Success for RAVN Systems’ Artificial Intelligence Platform at Reed Smith
LONDON, August 30, 2016
RAVN Systems, experts in Artificial Intelligence and Search, announced today that Reed Smith, a global law firm, has successfully piloted RAVN’s Artificial Intelligence platform, RAVN ACE (Applied Cognitive Engine).
RAVN ACE and its associated RAVN Extract application is a cutting-edge AI solution that automatically summarises, analyses and extracts key pieces of information from documents.
Reed Smith has piloted RAVN’s AI technology to improve efficiencies and consistency of review for due diligence matters within their Real Estate Group. The software successfully read, interpreted and extracted key provisions from a client’s leases and provided a concise, in-context review interface of the supporting extraction evidence and graphical tools for quickly identifying outliers and higher value or higher risk leases worthy of more detailed inspection.
The AI technology was used to allow Reed Smith to test how in a live due diligence exercise they could mitigate the risks of error from fatigue and inconsistency inherent in the traditional manual review process, allowing fee earners to focus on higher value, more complex aspects of the transaction.
Lucy Dillon, Chief Knowledge Officer at Reed Smith commented, “By adopting this innovative platform we are able to quickly focus on the high value and high risk contracts within large data sets, simply categorise and divide up tasks and ultimately provide a more responsive service to our clients. We are now looking to roll out the RAVN solution to live client matters within the firm”.
About RAVN Systems
RAVN Systems has extremely broad and deep experience with Unstructured Data processing and offers revolutionary, search-based and cognitive computing solutions for any information intensive vertical. RAVN expertise and solutions deliver long-term value, competitive advantages and help manage and mitigate risk through surfacing and harnessing the information contained within unstructured data.