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March 11, 2024

The Proxiio Advantage: Leveraging Relativity Active Learning and Human Ingenuity

Author: Nasir Ali

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Proxiio’s partnership with Relativity provides access to many technology-assisted review tools that are incorporated into the RelativityOne platform. One of the most widely used tools, Active Learning, serves as a technology-aided solution designed to efficiently analyze large datasets and predict the documents likely to be relevant for the review.

Using guidelines and requirements shared by the client counsel, Proxiio review managers identify the reviewers with highest level of experience and subject-matter expertise based on the facts of the case. They then  design a workflow to derive the most value from Active Learning.


Identifying Relevant Documents Quickly

When case requirements demand relevant or key documents be identified quickly from large data sets to meet production timelines or prepare for trial, Proxiio utilizes the “Prioritized Review” queue to accelerate the learning model and ensure that the most relevant documents are identified first.


Optimizing Review Time and Cost

Active Learning enables us to significantly cut down on review expenses and streamline the time needed to finalize the review in accordance with counsel directives. We utilize our proficiency in crafting workflows that decrease the necessity for human document review by as much as 75%, all while instituting safeguards to maintain uncompromised quality.


Efficient Management of Rolling Collection and Productions

When dealing with numerous custodians and data sources, creating a review workflow that efficiently handles the review and production of documents in several iterations can present complexities. Using the Active Learning “Coverage Review” queue allows us to design a workflow that ensures new documents are seamlessly added to review queues and processed for production.

 

Improving Quality and Accuracy

We utilize multiple Active Learning models for a project to ensure documents processed by our review team not only meet the relevance criteria, but also are appropriately flagged for Privilege, Confidentiality, and other parameters. We also ensure any documents scored highly by the model for a particular parameter receive a QC check, minimizing the chances of human error.  

author
Nasir Ali
Assistant Vice President

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