Legal Document Review Automation | AI eDiscovery

The document review cost problem is a volume problem

In a large eDiscovery production, the document set might contain 500,000 documents. Of those, perhaps 20-30% are genuinely relevant to the matter. Attorney review at $200-500 per hour applied to the full document set before any prioritisation is the most expensive way to find the relevant documents. Document review automation solves the volume problem: relevance classification removes clearly irrelevant documents before attorney review begins, issue coding groups the remaining set by topic, and predictive coding learns from initial coding decisions and applies them across the rest, so attorneys review the documents that require their judgment, in the order that makes the matter most efficient.

  • Relevance classification that removes clearly irrelevant documents from the review queue before attorney review begins

  • Privilege detection across common privilege types with privilege log generation for withheld documents

  • Issue coding and concept clustering that organises the document set around the key issues in the matter

  • Predictive coding with active learning that improves classification accuracy as reviewers code more documents

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The problem

Sound familiar?

  • Is your document review bill the single largest cost item in your litigation matters, driven primarily by attorney review hours?

  • Does your current review process include any quality control layer that catches inconsistent coding decisions across a large reviewer team?

Short answer

Legal document review automation uses AI to classify document relevance, detect privilege, code issues, and group near-duplicates across eDiscovery, due diligence, and regulatory investigation document sets, reducing attorney review volume by 60 to 80 percent. RaftLabs builds AI-assisted legal document review systems covering relevance classification, privilege detection with log generation, predictive coding with active learning, and review workflow management. Most document review automation projects are scoped at a fixed cost after an assessment of document set size, review requirements, and existing review platform.

Key takeaways

  • Continuous active learning re-scores the remaining unreviewed population after every attorney coding decision, maximising the learning signal without waiting for batch training rounds.
  • Near-duplicate and email-thread grouping can reduce the effective review population by 20-40%, letting reviewers code one decision per document group instead of per document.
  • Defensibility rests on a documented protocol: seed set selection, validation statistics, QC sampling results, and a complete audit log of coding decisions and model versions.
  • Typical document populations handled range from 50,000 to 5 million documents, with distributed processing for larger sets.

Trusted by

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Document review delivery, by the numbers

reduction in human review volume (typical)
60-80%
products shipped
100+
cost delivery
Fixed

The document review cost problem is a volume problem

Document review automation solves the volume problem: relevance classification removes the clearly irrelevant material before attorney review begins, issue coding groups the rest by topic, and predictive coding applies the reviewer's own decisions across the remaining population.

Capabilities

What we build

  • 01
    Relevance classification

    Confidence-scored relevance tiers route clearly relevant documents straight to attorney review and remove clearly irrelevant ones, typically cutting the review population by 40-60%.

  • 02
    Privilege detection and logging

    Attorney-client and work-product signal detection with automatic privilege log generation in the format required by opposing counsel or the court.

    Built with
    GDPR-aware
  • 03
    Issue coding and concept clustering

    Issue tagging trained on your matter's issue list, with concept clustering that groups thematically related documents for in-context review.

    Built with
    Relativity · Everlaw
  • 04
    Near-duplicate and email thread grouping

    Near-duplicate and thread reconstruction reduce the effective review population by 20-40% and remove redundant coding decisions.

  • 05
    Predictive coding with active learning

    Continuous active learning re-scores the unreviewed population after every coding decision, with defensible completion criteria validated by sampling.

    Built with
    Apache Tika · Lucene
  • 06
    Review workflow and quality control

    Reviewer assignment, coding-consistency monitoring, second-pass QC sampling, and dispute resolution for a multi-reviewer team.

How we work

From scope to live review system

  1. Week 1
    01

    Document population and matter scoping

    We map your document population size, matter type, and review timeline. You leave week 1 with a written scope document and a fixed-price quote.

  2. Weeks 2-4
    02

    Relevance and privilege model design

    Seed set selection, training methodology, and defensibility documentation designed against your matter's requirements.

  3. Weeks 5-9
    03

    Build and process

    Classification, issue coding, and predictive coding built and validated in parallel against real document samples.

  4. Ongoing
    04

    Review launch and QC

    Reviewers onboarded onto the workflow with quality control sampling running throughout the review period.

Why us

Why legal teams choose RaftLabs

  • 01
    Senior engineers build what they scope

    The engineers who assess your document population also build the solution. No bait-and-switch, no offshore handoff after the contract is signed.

  • 02
    Fixed price before development starts

    We scope the work, calculate the cost, and lock it in writing before any development starts.

  • 03
    9 years and 100+ products shipped

    Clients include Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin. Track record building legal-tech and document AI platforms.

  • 04
    Defensibility documented from day one

    Validation statistics, QC sampling results, and audit logs are built into the methodology, not assembled after the fact.

  • 05
    Integrates with your existing review platform

    Reviewers stay in their familiar environment; AI classification surfaces as additional context, not a new system to learn.

Have a document review project?

Tell us your document population size, matter type, and review timeline. We will scope the AI review system and give you a fixed cost.

Talk about your document review project

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Legal Document Review Automation, scoped in one call.

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Frequently asked questions

AI document review defensibility requires a documented, transparent methodology covering the relevance criteria, seed set selection, training process, validation methodology, and quality control sampling plan, along with validation statistics and a complete audit log of coding decisions. Courts and regulators in major common law jurisdictions have accepted TAR methodologies meeting these standards.

Document review systems handle standard legal discovery formats: native files (Word, Excel, PowerPoint, Outlook PST, EML), images with OCR (TIFF, PDF), and load file formats (EDRM XML, DAT/OPT, Concordance). Typical document populations range from 50,000 to 5 million documents, with distributed processing infrastructure for larger populations.

Yes. We integrate with existing review platforms including Relativity, Everlaw, Disco, and Logikcull through their APIs and export/import workflows. The AI classification layer sits alongside your existing platform, with results imported as coding fields, tags, or custom fields, so reviewers work in their familiar environment.

We support on-premises deployment in your controlled infrastructure environment, removing cloud data residency concerns. For cloud deployments, we use isolated tenants with data residency in your required jurisdiction and no cross-tenant sharing. All processing infrastructure is provisioned for the matter and decommissioned after review is complete.

Work with us

Tell us what you need. We'll tell you what it would take.

We scope Legal Document Review Automation in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.

  • Scope and cost agreed before work starts. No surprises. No obligation.
  • Working prototype within 3 weeks of kickoff.
  • Pay by milestone. You see progress before each invoice.
  • 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
  • All conversations are NDA-protected.

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