[Webinar] Carpe Data! Stop “Ctrl+Fing” Transcripts and Use Testimony Intelligence

August 7th, 1:00 pm - 2:00 pm ET

EDRM - Electronic Discovery Reference Model

August 7th, 2023
1:00 PM - 2:00 PM ET

Despite the ubiquitous advantages of AI-powered eDiscovery, matter management, content analysis, and other solutions, litigation costs and data volumes continue to rise. Is there any end in sight?

A traditional tagging and organizational system can’t find what it isn’t looking for. What if you could anticipate topics and issues rather than basing them on an attorney’s memory or opinions about a case?

In this webinar, the Cloud Court team will discuss an enormous latent resource that’s hidden in plain sight: testimony. They’ll explore how a system of “testimony intelligence” can mitigate or reduce the triple pervasive threats of exploding data volumes, fallible human memory, and relentless growing litigation costs.

Join us to see how their solution, Gibson, is a natural companion and complement to eDiscovery that can help teams reduce costs and drive better outcomes.

Who should attend

  • Litigators and litigation support professionals
  • Litigation-related software and service providers
  • Your role involves capturing and analyzing content from depositions and trials

What you will learn

  • Discover effective ways to leverage existing testimony content
  • Learn how to derive insights and advantages from AI-driven topic extraction and powerful reporting visualizations
  • Discover groundbreaking advantages in depo and trial witness prep, motion practice, collaboration with aligned parties, litigation team onboarding, leaps in efficiency and elimination of wasteful expenses
  • Identify ideal cases to pilot your first TI project

Speakers

Cloud Court team

Michael Okerlund, CEO, litigator and former F500 general counsel

Dr. Milena Higgins, CTO, data scientist, physicist and former KM professional

Joe Longtin, Product Marketing Manager, trainer and project manager

PRESENTED BY:

EDRM - Electronic Discovery Reference Model
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