Hyperscience and Blue Prism Partner to Unlock New Data Entry Automation Capabilities for Intelligent Document Processing
ORLANDO, Fla., May 23, 2019 /PRNewswire/ — HyperScience, a leading machine learning company offering enterprise-grade solutions for automating office work at scale, has partnered with Blue Prism and joined the Blue Prism’s Technology Alliance Program (TAP) as an affiliate.
HyperScience empowers business users to automate manual data entry processes, including extracting both handwritten and printed data from forms, as well as capturing data points from semi-structured documents, and do so at a lower error rate. By combining HyperScience’s distinctive data extraction product and Blue Prism’s connected-RPA platform, customers can automate each step in the full lifecycle of intelligent document processing capabilities – unlocking additional speed and cost-saving value for their Blue Prism deployment.
Beginning today, Blue Prism customers can download a connector from the Blue Digital Exchange to unlock data from documents in image or PDF format that were previously inaccessible through RPA. Once users have extracted the document data using HyperScience’s machine learning platform, they can continue to process the data using Blue Prism’s connected-RPA platform.
“Enterprises have struggled to find reliable and cost-effective means for extracting key insights from their troves of scanned images and PDFs,” said Swapnil Parikh, Vice President of Product at HyperScience. “HyperScience delivers on machine learning’s potential in a real way: our technology enables companies to automate up to 95 percent of the manual work required.
“HyperScience expands the data extraction services we can offer our customers,” said Linda Dotts, SVP Global Partner Strategy and Programs for Blue Prism. “Traditional methods of data entry have been manual, expensive and notoriously human-error prone. Through this partnership, users can automate the entire data extraction process from entry into a searchable database to preparing the data for analytics. With the combination of machine learning and human touchpoints to ensure accuracy, users get the most accurate and cost-effective data processing and analysis.”
The combination of HyperScience’s powerful data entry automation and Blue Prism’s Digital Workforce lets enterprises take a much deeper dive into intelligent data extraction. HyperScience’s patented, powerful machine learning algorithms, extracts printed and handwritten text and then hands it over to a Blue Prism digital worker for processing in accordance with enterprise goals and requirements.
For more information about the HyperScience product, see https://www.hyperscience.com/product.
HyperScience is a machine learning company focused on automating office work. Their products allow organizations to automate large amounts of manual data entry, even for documents with handwriting, poor image quality, or variable structure. HyperScience delivers automation solutions to Global 2000 companies and government institutions around the world, helping organizations reduce the bureaucratic burden on their business and their customers.
About Blue Prism
In this digital era where start-ups are constantly disrupting markets, only the most agile and innovative enterprises survive and thrive. At Blue Prism, we pioneered Robotic Process Automation (RPA), emerging as the trusted and secure intelligent automation choice for the Fortune 500 and public-sector market. Now we bring you connected-RPA supported by the Digital Exchange (DX) app store—marrying internal entrepreneurship with the power of crowdsourced innovation.
Blue Prism’s connected-RPA can automate and perform mission critical processes, allowing your people the freedom to focus on more creative, meaningful work. More than 1,300 global customers leverage Blue Prism’s digital workforce, empowering their people to automate billions of transactions while returning hundreds of millions of hours of work back to the business. Visit www.blueprism.com to learn more about Blue Prism (AIM: PRSM).
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