Powerful eDiscovery
machine learning & analytics.

Envize provides powerful machine-learning eDiscovery analytics to help you focus on the critical documents in an eDiscovery review, so that you can get your work done faster – and better.

Envize is a robust data analytics framework that brings uniquely scalable and flexible machine-learning based solutions, including patent-pending batch selection and stability measurement methods to any document review process.

Envize integrates with industry-leading review platforms so that you don’t have to manually move ESI between systems. You can painlessly leverage a unified workflow between the powerful machine-learning analytics of Envize and your document review system.

Envize Saved
$600K in 20 hours

A federal trade secrets case where Envize helped a defense firm deal with issues resulting from an overly-large collection created by overly-broad search terms; the 7,500 hours required to review the 485,000 documents wasn’t possible within the case budget and timeline. Using Envize, we saved the client nearly more than half a million dollars by allowing one reviewer to train the CAL system, and supplement with QC review.

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Robust data analytics framework that brings scalable and flexible machine-learning methods to any document review process

Continuous active learning functionality, process flexibility, and immediate review availability make Envize a clear choice.

Legility Envize’s easy-to-use interface and large document capacity, combined with batch selection and training stabilization, make process visibility and workflow flexibility possible.

Legility Envize’s novel approach to data clustering provides yet another example of a differentiated solution helping cut review time and cost.