E-discovery providers are primed to make the shift from providing products designed for e-discovery to providing products for much more complex document analytics.
Casepoint is typically thought of as an e-discovery company, although it describes itself as a “leader in cloud-based legal technology solutions.” It recently announced a new iteration to its built-in AI and advanced analytics technology, called CaseAssist.
According to the Casepoint press release, the enhanced technology will “give users more insight and control over the analytics process with enhanced visualization capabilities and configuration templates.” Says Casepoint, the enhancements will provide a continuing learning AI platform for the review of documents. It will work with or without sample documents for eDiscovery, investigations, and other document-intensive review projects. It embraces active and ongoing machine learning. According to Casepoint, CaseAssist includes workflows with natural language interactions. It will enable users to find critical documents by quickly answering simple questions. And there are numerous other enhancements. See the website and Bob Ambrogi’s article for a more comprehensive description.
But to me, that’s not the real story. When Vishal Rajpara, cofounder and chief technology officer of Casepoint,showed me the product, I noticed something called Data Story Builder. Rajpara explained that this allowed a user to prepare a “story” about such things as the pleadings, the case itself, or the defenses. The user can then ask CaseAssist to find documents that support (or are potentially antithetical to the story).
The CaseAssist tool thus provides really a different and much broader use case entirely than just e-discovery. And it transitions Casepoint from just an e-discovery provider to something more remarkable. Casepoint and companies offering similar products (see below) have in fact become data analytics providers. Analytics that can be applied to vast sets of documents not only to find