One review standard across the collection
Use a shared classification approach to focus the team’s work. Review AI calls against the source documents and override them when the evidence requires it.
The same engine, a different matter
The engine that reviews a million documents in a fraud case can support the review of 5,000 contracts in a data room. That is an illustrative deal workload, not a reported customer result. The common task is to classify a large document set, inspect the reasoning, and decide what deserves closer review.
Build the review around your deal questions
01
Define the scope
Load the data-room documents into a matter. Agree on the issues and classification criteria your review team will use.
02
Classify in bulk
Apply AI document classification across the collection. Inspect the reasoning and underlying source before relying on a result.
03
Resolve the exceptions
Have attorneys verify priority documents, correct classifications, and decide which agreements need a closer contract review.
Start broad. Read closely where it matters.
Sensitive documents stay in scope
Bring privilege and confidentiality analysis into the review process so the team can assess sensitive material before deciding how to share it.
Take the next contract into Word
Once your team identifies an agreement that needs negotiation, review it in the Word add-in. Set the party you represent, add deal-specific instructions, and examine clause-quoted findings before inserting a tracked change.
Questions about this workflow
Is the 5,000-contract example a customer case study?
No. It illustrates how bulk document review can be applied to a deal. The linked fraud-matter case study is litigation proof of scale, not a transactional outcome.
Does classification replace attorney diligence?
No. Classification helps prioritize the review. Attorneys verify source documents, resolve gaps, and make the legal and commercial judgments.
How should we scope a data-room review?
Bring the document types, approximate volume, and review questions to a demo. We will walk through how the existing classification workflow fits your matter.
The same controls, across the matter
Your documents are not training data
DecoverAI carries matter-level permissions, role-based access, and audit logging across the platform. Your data is never used to train our models.
Review our security controls, data processing agreement, and published pricing with your team.