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Today, we are excited to announce that the Document AI Custom Extractor, powered by generative AI, is Generally Available (GA), open to all customers, and ready for production use through APIs and the Google Cloud Console. The Custom Extractor, built with Google’s foundation models, helps parse data from structured and unstructured documents quickly and with high accuracy.
In the past, developers trained discrete models by using thousands of samples for each document type and spending a significant amount of time to achieve production-ready accuracy. In contrast, generative AI enables data extraction from a wide array of documents, with orders of magnitude less training data, and in a fraction of the time.
In spite of the benefits of this new technology, implementing foundation models across document processing can be cumbersome. Developers need to manage facets such as converting documents to text, managing document chunks, optimizing extraction prompts, developing datasets, managing model lifecycles, and more.
Custom Extractor, powered by generative AI, helps solve these challenges so developers can create extraction processors faster and more effectively. The new product allows for foundation models to be used out of the box, fine tuned, or used for auto-labeling datasets through a simple journey. Moreover, generative AI predictions are now covered under the Document AI SLA.
The result is a faster and more efficient way for customers and partners to implement generative AI for their document processing workflows. Whether to extract fields from documents with free-form text (such as contracts) or complex layouts (such as invoices or tax forms), customers and partners can now use the power of generative AI at an enterprise-ready level. Developers can simply post a document to an endpoint and get structured data in return with no training required.
During public preview, developers cut time to production, obtained higher accuracies, and unlocked new use cases like extracting data from contracts. Let’s hear directly from a few customers:
“Our partnership with Google Cloud continues to provide innovative solutions for Iron Mountain’s Intelligent Document Processing (IDP) and Workflow Automation capabilities powered by Iron Mountain InSight®. Document AI’s Custom Extractor enables us to leverage the power of generative AI to classify and extract data from unstructured documents in a faster and more effective way. By using this new product and with features such as auto-labeling, we are able to implement document processors in hours vs days or weeks. We are able to then build repeatable solutions, which can be delivered at scale for our customers across many industries and geographies.” – Adam Williams, Vice President, Head of Platforms, Iron Mountain
“Our collaboration with Google marks a transformative leap in the Intelligent Document Processing (IDP) space. By integrating Google Cloud’s Document AI Custom Extractor with Automation Anywhere’s Document Automation and Co-Pilot products, we’re leveraging generative AI to deliver a game-changing solution for our customers. With the integration of the Custom Extractor, we are not just improving document field extraction rates; we are also accelerating deployment time by more than 2x and cutting ongoing system maintenance costs in half. We are excited to partner with Google to shape the next generation of Intelligent Document Processing solutions and revolutionize how organizations automate document-intensive business processes.” – Michael Guidry, Head of Intelligent Document Processing Strategy, Automation Anywhere
In addition, the latest Workbench updates make it even easier to automate document processing:
To quickly see what the Custom Extractor with generative AI can do, check out the updated demo on the Document AI landing page. Simply load a sample document (15 page demo limit). In seconds you will see the power of generative AI extraction as shown below.
If you are a developer, head over to Workbench on the Google Cloud Console to create a new extractor and to manage complex fields or customize foundation models’ predictions for your documents.
Or, to learn more, review documentation for the Custom Extractor with generative AI, review Document AI release notes, or learn more about Document AI and Workbench.
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