Artificial intelligence software company H2O.ai announces the launch of its new system named H2O Document AI. The newly launched system will help in automating the task of document processing.
The highly capable artificial intelligence-powered system can process, store, and manage extensive amounts of different documents and unstructured data that businesses manage every day across the globe. The AI system can also help companies reduce significant costs involved in document management while simultaneously streamlining and optimizing the process.
Companies can use H2O Document AI to segregate documents, extract text, tables, images, graphs, and many other elements. H2O Document AI will allow businesses to seamlessly extract data from documents to analyze them and use the generated results to upscale their operations.
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Expert in Residence for AI, UCSF Center for Digital Health Innovation, Bob Rogers, said, “When we started this journey, we were hopeful that information extraction from semi-structured documents was possible, but we weren’t sure. Some in the industry told us it couldn’t be done. Working with H2O.ai has opened up many possibilities.”
San Francisco-based artificial intelligence firm H2O.ai was founded by Cliff Click and Sri Satish Ambati in 2012. To date, the startup has raised $251 million over eight funding rounds from investors like Commonwealth Bank of Australia, Goldman Sachs, Celesta Capital, Crane Ventures Partner, and many others. H2O.ai is known for its open-source machine learning platform that makes smart applications development more accessible.
Founder and CEO of H2O.ai, Sri Ambati, said, “Our banking, insurance, health, audit, and public sector customers each process billions of documents every year. Documents are the fastest growing source of data in the enterprise, ranging from contracts, bank statements, invoices, payroll reports, regulatory reports, and medical referrals to customer conversations in text, chat, and email.”
He further added that their new system would enable customers to sieve intelligence across a wide variety of document types very accurately at an unprecedented rate, which was not possible until now.
