Some of the most important data at your company isn’t living in databases, but in documents, and most business processes begin, involve or end with a document. 

Yet 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. 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. Under the hood are Google’s industry-leading technologies:

• Computer vision (including OCR) and Natural Language Processing (NLP) that creates pre-trained models for high-value, high-volume documents. 
• Google Knowledge Graph to validate and enhance the fields in your documents.
• Training and creation of your own custom document models. 
• Human interaction with AI to ensure accuracy where needed.

Google Cloud DocAI platform, Lending DocAI and Procurement DocAI are now generally available. Thousands of customers have tried these products in the preview phase—and DocAI has already processed tens of billions of pages of documents across lending, insurance, government and other industries.

“The vast majority of enterprise content still resides in unstructured sources like documents. Google Cloud’s Document AI brings a fresh new perspective to the problem informed by the company’s decades of experience making sense of the largest unstructured corpus in the world—the world wide web.” —Ritu Jyoti, VP of AI Research, IDC

Cut document processing costs by up to 60%

Lending DocAI helps banks, mortgage brokers and other lending institutions fast track the loan application process from weeks to days, dramatically reducing the cost of issuing a loan. And Procurement DocAI enables companies to automate procurement data capture at scale, lowering processing costs by up to 60%.

These solutions are built on DocAI platform, a unified console for document processing that lets you quickly access all parsers and tools. From the platform, you can automate and validate documents to streamline workflows, reduce guesswork, and keep data accurate and compliant. 

Get more value from AI with DocAI’s industry-specific solutions

According to Accenture’s AI: Built to Scale report: “Companies that scale successfully see 3x the return on their AI investments compared to those who have not fully rolled out AI capabilities.” 

Core to our strategy at Google Cloud is the creation of industry-specific solutions that help companies get maximum value out of their investments in AI. We announced Lending DocAI, our first solution designed specifically for the financial services industry, at the Mortgage Bankers Association convention last year. It processes borrowers’ income and asset documents using a set of specialized machine learning (ML) models, and automates routine document reviews so that mortgage providers can focus on more important work. 

Lending DocAI is now generally available and includes more specialized parsers for critical loan documents including paystubs, bank statements, and more. Our goal is to provide the right tools to help borrowers and lenders have a better experience and close home loans faster. For more, watch this video.

Procurement DocAI is also now generally available. This solution helps companies accelerate document processing for invoices, receipts, and other valuable documents in the procurement cycle. 

Automating data capture is helping our customers increase accuracy and also lower their procure-to-pay processing costs. We are continually expanding the types of documents Procurement DocAI can process—the latest is a utility parser for electric, water and other bills. In addition, Procurement DocAI leverages Google Knowledge Graph to validate and enrich parsed information to make the data even more useful. Check out this overview video for more details. 

One company that lives and breathes AI-enabled document management is AODocs. It uses Procurement DocAI to simplify invoice processing for enterprise customers and launched a new Gmail add-on, Invoice to Sheet, for SMB customers who just want to track their invoices in Google Sheets.

“Google Cloud’s Procurement DocAI service allows our document management platform to better automate the processing of invoices; AODocs customers who have tested our new account payables workflow estimate that the productivity of their A/P team has more than doubled, thanks to the reduction of manual data input brought by the Procurement DocAI.”—Stéphan Donzé, Founder and CEO, AODocs

The new specialized parsers for Lending and Procurement DocAI can be used alongside our existing AutoML Text & Document Classification and AutoML Document Extraction services. These technologies provide a state-of-the-art toolset for creating new document models and have been widely deployed by customers in financial services and other industries. 

Partner to accelerate your AI deployment and results

Having the right partner to ease the complexity of rolling out your AI-strategy in mortgage document processing is critical to transforming your customers’ experience. We’re excited to announce a partnership with Mr. Cooper, a leader in mortgage servicing, to provide customers with more automation and workflow tools throughout their entire mortgage life cycle. As part of this agreement, both companies will collaborate on digitizing Mr. Cooper’s core mortgage platform, creating a more personal customer experience utilizing AI, and driving a broader culture of innovation to imagine and develop services and solutions that will transform the mortgage experience for American homeowners.

“Over the last few years, we have made substantial investments in our servicing technology and core mortgage platform that have revolutionized the customer experience, while providing dramatic efficiencies in operating cost. Our partnership with Google Cloud AI will build on those advances and help make these technologies available for the mortgage industry.” —Jay Bray, Chairman and CEO, Mr. Cooper Group

This builds upon the robust partner ecosystem we’re creating to help customers revolutionize the home loan experience, which includes last year’s partnership announcement with Roostify.

Integrate human review into ML predictions 

Next up is the general availability of Human-in-the-Loop AI, a new DocAI feature that will help companies achieve higher document processing accuracy with the assurance of human review. Adding human review can increase accuracy and help businesses interpret predictions using purpose-built tools to enable those reviews. 

Processing documents quickly and cost-effectively is important. But it’s often necessary to have a high level of assurance on data accuracy for compliance. CIOs and IT decision-makers need highly accurate ML predictions to fulfill compliance requirements, improve employee experience (e.g. less rework), and raise customer satisfaction (e.g. fewer data errors). Including human participation in ML processes allows AI and humans to work together for the best possible results.

Human-in-the-Loop AI provides the workflow to manage human review tasks and produces a percentage confidence score of how “sure” it is that the AI ingested the document correctly. Document AI extracts data from documents with ML, and when paired with Human-in-the-Loop AI, human reviewers are able to verify the data captured. This system is customizable, providing the flexibility to set different thresholds and assign individual groups of reviewers to various stages of the workflow. With Human-in-the-Loop AI, developers can choose trusted reviewers to assign to the task; these reviewers can be from within their own or partner organizations.

More Document AI resources

To learn more, check out the Document AI webpage and watch a demo of how to process sample forms in AI Platform notebooks to inspect data extraction and confidence scores. For more on how customers and partners like Workday, AODocs, and Mr. Cooper are using Document AI, listen to our fireside chat. And stay tuned for the exciting evolution of these technologies in future releases of DocAI.

There are a number of terms and concepts in cloud computing, and not everyone is familiar with all of them. To help, we’ve put together a list of common questions, and the meanings of a few of those acronyms. You can find all these, and many more, in our learning resources.

What are containers?

Containers are packages of software that contain all of the necessary elements to run in any environment. In this way, containers virtualize the operating system and run anywhere, from a private data center to the public cloud or even on a developer’s personal laptop. Containerization allows development teams to move fast, deploy software efficiently, and operate at an unprecedented scale. Read more.

Containers vs. VMs: What’s the difference?

You might already be familiar with VMs: a guest operating system such as Linux or Windows runs on top of a host operating system with access to the underlying hardware. Containers are often compared to virtual machines (VMs). Like virtual machines, containers allow you to package your application together with libraries and other dependencies, providing isolated environments for running your software services. However, the similarities end here as containers offer a far more lightweight unit for developers and IT Ops teams to work with, carrying a myriad of benefits. Containers are much more lightweight than VMs, virtualize at the OS level while VMs virtualize at the hardware level, and share the OS kernel and use a fraction of the memory VMs require. Read more.

What is Kubernetes?

With the widespread adoption of containers among organizations, Kubernetes, the container-centric management software, has become the de facto standard to deploy and operate containerized applications. Google Cloud is the birthplace of Kubernetes—originally developed at Google and released as open source in 2014. Kubernetes builds on 15 years of running Google’s containerized workloads and the valuable contributions from the open source community. Inspired by Google’s internal cluster management system, Borg, Kubernetes makes everything associated with deploying and managing your application easier. Providing automated container orchestration, Kubernetes improves your reliability and reduces the time and resources attributed to daily operations. Read more.

What is microservices architecture?

Microservices architecture (often shortened to microservices) refers to an architectural style for developing applications. Microservices allow a large application to be separated into smaller independent parts, with each part having its own realm of responsibility. To serve a single user request, a microservices-based application can call on many internal microservices to compose its response. Containers are a well-suited microservices architecture example, since they let you focus on developing the services without worrying about the dependencies. Modern cloud-native applications are usually built as microservices using containers. Read more.

What is ETL?

ETL stands for extract, transform, and load and is a traditionally accepted way for organizations to combine data from multiple systems into a single database, data store, data warehouse, or data lake. ETL can be used to store legacy data, or—as is more typical today—aggregate data to analyze and drive business decisions. Organizations have been using ETL for decades. But what’s new is that both the sources of data, as well as the target databases, are now moving to the cloud. Additionally, we’re seeing the emergence of streaming ETL pipelines, which are now unified alongside batch pipelines—that is, pipelines handling continuous streams of data in real time versus data handled in aggregate batches. Some enterprises run continuous streaming processes with batch backfill or reprocessing pipelines woven into the mix. Read more.

What is a data lake?

A data lake is a centralized repository designed to store, process, and secure large amounts of structured, semistructured, and unstructured data. It can store data in its native format and process any variety of it, ignoring size limits. Read more.

What is a data warehouse?

Data-driven companies require robust solutions for managing and analyzing large quantities of data across their organizations. These systems must be scalable, reliable, and secure enough for regulated industries, as well as flexible enough to support a wide variety of data types and use cases. The requirements go way beyond the capabilities of any traditional database. That’s where the data warehouse comes in. A data warehouse is an enterprise system used for the analysis and reporting of structured and semi-structured data from multiple sources, such as point-of-sale transactions, marketing automation, customer relationship management, and more. A data warehouse is suited for ad hoc analysis as well custom reporting and can store both current and historical data in one place. It is designed to give a long-range view of data over time, making it a primary component of business intelligence. Read more.

What is streaming analytics?

Streaming analytics is the processing and analyzing of data records continuously rather than in batches. Generally, streaming analytics is useful for the types of data sources that send data in small sizes (often in kilobytes) in a continuous flow as the data is generated. Read more.

What is machine learning (ML)?

Today’s enterprises are bombarded with data. To drive better business decisions, they have to make sense of it. But the sheer volume coupled with complexity makes data difficult to analyze using traditional tools. Building, testing, iterating, and deploying analytical models for identifying patterns and insights in data eats up employees’ time. Then after being deployed, such models also have to be monitored and continually adjusted as the market situation or the data itself changes. Machine learning is the solution. Machine learning allows businesses to enable the data to teach the system how to solve the problem at hand with machine learning algorithms—and how to get better over time. Read more.

What is natural language processing (NLP)?

Natural language processing (NLP) uses machine learning to reveal the structure and meaning of text. With natural language processing applications, organizations can analyze text and extract information about people, places, and events to better understand social media sentiment and customer conversations. Read more.

Learn more

This is just a sampling of frequently asked questions about cloud computing. To learn more, visit our resources page at cloud.google.com/learn.

The COVID-19 pandemic tested our nation’s public benefits system in unimaginable ways. With an unprecedented 60 million individuals turning to unemployment and social services to satisfy their basic needs, state and local governments were stretched to meet the demand.

But state and local government leaders have risen to the challenge of providing their constituents with critical services. They created and administered innovative solutions, complete with wholly original processes, and brought desperately needed employment, cash, food, and healthcare support to families in crisis. These public sector heroes raised the bar during this critical time and facilitated economic recovery for our local communities, making the impossible possible for its people. 

When state and local government agencies partner with Google Cloud, they can reimagine how they deliver human services and labor services for their communities. All in a matter of weeks, instead of months or years.

Tools that solve for remote work and service delivery hurdles

Never before was there such a need for modern and accessible tools to allow a workforce to secure new jobs and operate remotely. The Virtual Career Center (VCC), built on Google Cloud, was designed with these needs in mind.

Google Workspace, including Google Meet, allows job seekers to schedule video meetings with career coaches, job recruiters, and potential employers. The Google Job Search API will enable them to explore career opportunities best suited to their skills and interests.

A thousand miles away, the Rhode Island Virtual Career Center was helping its job seekers by offering virtual meetings with career coaches, the ability to schedule meetings with prospective employers, and help on building effective resumes. Skipper, the CareerCompass RI Bot, an intelligent agent for careers, uses data and machine learning to facilitate potential new career paths and reskilling opportunities for Rhode Islanders.

In addition to enabling remote work, Google Workspace can support remote service delivery, a critical need for those involved in health and human services programs. Engaging with individuals and families is an essential component for more of these programs, including eligibility determination, assessment, and service delivery. The ability to engage directly with the public through virtual interviews, counseling sessions, telehealth, etc. improves access for those on both sides of the screen. Further, through Google Classroom, a component of Google Workspace for Education, foster and adoptive parents can obtain virtual training required for licensing. 

Tools to expedite relief funds and employment assistance

As the number of people applying for unemployment assistance skyrocketed, so did the backlog of claims requiring review. The pandemic also saw a rise in fraudulent claims, which caused delays for families waiting on legitimate payments and resulted in unnecessary spending for government agencies. Realizing the magnitude of the challenge was beyond the agencies’ current resources, and they shifted their focus to machine learning.  

To address the massive backlog of claims, SpringML partnered with Google Cloud to develop the Improper Payment Analytics solution that successfully leverages AI to help agencies identify fraudulent claims and avoid improper payments so aid can be prioritized for those who need it. Though such a tool would typically take months to develop, the team was able to launch it in just weeks, to the benefit of all stakeholders involved. Families awaiting legitimate payments received checks more quickly, and state and local governments saved millions in spending.

For instance, to address erroneous payments totaling $330 million to fraudulent applicants, the State of Ohio partnered with Google Cloud to utilize artificial intelligence and machine learning solutions to proactively identify and decline improper payment risks. As a result, the State avoided paying fraudulent claims and accelerated the payment of legitimate benefits to families facing financial hardship.

The New York State Department of Labor launched a streamlined unemployment application to allow residents to apply for pandemic unemployment assistance without the added burden of applying for unemployment insurance. Success was immediate, and the application backlog plummeted as New Yorkers got the financial aid they desperately needed. Illinois took a different approach, deploying Contact Center Artificial Intelligence (CCAI) to create virtual agents who assist with specialized calls 24/7, in multiple languages, providing turn-by-turn guidance in real-time. Conversations can be turned into insights through analytics and reporting tools that uncover key call drivers and customer sentiment. By solving for spikes in call volume, CCAI has helped process more than 1 million unemployment claims.

Automated data solutions bring immediate food and cash assistance resources

With millions out of work and out of school during the pandemic, food and cash assistance became a critical need. Applying for all of these benefits requires significant documentation that, in most cases, has historically been manually processed. Given the volume of people in need, this took a massive amount of human resources and time.

Using Document AI (DocAI), agencies can automate this highly manual process and speed up the delivery of critical benefits to individuals and families. DocAI extracts the key data, provides a confidence score in a single review pane for staff review, and automatically uploads that data to the case management system–significantly reducing manual processing. Today, these state and local government organizations turn to DocAI to eliminate the difficult paperwork application process to help meet the benefit needs of their residents now and tomorrow, as illustrated by both the Wisconsin Department of Workforce Development (DWD) and the State of Hawaii.

The Wisconsin DWD streamlined their paper unemployment insurance claims using DocAI by enabling DWD staff to receive critical data extracted from submitted applications and make decisions rapidly, saving time for both applicants and staff. The State of Hawaii also used DocAI to extract, interpret, and transport COVID-19 test result data of incoming travelers to Google Cloud instantly. As a result, Hawaii was able to welcome travelers and reopen its economy in the midst of the pandemic.

Partner with us

After making it through this very difficult period, agencies have emerged from the pandemic with stronger labor and health and human service delivery for their residents. State and local governments recognized the necessity of virtual engagement and expanded access to services to meet this critical moment, solving for work, cash, food and healthcare needs.

To learn more about Google Cloud for human services and labor, watch our video on the power of customer innovation or contact your Google Cloud sales representative. Let’s get solving together.