw wrote this account, dated February 6, 2025, from an Orrick attorney's talk at Everlaw's summit. The attorney, Cal Yeaman, said the AI was "more accurate than human review by a statistically significant margin" and that reviewers reversed one document among those it called not relevant. He estimated review cost fell by more than 50% (case study).

Dinsmore & Shohl reviewed 26,000 contracts for security-incident notice clauses. Everlaw's write-up (March 24, 2026) says search removed about 20,000 contracts and a first AI pass reached about 92% recall. Two more passes added just over one percentage point, and a predictive coding layer plus sampling brought final recall to 98.2%. The firm estimated manual review at about 260 attorney hours. The write-up gives no precision figure or validation sample size (case study).

A plaintiffs' co-counsel team used Casepoint to test a defendant's production of 3.5 million documents. Casepoint's case study says the defense's control set estimated the production held 85% of responsive documents at 58% precision. The plaintiffs' validation sample put the share of responsive documents captured at over 97% and precision at 16.7%. Casepoint says that left about 600,000 responsive documents out of 3.5 million. The defendants confirmed an error, and the court granted the plaintiffs' motion to extend. Casepoint credits its TAR 2.0 features. The page does not show how the defense's control set was built or how the plaintiffs' sample was drawn, so the gap cannot be traced to a specific tool (case study).

Delicato Family Wines used Logikcull. Logikcull's page says the in-house team's review and tagging of more than 2,100 documents went from up to 10 to 14 days to under 7 hours, which it calls 15x. The page does not say whether these are calendar days or working hours, or how many attorney hours either approach took (case study).

What this evidence cannot tell you

Vendors wrote most of it. As of November 2026, 27 of the 44 records cite the vendor's own website, and 34 of 44 carry no source publication date. Everlaw has the most records, 9 of 44, about 20%. The counts show what was published, not market share or quality.

Headline figures are often undefined. The 99.9% privilege accuracy in the Relativity row comes from a Relativity blog post first published in 2022 (republished September 2023) on Text IQ for Privilege, a tool Relativity has since renamed, run on 225,000 documents (case study). The post says "documented accuracy of 99.9 percent" and does not say what accuracy was measured against. Dinsmore's write-up reports recall without precision.

Savings are usually estimates. Arnold & Porter's 75%, Orrick's 50% and Dinsmore's 260 hours compare a real review with one that did not happen. A Consilio case study, which is undated, says predictive coding set aside 89% of a roughly 200,000-document set and the client saved "at least $209,000". That is figured at an average cost of about $1.25 per document for first-level review. The page also says that on re-review the associate overturned 72% of his own coding decisions, without making clear which set of documents that figure covers. It ties this to coding document families without reading each document, which explained why a second round of training made only marginal gains.

Nothing here is a head-to-head test. None of the five records above compares two platforms on the same documents.

What the listed vendors publish about price

Most vendors do not publish prices. In the rows checked on November 5 and 6, 2026, four of the twelve vendors show no pricing: Everlaw, Consilio, Exterro and KLDiscovery. Casepoint's row says pricing is tailored to the applications and use cases bought, with no units or dollar figures. No row quotes a dollar price. Lighthouse's rate card, linked below, is the exception: it lists dollar rates.

The others describe how they bill, and the units differ. For example:

• Relativity: a subscription based on a committed data volume, pay as you go or on a one- or three-year commitment, with Legal Hold, FOIA, breach response and translation licensed separately (pricing page).
• DISCO: per GB of processed data for the platform, with AI included, and per document for Auto Review (pricing page).
• Logikcull: a pay-as-you-go plan charged per GB of data per month, and annual subscription plans (pricing page).
• Lighthouse: a published 2025 rate card, part of its Master Spectra Subscription Agreement, prices additional services per GB, hour or document unless the order form says otherwise (rate card).

Two records show why the unit matters. Arnold & Porter's April 2026 account describes a per-document charge for aiR. Relativity's pricing page, checked November 6, 2026, now says aiR Review, Privilege and Case Strategy "come with your data volume, with no separate AI licenses and no per-user fees", so ask whether that applies to the plan or service provider you would contract through. Delicato's page credits flat-rate pricing for cost clarity but does not say which plan it used. Logikcull's pricing page lists a per-GB monthly plan alongside annual subscriptions and storage- or matter-based plans, so ask which plan produced the result.

Questions to ask in a demo

• What does the quoted percentage measure, and against what baseline? Recall, precision, elapsed time and cost against an estimate are different claims.
• Can you report recall and precision from a validation sample of our own documents, not recall alone?
• What is the billing unit for storage, processing, hosting and AI review, and what happens to the bill when the data set doubles?
• Who trains and checks the model, and how many hours of attorney time does that take before review starts?
• Where does our data go when AI is used? Arnold & Porter's post says to check whether client data is retained or used for model training, and whether client notification is warranted.
• Which reference customers are independent of you, and who wrote their published results?
• Which sources can you collect from, such as Slack, Microsoft Teams or Google Workspace?

How to use the ranking

Start with the e-discovery use case and the two or three records closest to your matter size and type. Read the page each one cites, check what the figure measures and who wrote it, then put the demo questions to the vendors. A vendor in the "Also listed" table has no published case study linked to a published profile here, and the same questions apply to it. For other software categories, see the software directory.