Document Review

Document Review (Doc Review) is the full‑screen workspace for reviewing, coding, and tagging documents in a matter. It's the heart of discovery review in litigation and document diligence in deals.

The three‑pane workspace

Document Review uses a three‑column layout:

• Left — document list. The set of documents you're reviewing, with search, faceted filters (date range, sender/recipient, custodian, tags, flags, responsiveness, document type), and saved searches. Move through the set one document at a time or work in bulk.
• Center — the document. A viewer with the rendered document (PDF with a selectable text layer), plus email family and threading views so you can see an email together with its attachments and the rest of its conversation.
• Right — coding & context. Where you code the document, apply tags, add notes, and see AI suggestions.

Coding and tagging

• Coding assigns a structured determination to a document — for example a relevance/responsiveness code or privilege call. Coding is how a review produces its work product.
• Tags are flexible labels (issues, custodians, "hot," etc.) you can apply to organize and later filter documents.
• Flags (such as marking a document "hot") highlight documents that need attention.

You can apply these to a single document, or in bulk to many documents at once — for example, tagging every email from a custodian within a date range.

Searching and filtering within review

The left pane's filters narrow the working set. You can combine keyword search with facets (sender, custodian, date, tag, flag, type) and save a search to return to it. You can also ask Emma to set the filters for you — e.g. "show me unreviewed documents mentioning the merger from Q2 2022" — and she'll apply them in place. See Emma.

AI assistance during review

Emma can act inside the review workspace: advance to the next document, mark responsiveness or toggle a flag, apply or remove tags (individually or in bulk), and surface coding suggestions. This lets you drive review by voice or chat while keeping your hands on the document.

Predictive coding (TAR / CAL)

For large review sets, Review Intelligence provides predictive coding — Technology‑Assisted Review (TAR) / Continuous Active Learning (CAL):

1. Create a project, choosing the criteria and tags it should predict and a confidence threshold.
2. Run it across a document set; the project reports progress (processed, remaining, failed).
3. Use validation coding to hand‑code a sample so the model refines its predictions.

Predictive coding helps prioritize the most likely‑relevant documents and measure review completeness. Projects show status (draft, running, validating, paused, completed, failed) as they progress.

Analytics

Review Analytics summarize the work: tag usage by category, how many documents are coded, and review progress. Drilling into a tag lists the documents that carry it and jumps you into review pre‑filtered to that tag. See also Relationship Intelligence & Calendar for communication analytics.

Privilege review

Documents identified as privileged or work product can be recorded in the matter's privilege log, with the privilege basis and redaction status, ready to export for a privilege assertion. See Productions & eDiscovery → Privilege log.

Tips

• Use saved searches to define review batches and return to them.
• Bulk actions are the fastest way to handle obvious sets (e.g., everything from a single custodian) before fine‑grained review.
• Everything you code and tag feeds Analytics and is visible to Emma and search.