Legal Document Review Automation

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.

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. For international matters involving cross-border data transfers, GDPR transfer documentation requirements are tracked as a separate coding category, ensuring documents subject to EU data transfer restrictions are identified and handled appropriately in the production set. Privilege log generation for withheld documents with the required fields populated from document metadata: author, recipient, date, subject, privilege type, and basis -- attorney-client or work product -- with the log exportable in the format required by opposing counsel or the court. Reduces the manual privilege log assembly that currently requires one reviewer to work through every withheld document individually.

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. Custodian interview findings can be used to seed the initial issue model: the persons of interest identified during custodian interviews are mapped to documents from their custodian set, giving the model early signal on which issues each custodian's data is likely to cover. Issue distribution analytics showing which issues are heavily documented and which have thin coverage -- useful for understanding the evidentiary landscape early in the review. The system integrates alongside existing review platforms including Relativity, Everlaw, Reveal AI, and Nuix, so attorneys work in their familiar review environment with AI classification results surfaced as coding fields or tags.

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%.

Technology-assisted review (TAR) using continuous active learning (CAL): the classification model learns from each attorney coding decision and immediately re-scores the remaining unreviewed population, rather than waiting for a batch training round. The model surfaces the documents it is most uncertain about for prioritised review -- maximising the learning signal from each review decision. Native file processing handles standard discovery formats using Apache Tika for content extraction and Lucene for full-text indexing, supporting Word, Excel, Outlook PST/EML, TIFF with OCR, and EDRM XML load files. 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 random sampling of the withheld set. Defensible completion criteria documented for court or regulator in line with EDRM workflow stage standards.

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.