TERIS understands the unique demands and processes legal teams require during litigation. As an alternative to in-house systems, TERIS simplifies online document management with complete hosting and repository solutions. Our systems allow us to quickly setup your database and deploy your case for review within hours instead of weeks.Read More
Computers inherently create mass amounts of duplicative data. In today’s age of massive data proliferation, duplicate and near-duplicate ESI have become big contributors to excessive data populations, rising legal cost, and even a decreased confidence in data for those without access to the appropriate technology to organize and search within this data. As a result, de-duplication and near-duplication identification have become standard workflows for most eDiscovery and review teams.Read More
The EDRM (Electronic Discovery Reference Model) refers to both a conceptual framework for understanding the stages within the overall eDiscovery process, and also the EDRM organization and community behind this framework and other resources. The EDRM model consists of nine stages: information governance, identification, preservation, collection, processing, review, analysis, production, and presentation. It essentially serves as a map of the electronic discovery…Read More
Sooner or later, most e-discovery professionals have experienced the pressure of a slow-moving document review. Pressure to reduce time spent with review and cost control is a major reason that eDiscovery is prime real estate for the current blooming use of artificial intelligence (AI) in law.Read More
The document review phase during a case can seem like a near impossible task. What looks like an endless pit of data is made even more complex through the mass amounts of words, phrases, characters and other searchable content that could in turn be possible evidence.
Although it may seem complicated, once understood it becomes easier to gain a grasp and control over your dataRead More
In e-discovery, active learning utilizes machine learning technologies such as technology-assisted review (TAR), helping legal teams dramatically speed document review and thereby reduce its cost. Active learns puts the most relevant documents first, typically eliminating the need to review from 50 to 90 percent of a collection. It does this through continually learning, in real…Read More
2019 Future Ready Lawyer Survey from Wolters Kluwer For law firms and their in-house partners to hold their own and survive in the changing legal landscape, differentiating services and analyzing big data will be key, while understanding and harnessing technology are the first steps to adapting, according to a Wolters Kluwer survey. Based on quantitative…Read More