Security News > 2021 > June > Casepoint CaseAssist helps organizations expedite review and investigations

The predictions generated by Casepoint's CaseAssist technology in eDiscovery, investigations, and other document-intensive review projects eliminate the need for users to review documents that are nearly certain to be non-relevant, saving thousands of dollars in review time.

Through CaseAssist Active Learning, users can choose to train a single or multiple models with no sample set requirement and CaseAssist will ensure relevant documents are prioritized for review.

Casepoint's Dynamic Batch Review workflow seamlessly integrates with CaseAssist to make the transition from prioritization to linear review with ease.

In addition to guiding the user through the oftentimes complex workflow of advanced analytics, Casepoint's CaseAssist Active Learning utilizes active learning based on the user's input to continuously predict and rank unreviewed documents.

CaseAssist is commonly used in intelligence mining, early data analytics, investigative analytics, review prioritization, and coding validation.

"While DIY timeline analysis and DIY sender/recipient analysis are probably the most popular features for our teams, we also use Casepoint analytics to do things like spot gaps by date or file type in loaded content, determine data volumes by individual custodian, identify odd file types, and identify sender and recipient domains to bulk-identify documents that are unlikely to be relevant for first or early review. Because CaseAssist's is so simple to learn, more of our attorneys are using it on their own. Also, the use cases are virtually unlimited, which is why adoption continues to increase across the firm."

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