LONDON, September 27, 2016
RAVN Systems, leading experts in Artificial Intelligence, Search and Knowledge Management solutions, announced today the launch of a RAVN ACE powered Robot for LPP (Legal Professional Privilege) review, allowing clients to automate the review of determining if material is subject to LPP.
The LPP Robot uses state of the art AI (Artificial Intelligence) techniques to automatically read through vast document collections, such as case material in litigation, as well as other document types, to determine whether individual items are subject to LPP. The accuracy levels achieved by the Robot making these determinations now surpass traditional manual efforts and is several orders of magnitude faster.
The solution exploits supervised iterative Machine Learning models inside the RAVN ACE (Applied Cognitive Engine) platform, meaning the Robot will become more accurate over time. It is a technological leap from legacy predictive coding methods that sometimes are used today. The certainty levels over classification accuracy can be adjusted to desired levels of recall to alleviate any misclassification concerns.
Usage statistics indicate a radical reduction in time taken to perform the LPP review using the Robot. In most cases, the reduction in total time taken to perform the review with the LPP Robot exceeds 80%, including the administrative overheads of deploying the Robot.
Peter Wallqvist, CEO at RAVN Systems commented, “We are excited to add the LPP Robot to our portfolio of ACE based applications, completely transforming the review process, resulting in an overall improved service to end clients”.
About RAVN Systems
RAVN Systems are leading experts in the Natural Language Processing and Machine Learning branches of Artificial Intelligence. They offer revolutionary cognitive computing solutions for any information intensive vertical. RAVN’s expertise and solutions deliver long-term value, competitive advantages and help manage and mitigate risk through structuring and surfacing information contained within unstructured data.