Law firms can use AI for document review and eDiscovery by deploying machine learning platforms that classify documents for responsiveness, privilege, and confidentiality automatically. DecoverAI provides a complete AI-powered workflow — from document ingestion through Bates-stamped production — that reduces attorney review time by up to 80% and completes most matters in under an hour.
AI document review is the application of machine learning models to the task of reading, classifying, and tagging legal documents at scale. In a traditional eDiscovery workflow, teams of contract attorneys work through document populations one record at a time, applying tags for responsiveness, privilege, and confidentiality based on written review guidelines. This is accurate when done carefully, but it is expensive, slow, and prone to inconsistency across a large reviewer population.
AI review inverts this model. A trained classification model reads every document in the corpus and assigns tags based on patterns it has learned from thousands of prior legal matters and the specific case instructions it receives. Human attorneys then concentrate their time on the documents the AI flags as uncertain, borderline, or high-priority — rather than reading everything. The result is that attorney judgment is applied where it is most valuable, not uniformly across the full population.
There are three main approaches to AI document review in use today:
The workflow below reflects how law firms using DecoverAI move from raw ESI to a completed production. Each step is designed to keep attorneys in control of legal judgment while AI handles high-volume classification work.
Gather electronically stored information from all relevant custodians: email archives (PST, EML), cloud storage (Google Drive, OneDrive, SharePoint, Dropbox), collaboration tools (Slack, Teams), hard drives, and document management systems like iManage. Upload to DecoverAI, which automatically deduplicates the document set, reconstitutes email families (attachments grouped with their parent emails), and indexes every document for AI classification. Custodian assignment and legal hold tracking are configured at this stage.
Configure the AI with the case's responsiveness criteria, privilege basis, and any confidentiality designations required by the protective order. DecoverAI's generative AI classifier reads every document in the corpus and assigns tags — responsive, non-responsive, potentially privileged, confidential — with a confidence score for each. The full corpus is classified in parallel; a 30,000-document matter typically completes classification in under three hours. Results are immediately available for attorney review.
Attorneys review the AI's classifications through the platform's review interface. The workflow surfaces responsive and potentially privileged documents first, and highlights documents where the AI's confidence score is low. Attorneys confirm or override each tag; overrides are logged and can be used to improve future classification runs. This step is where attorney judgment on borderline privilege calls, case-specific responsiveness nuance, and confidentiality tier assignments is applied. The time investment is a fraction of a traditional linear review because most clear-cut documents are already accurately classified.
For responsive documents that contain privileged or confidential passages, attorneys apply redactions to specific regions using the platform's redaction tool. Redactions are stored as annotations on the original document and burned into the production copy at export time — the native file is never altered. Once redactions are complete, the platform compiles the production set: applying Bates numbers in the specified format, generating the production load file (DAT and OPT), and organizing documents according to the agreed production protocol.
DecoverAI drafts the privilege log automatically from its own classifications and the attorney review record. Each withheld or redacted document generates a log entry pre-populated with date, author, recipient list, document type, and the basis for withholding. Attorneys review and finalize each entry. The completed privilege log exports in the format required by the jurisdiction or agreed protocol. The production set and privilege log are then delivered to opposing counsel or the requesting party via secure file transfer or directly to their review platform.
The short answer is yes — when properly validated and documented. Courts across federal and state jurisdictions have accepted AI-assisted review in eDiscovery, and the leading case law now treats it as an established methodology rather than an experimental one.
The foundational case is Da Silva Moore v. Publicis Groupe (S.D.N.Y. 2012), in which Magistrate Judge Andrew Peck approved predictive coding as an acceptable form of document review under the Federal Rules of Civil Procedure, noting that it is "more cost-effective, more accurate, and superior to the review method most commonly used." Subsequent cases including Global Aerospace v. Landow Aviation and Hyles v. New York City have reinforced this position, while drawing a key line: the reasonableness and proportionality of the chosen methodology must be demonstrable.
The governing standard is FRCP 26(g), which requires certifying counsel to attest that their discovery responses are complete and correct to the best of their knowledge after a reasonable inquiry. AI review satisfies this standard when the following elements are present:
Practical note: Courts are increasingly skeptical of parties who refuse to use AI review in large-volume matters and then claim cost burden as grounds for limiting production. The availability of accurate, low-cost AI review has shifted proportionality analysis. Opposing counsel and courts expect sophisticated parties to use available technology.
DecoverAI generates a validation report for each matter that documents classification methodology, confidence score distributions, sampling results, and the attorney review audit trail — giving producing parties a ready-made record to support any challenge to their review process.
Document review is consistently the largest cost center in eDiscovery, accounting for 70–80% of total matter spend in document-intensive litigation. AI review compresses this cost substantially. The table below compares the three main review approaches on a per-document basis and across key capability dimensions.
| Review Method | Cost per Document | Time to Complete 30k Docs | Privilege Log | Bates + Production | Audit Trail |
|---|---|---|---|---|---|
| Manual attorney review | $3–$8 | 2–4 weeks | Manual | Manual / vendor | Limited |
| Traditional TAR platform | $1–$3 | 1–2 weeks | Semi-automated | Vendor processing | Moderate |
| DecoverAI (AI-native) | from $0.02 — $60/GB | Under 3 hours | Auto-generated | Built-in, Bates-stamped | Full audit log |
Case study: A litigation team using DecoverAI on a 30,000-document matter completed document classification, attorney review, and a Bates-stamped production in 3 days — compared to a 6-week manual review on a comparable prior matter. Total review cost decreased by 98%. The AI-generated privilege log required less than two hours of attorney editing before production.
The $60/GB pricing model means costs scale linearly with data volume rather than document count, which benefits matters with large numbers of small documents (email-heavy dockets, in particular). A 1 GB dataset containing 30,000 average-sized emails costs $60 total to classify — versus up to $240,000 at $8 per document for manual review.