HyperScience automatically classifies submitted documents using Machine Learning technology.
HyperScience extracts handwritten and printed text at 80% automation and over 98% accuracy on Day 1 with improvements over time.
HyperScience sends small subsets of exceptions to your data entry teams for review, which finetunes the underlying model.
Structured data files can be sent to downstream systems for processing via flexible API.
HyperScience extracts handwritten, cursive and printed text across diverse documents, including forms, invoices, checks, and more. Skewed or stretched scans and low resolution images are no match for HyperScience, which delivers double the accuracy on handwritten text compared to alternative data capture technology.
HyperScience’s containerized deployment requires minimal resources to set-up, implement and maintain, meaning your organization is up and running in days, not weeks. With our flexible REST API and connectors with leading RPA providers, enterprises can easily integrate HyperScience into existing workflows, unlocking data from previously inaccessible images and PDFs and achieving end-to-end automation for document processing.
HyperScience deploys on-premise or in your private cloud utilizing commodity Linux hardware, so data never leaves your environment. Our solution is designed to scale horizontally, meaning large or unpredictable volumes of incoming documents are not a problem. Our models learn on your specific data to deliver on-going performance improvement, so you reap the benefits of Machine Learning behind your firewall.
With HyperScience, not only do you have a more robust solution to start, but you have a solution that can measure how well it’s doing and get better over time. HyperScience meets your organization’s accuracy requirements, typically automating 70-90% of data entry out-of-the-box at >99.5% accuracy. Our built-in quality assurance mechanism measures how well it's doing and knows when to ask for help, bringing supervisors in the loop to review and resolve a subset of exception cases, which refines the underlying model.
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