RAVN Systems (now part of iManage) are leading experts in the Natural Language and Machine Learning branches of Artificial Intelligence (AI). The cognitive computing solutions RAVN offer are revolutionary for virtually any information intensive vertical as they transform the way organisations exploit their knowledge and information. RAVN help organisations by automating the organising, discovering and summarising of unstructured document data. This has huge competitive advantages for organisations to help manage and mitigate their risk along with increases in process efficiency. RAVN's unique proprietary technology has been trained by experts in many law firms and corporations and continues to learn as it is used. Whether applied to Knowledge Management, Cognitive Computing, Universal Search or other Big or Complex Unstructured Data handling or analytics challenges, RAVN’s unique technology for data connectivity, integration, discovery, analysis and visualisation can help maximise the full potential of unstructured data, across all information-intensive industries.
Discovery and Records Management in Place of your entire electronic document estate. RAVN Refine allows you to intelligently sub-divide your data into clear scopes and refinements and to apply appropriate policies for retention, disposal and other controls for such considerations as sensitivity control.
RAVN Connect Enterprise is an innovative approach to capturing, finding, managing and collaborating on your organisation’s hard won knowledge and expertise, leveraging the capability to navigate links between knowledge types across the enterprise. It can learn from behaviour and establish explicit and implicit links between data objects and people.
RAVN Extract uses Artificial Intelligence to automatically read, interpret and distil key information from documents and unstructured data into a desired business output. It enables organisations to increase efficiencies, reduce costs, mitigate risk by eradicating human error, as well as increasing staff morale.