July 13th, 2021
1:00 PM - 2:00 PM EDT
While advanced analytics have improved the document review process, common technology-assisted review (TAR) workflows require case teams to decide between rapid-start prioritized reviews and the rigorous recall measurement required of document productions. The distinctions of TAR 1.0 and 2.0 are little help when you want something that blends the best of both. Coming soon to the already advanced AI solutions available in H5 Matter Analytics, Active Learning is a TAR solution built from the ground up to offer flexible workflow options, true precision and recall measurement, and all inside of Relativity.
In this exclusive preview, explore ways Active Learning in H5 Matter Analytics helps you to:
- Quickly start your TAR review with the best of TAR 1.0 and 2.0 by enabling rapid start prioritized review with full precision and recall measurement
- Tailor your workflow by adjusting the diversity of content present in your review queue
- Accelerate the startup process with seamlessly integrated control sets and auto-detection of documents ineligible for Active Learning
- Make informed decisions with active learning job monitoring that provides actionable insights with estimated outcomes
- Manage every aspect of your Active Learning workflows right inside of Relativity
Speakers
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Jason Richard
VP of Products
H5
Jason Richard has more than 20 years of experience in the legal services industry. As Vice President of Products for H5, his primary focus is in the development of products that leverage H5’s expertise in electronic discovery, litigation readiness and data analysis. He is also involved in strategic consulting regarding the management of electronic records in support of all phases of litigation, investigations, and litigation preparedness initiatives. Prior to joining H5, Mr. Richard served as the Director of Technology Services at Document Technologies, Inc., a national litigation services firm, where he was responsible for managing document and eDiscovery services for law firms and corporations with large scale discovery needs.
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