Is your document review bill the single largest cost item in your litigation matters, driven primarily by attorney review hours?
Are reviewers spending time on documents that are clearly irrelevant -- email threads discussing weekend plans, routine operational emails with no connection to the matter?
Does your current review process include any quality control layer that catches inconsistent coding decisions across a large reviewer team?
Legal Document Review Automation
AI document review that classifies relevance, detects privilege, codes issues, and groups near-duplicates across document populations of 50,000 to 5 million documents -- reducing the volume of documents that require human attorney review by 60-80%.
Built for eDiscovery productions, M&A due diligence, regulatory investigation responses, and internal investigation document sets where review cost and speed are material constraints.
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
RaftLabs builds AI-powered legal document review systems for eDiscovery, due diligence, and regulatory investigation document sets. Systems include relevance classification, privilege detection and logging, issue and concept coding, near-duplicate and email thread grouping, predictive coding with active learning, and review workflow management. AI document review reduces attorney review time on large document populations by 60-80% by prioritising high-relevance documents and removing clearly irrelevant ones from the human review queue. Most document review AI projects are scoped at a fixed cost after an assessment of your document set size, review requirements, and existing review platform.
60-80%Reduction in human review volume (typical)
·100+Products shipped
·24+Industries served
·FixedCost delivery
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. Of those, perhaps 5-10% contain the most significant evidence. 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.
AI document review solves the volume problem. Relevance classification removes the clearly irrelevant documents -- the operational routine emails, the unrelated project discussions, the clearly out-of-scope material -- before attorney review begins. Issue coding groups the remaining documents by topic so reviewers can work through related documents together rather than reviewing an undifferentiated pile. Predictive coding learns from the initial coding decisions and applies them across the remaining document set.
The result: attorneys review the documents that require their judgment, in the order that makes the matter most efficiently.
What we build
Relevance classification
AI classification of document relevance to the matter at hand. Models trained on your review criteria and confirmed relevant and irrelevant seed documents from the population. Classification confidence scores for every document: clearly relevant, possibly relevant, possibly irrelevant, clearly irrelevant. Configurable review queues: clearly relevant documents go directly to attorney review, possibly relevant documents go to first-pass reviewer, clearly irrelevant documents are withheld from review subject to quality control sampling. Relevance decisions logged with confidence score and model version for defensibility. The first-pass review layer that removes 40-60% of the document population before attorney review begins.
Privilege detection and logging
Detection of attorney-client privilege and work product doctrine protection indicators across the document population. Signals: attorney name or email address in participants, legal advice language patterns, litigation hold references, privileged communication headers. Classification into: clearly privileged (withhold), potentially privileged (attorney review required), and no privilege indicators. Privilege log generation for withheld documents with the required fields populated from document metadata: author, recipient, date, subject, privilege type, and basis. Reduces the manual privilege log assembly that currently requires one reviewer to work through every withheld document individually.
Issue coding and concept clustering
Issue tagging that identifies which of the matter's defined issues each document relates to. Issue coding models trained on your issue list and confirmed coding decisions from the review team. Concept clustering that groups thematically related documents together -- email threads, related meeting notes, documents discussing the same event or transaction -- so reviewers can work through related material in context rather than sequentially through an undifferentiated queue. Issue distribution analytics showing which issues are heavily documented and which have thin coverage -- useful for understanding the evidentiary landscape early in the review.
Near-duplicate and email thread grouping
Near-duplicate identification that groups documents with similar or identical content across the population. Email thread reconstruction that groups emails into full conversation threads with the thread displayed in chronological order rather than as individual documents. Near-duplicate grouping means reviewers make one coding decision for a document group rather than coding the same email forwarded 50 times individually. Thread grouping means the context of each email is visible without searching for related messages. For large document populations, near-duplicate and threading can reduce the effective review population by 20-40%.
Predictive coding with active learning
Technology-assisted review (TAR) using active learning: the classification model learns from each attorney coding decision and improves its classification of remaining unreviewed documents. The model surfaces the documents it is most uncertain about for prioritised review -- maximising the learning signal from each review decision. As review proceeds, the model's confidence on the remaining population increases. Review is complete when the model's estimated remaining relevant documents in the unreviewed population falls below a defined threshold, which is validated by sampling. Defensible completion criteria documented for court or regulator.
Review workflow and quality control
Review management workflow for teams of multiple reviewers. Document assignment by reviewer, track, and issue. Reviewer productivity metrics and coding consistency monitoring -- identification of reviewers whose coding decisions diverge significantly from the team consensus, triggering calibration review. Second-pass review sampling for quality control. Review progress tracking against completion estimate. Dispute resolution workflow for documents where first and second pass reviewers disagree. The management layer that keeps a multi-reviewer team coding consistently and tracks progress against the production deadline.
Frequently asked questions
AI document review defensibility requires a documented, transparent methodology. The key elements are: a defined and documented review protocol specifying the relevance criteria, the seed set selection process, the training process, the validation methodology, and the quality control sampling plan. Validation statistics showing the model's performance on a held-out validation set. QC sampling results showing that the withhold population has an acceptable remaining relevant rate. A complete audit log of coding decisions, model versions, and training iterations. Courts and regulators in major common law jurisdictions have accepted TAR methodologies meeting these standards. We design the review protocol and documentation to meet the standards applicable to your jurisdiction and matter type.
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 we work with range from 50,000 to 5 million documents. For very large populations (5 million plus), we use distributed processing infrastructure to complete the initial processing and classification within the matter timeline. Processing speed, infrastructure requirements, and cost are scoped based on document population size and file type mix during the project assessment.
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 review platform: documents are processed, classified, and tagged in the AI layer, and the results are imported into your review platform as coding fields, tags, or custom fields. Reviewers work in their familiar review platform environment with AI classification information surfaced as additional context. If you do not have a review platform, we scope a lightweight review workflow as part of the project.
Document confidentiality is a primary requirement in legal matters. We support on-premises deployment in your controlled infrastructure environment, eliminating cloud data residency concerns. For cloud deployments, we use isolated tenants with data residency in your required jurisdiction and no cross-tenant data sharing. All processing infrastructure is provisioned for the matter and decommissioned after review is complete. Personnel with access to document content are limited to those required for system development and support. Data handling requirements, processing jurisdiction, and decommissioning procedures are documented in the engagement agreement before any documents are ingested.
