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Uría Menéndez and RAVN Systems are collaborating to enhance RAVN’s Artificial Intelligence platform by creating a Spanish Due Diligence robot

London, May 9, 2017

RAVN Systems, leading experts in Artificial Intelligence (AI), Search and Knowledge Management solutions, is pleased to announce Uría Menéndez is collaborating with RAVN Systems to enhance RAVN Extract by creating a Spanish due diligence robot.

RAVN Extract is an application built upon RAVN’s Applied Cognitive Engine (RAVN ACE), an AI platform which automatically reads, interprets and extracts key information from unstructured data into any desired business output.

Uría Menéndez is one of most prestigious international law firms across the Iberian and Latin American markets with a large contract estate. They wanted to transform their due diligence process through automation to increase their efficiencies as well as giving them further competitive advantage.

Santiago Gómez Sancha, ICT Director, commented, “This technology will bring us a flexible environment that is dedicated to our firm so we can ensure we can look after our clients as efficiently as possible. We chose to collaborate with RAVN as we recognised them as a leading AI provider and wanted their product to be part of the firm’s portfolio”.

Dries Ausems, Sales Director at RAVN Systems, commented, “We are excited to collaborate with such an innovative firm and further enhance our platform for the Spanish market. They see the benefits Artificial Intelligence can bring to their firm and most importantly that they can deliver a more efficient service to their end clients”.

About RAVN Systems
RAVN Systems are leading experts in the Natural Language Processing and Machine Learning branches of Artificial Intelligence (AI). They offer revolutionary cognitive computing solutions for any information intensive vertical. RAVN’s unique proprietary technology and expertise deliver long-term value, competitive advantages and help manage and miti gate risk through structuring and surfacing information contained within unstructured data.

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