LONDON, August 23, 2016
RAVN Systems Launch Self-service AI Portal to Automatically Summarise, Analyse and Extract Information From Documents
RAVN Systems, experts in Artificial Intelligence (AI), Enterprise Search and Knowledge Management solutions, announced today they are developing a self-service AI portal, allowing clients to be firmly in control of automatically summarising, analysing and extracting key information from documents.
The AI robot, RAVN Extract Direct, can automatically read, interpret and extract key information from high volumes of documents into a desired business output, like a human would do but much faster and eradicating human error. RAVN Extract Direct allows the client to have total control of the platform by training the robot how to read the documents and what KPIs (key points of interest) should be extracted. By having complete control of the robot clients can speed up the data extraction process, increasing productivity within their organisation.
This self-service robot can be easily configured by the client to handle a range of document sets and clients choose the KPIs they need, that may vary depending on the project. Examples include due diligence exercises, contract analysis, review of financial documents, lease forms etc.
Sjoerd Smeets, COO at RAVN Systems commented, “We are delighted to take the next step in cognitive innovation. After listening to our clients needs we are able to give them the power in their own hands. Law firms, corporate law departments and other organisations can ensure increased response times and an overall improved service to their clients by working directly with the robot”.
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.