Google unveils AI-powered document processing services

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The services they previewed last year are now released to general availability.

This week Google announced the general availability of its Cloud DocAI platform, Lending DocAI and Procurement DocAI. These platforms all use AI to streamline the heavy and work-intenive tasks of document processing.

Google first announced these products back in November 2020. Since then, thousands of businesses have piloted the solutions. Now they are available to all customers, complete with some new features and resources.

Sudheera Vanguri, Google’s Head of Product for Document AI, detailed the general release in a blog post this week. “Most companies are still manually entering data and reliant on guesswork to make sense of it all as the volume and variety of data explodes,” he explained. “Organizations are also leaving heaps of value on the table in the form of new and better customer experiences that can be unlocked with artificial intelligence (AI) applied to documents.”

“The latest releases of Document (Doc) AI platform, Lending DocAI and Procurement DocAI, built on decades of AI innovation at Google, bring powerful and useful solutions to these challenges,” he claimed.

Powered by a core of new technologies

The new solutions are based on a stable of Google technology building blocks. First, computer vision (including OCR) and Natural Language Processing (NLP) create pre-trained models for high-value, high-volume documents. Then, Google Knowledge Graph validates and enhances the fields in the customer’s documents. There is also training, and customers can create their own custom document models. 

The new platforms also allow for human interaction with the AI to ensure accuracy where needed. As an example of the platform’s value, Google claims that Lending DocAI enables banks to reduce the loan application process from weeks to days. It thus dramatically reduces the cost of issuing a loan.

Also, Google says that Procurement DocAI helps firms to automate procurement data capture at scale. This, they claim, lowers processing costs by up to 60 percent.