ADP - A TAR Module
It is no secret that in the e-Discovery lifecycle, linear document review accounts for the largest portion of costs. In an ever-expanding big data environment, corporations facing litigation must confront the question of whether a linear review is proportional to its likely benefit, or whether it is even possible.
Capital Novus’ Automated Document Profiling ("ADP"), technology assisted review (predictive coding), module integrated within eZReview is empowering the e-Discovery landscape by leveraging sampling techniques and advanced algorithms to optimize the review of millions of documents.
ADP is an automated probabilistic review methodology utilized to dramatically increase review efficiencies. Employing human input along with advanced technology to identify relevant data, users of ADP can realize significant cost savings in an expedited timeframe.
The ADP Model:
- A form of supervised learning
- Samples of relevant information are provided to the learning machine
- Lawyers provide correct answers
- The learning process is repeated until satisfactory results are obtained consistently