eDiscovery Document Review

eDiscovery document review is the process of identifying, organizing, and assessing electronically stored information—such as emails, chats, documents, and files—for litigation, investigations, and regulatory matters. At scale, this traditionally requires large teams of lawyers and reviewers to manually sift through millions of items to determine relevance, privilege, and risk, which is slow, extremely costly, and prone to human error. Modern systems apply advanced automation to prioritize, classify, and filter documents so that humans review a much smaller, higher‑value subset. These tools rank likely‑relevant materials, flag potentially privileged or risky content, and expose patterns or connections across vast datasets, while preserving audit trails and defensibility for courts and regulators. This dramatically reduces review time and spend, helps avoid missed evidence, and enables litigation and investigations teams to respond faster and more confidently under tight deadlines.

The Problem

Slash review time and costs in eDiscovery with AI-driven document triage

Organizations face these key challenges:

1

Skyrocketing costs and billable hours for manual review

2

Missed or misclassified privileged information and key evidence

3

Project delays due to review volume bottlenecks

4

Inconsistent relevance determinations across reviewers

Impact When Solved

Cut review volumes and costs by more than halfRespond to new matters 5–10x faster without emergency staffingImprove consistency, auditability, and defensibility of review decisions

The Shift

Before AI~85% Manual

Human Does

  • Design keyword searches and filters to narrow the review set.
  • Manually read and code documents for relevance, privilege, and issues.
  • Organize, cluster, and tag documents into themes or topics.
  • Escalate ambiguous or sensitive items to senior attorneys for judgment calls.

Automation

  • Basic deduplication and near‑duplicate detection.
  • Simple keyword highlighting and rule‑based filtering by metadata (date, custodian, file type).
  • Exporting and loading data between review platforms and matter workspaces.
With AI~75% Automated

Human Does

  • Define case strategy, issues, and training examples for AI models (seed coding).
  • Review AI‑prioritized documents, make final determinations on relevance, privilege, and risk.
  • Handle edge cases, sensitive materials, and contested calls that require legal judgment.

AI Handles

  • Ingest and automatically classify documents by relevance, topic, sentiment, and potential privilege.
  • Continuously learn from attorney decisions to reprioritize and rank documents for review (technology‑assisted review / active learning).
  • Cluster similar documents, threads, and conversations; identify anomalies and hidden patterns across large corpora.
  • Flag potentially privileged, risky, or responsive content for human confirmation, and suppress obviously irrelevant material.

Solution Spectrum

Four implementation paths from quick automation wins to enterprise-grade platforms. Choose based on your timeline, budget, and team capacity.

1

Quick Win

Batch Keyword Filtering with Cloud eDiscovery APIs

Typical Timeline:2-4 weeks

Leverage cloud-based eDiscovery APIs to automate initial filtering using rule-based and keyword search, rapidly excluding obviously irrelevant items and grouping documents by basic metadata. Minimal setup; integrates with existing eDiscovery tools.

Architecture

Rendering architecture...

Key Challenges

  • No deep semantic relevance or privilege assessment
  • High false negatives and false positives for complex topics
  • Limited support for context-dependent queries
  • Relies heavily on quality of input keyword lists

Vendors at This Level

EverlawDISCO

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Market Intelligence

Technologies

Technologies commonly used in eDiscovery Document Review implementations:

Key Players

Companies actively working on eDiscovery Document Review solutions:

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Real-World Use Cases

Advancing Legal Operations with AI in eDiscovery

This is like giving your litigation and investigations team a super‑powered, tireless junior lawyer that can read millions of emails and documents in hours, highlight what’s important, group similar issues, and surface risks and evidence so your senior lawyers only spend time on what really matters.

Classical-UnsupervisedEmerging Standard
9.0

Relativity AI-Assisted Legal Document Review

This is like giving every litigation team a super-fast junior attorney that can read thousands of documents, flag what’s relevant, explain why it thinks so, and show its work—so humans can make final calls much faster and with better evidence at hand.

RAG-StandardEmerging Standard
9.0

AI in eDiscovery (Legal Discovery Workflows)

This is about using AI as a super-fast paralegal that can read millions of emails and documents, find what matters for a case, and summarize it for lawyers, instead of humans doing that work manually.

Classical-SupervisedEmerging Standard
9.0

AI for eDiscovery and Legal Document Review

This is like giving litigators a super-fast junior attorney who can skim millions of pages, highlight what matters for your case, and organize it for you in hours instead of weeks.

RAG-StandardEmerging Standard
9.0

AI-Powered eDiscovery and Legal Document Review

This is like giving litigators a super-fast, tireless junior associate that can read millions of documents, highlight what matters for your case, and organize evidence, instead of armies of humans scrolling through emails one by one.

RAG-StandardEmerging Standard
8.5
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