Maximize Efficiency in Document Extraction Models with Document AI Workbench GA Release
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Each day, more documents are created and used across companies to make decisions. However, the value in these documents is primarily expressed as unstructured data, which makes the value difficult and manually intensive to extract and use for business processes.
As the number and variety of documents used by businesses proliferate, machine learning (ML) solutions need to be more flexible to handle the broader set of use cases. That’s why we introduced the first Document AI Workbench model, Custom Document Extractor (CDE), in Public Preview at Google Cloud Next ‘22. CDE makes it fast and easy to apply ML to virtually any document-based workflow to extract structured data from unstructured document types, to automate business processes.
CDE lets developers and analysts use their own data to train models and extract fields from documents needed for the business. CDE lets organizations build models faster and with less data — thus accelerating time-to-value for processing and analysis of data in documents.
Today, we announce that Document AI Workbench is Generally Available (GA), open to all customers, ready for production use through APIs and the Google Cloud Console. Document AI Workbench is covered by the Document AI SLA — online and batch document prediction is supported with >=99.9% uptime. Furthermore, Document AI Workbench is now covered by Google Cloud’s GA product terms. For example, Google will notify customers at least 12 months before significantly modifying a customer-facing Google API in a backwards-incompatible manner.
In this blog post, we’ll explore ways customers are already using CDE and Document AI Workbench’s updated capabilities.
What users are saying about Workbench
Deliver higher model accuracy with Workbench
Users leverage Workbench to ultimately save time and money. A third party evaluated Document AI Workbench and concluded that it extracts data more accurately1 than several competing products for document types with variable layouts (e.g. invoice, receipt, bank statements, paystubs). Better accuracy drives higher automation rates, helping Workbench users save time and money.
Chris Jangareddy, managing director for Artificial Intelligence & Data at Deloitte Consulting LLP said, “Google Cloud Document AI is a leading document processing solution packed with rich features like multi-step classify and text extraction to automate sorting, classification, extraction, and quality assurance. By combining Document AI with Workbench, Google Cloud has created a forward-thinking and powerful AI platform for intelligent document processing that will allow for process transformation at an enterprise scale with predictable outcomes that can benefit businesses.”
Mansoor Khan, CEO of OneClinic said, “We help medical professionals scale their clinics through automation. We used Google’s Document AI Workbench to create a model to automatically extract data from patients’ insurance cards as part of our patient check-in software. Workbench is easy to use and we are really happy with the model accuracy — it extracts data more accurately than what we would expect from human data entry.”
Rajnish Palande, VP, Google Business Unit for BFSI, TCS said, “The Google Cloud Document AI Workbench leverages artificial intelligence (AI) to manage and glean insights from unstructured data. The Workbench brings together the power of classification, auto-annotation, page-number identification and multi-language support to help organizations rapidly deliver enhanced accuracy, improved operational efficiency, higher confidence in the information extract, and increased return on investment.”
Build production ready models faster with Workbench
Document AI Workbench helps users create machine learning models faster. For example, a third-party evaluation shows Document AI Workbench trains machine learning models up to 3x faster than a leading competitor. This is an important improvement which lowers total cost of ownership and increases value.
Dallas Dolen, Partner, Google Alliance Leader at PwC said, “Google Document AI Workbench helps to accelerate our custom parser models training as well as improves accuracy and performance using a custom document extractor with human in the loop. It helps us solve complex business problems for our clients in the financial services and healthcare industries.”
Ziang Jia, Senior DocAl Development Lead at Resultant, said, “Document AI Workbench has unlocked a brand-new machine learning development experience for information extraction solutions. Its simplicity and robustness enabled us to build models and deliver a highly accurate outcome in an agile way for a large government agency. We couldn’t be more impressed by its simplicity and robustness and are excited to see how the product will evolve in the future.”
Sean Earley, VP of Delivery Services of Zencore said, “Document AI Workbench allows us to develop highly accurate document parsing models in a matter of days. Our customers have automated tasks that formerly required significant human labor. For example, using Document AI Workbench, a team of two trained a model to split, classify and extract data from 15 document types to automate Home Mortgage Disclosure Act reporting. The mean trained model accuracy was 94%, drastically reducing the operational cost of our customer’s compliance reporting procedures.”
What’s new with Document AI Workbench
The latest Workbench capabilities make it even easier to train and deploy an extraction model:
- With Workbench’s public APIs, you can programmatically create, delete, train, evaluate and deploy models.
- Our updated dataset management tools automatically detect and create existing schema labels from your pre-annotated documents. They also provide you more flexibility when creating and managing schema.
- Our new DocAI Toolkit includes a labeled document converter so that you can easily convert your labeled documents to DocAI’s format and start training faster.
- We’ve reduced the cognitive load for labelers with efficiency enhancements to our Labeling UI.
- The revamped Processor Gallery helps you quickly identify the best model for your use case.
What’s next for Document AI Workbench
We continue to invest in Document AI Workbench to help you automate document processing. Here are a few things we’re working on that we’re excited about:
- Classify document types with the Custom Document Classifier (CDC), coming soon in public preview
- Copy processor versions across projects and processors to streamline managing development and production environments
- Support larger documents (e.g., longer than 50 pages) so you can process a wider array of documents
- Broader (non-latin) language support–equivalent to Document AI OCR
- And many more investments, using state of the art technology, to help you build world class models faster to automate document processing
Document AI Workbench is in GA and ready for production workloads. Learn more via Document AI Workbench documentation or try it out in the Google Cloud Console.
Acknowledgements: Tomas Moreno, Outbound Product Manager, Lukas Rutishauser, Software Engineering Manager, Michael Kwong, Software Engineering Manager, Rajagopal Janani, Software Engineering Manager, Michael Lanning, UX Designer.
1. When trained with 200+ documents
Conversational AI drives better customer experiences
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Conversational AI is opening up a new world of possibilities in areas like customer experience, user engagement, and access to content.
In Cloud AI, we’ve taken Google’s groundbreaking machine learning models in speech and natural language processing and applied them to the contact center space, radically improving the customer experience while also driving down operational costs. It’s shifting the focus of contact centers from the backroom, out of sight (and mind) to the boardroom and the strategic heart of the business.
Our solution, called Contact Center AI (CCAI), is an accelerator of digital transformation as organizations all over the world figure out how to support their customers during these challenging times. One customer that’s redefined the possibilities of AI-powered conversation using CCAI, is Verizon.
Whether through voice calls or chat, Verizon customers will no longer need to go through menu prompts or option trees; they simply say or type their question, and CCAI’s natural-language recognition feature finds the best way to assist them. No stilted speech or robot-like commands.
“These customer service enhancements, powered by the Verizon collaboration with Google Cloud, offer a faster and more personalized digital experience for our customers while empowering our customer support agents to provide a higher level of service,” said Shankar Arumugavelu, SVP & CIO at Verizon.
CCAI is also driving cost savings without cutting corners on customer service. In the past, to improve customer satisfaction (CSAT), you had to hire more agents, increasing operating costs. Conversely, if you reduced the number of agents, your CSAT scores went down. Contact Center AI (CCAI) lets you have both.
CCAI does this by expediting customer requests using virtual agents, assisting live agents, and providing insights on customer interactions to drive improvements back into the system. The result is a better experience for your customers—they get answers faster—and you get lower operating costs. It allows human agents to focus on higher value tasks in their customer interactions.
New products and features add richer customer experience
We are continuing to invest in CCAI and today we are excited to announce the following new capabilities:
Dialogflow CX is the latest version of Dialogflow, a development suite used by over a million developers for building natural, rich conversational experiences into mobile and web applications, smart devices, bots, interactive voice response systems, popular messaging platforms and more. This new version of Dialogflow is optimized for large contact centers that deal with complex (multi-turn) conversations and it is truly omnichannel – you build it once and deploy it everywhere – in your contact centers and digital channels. Dialogflow CX features a new visual builder to create, build and manage virtual agents. It’s available now, in beta.
Lukasz Rewerenda
Dialogflow CX brings conversation state management to a whole new level
Principal Solutions Architect, Randstad (Netherlands)
Agent Assist for Chat is a new module for Agent Assist that provides agents with continuous support over “chat” in addition to voice calls, by identifying intent and providing real-time, step-by-step assistance. Agent Assist enables agents to be more agile and efficient and spend more time on difficult conversations, giving both the customer and the agent a better experience. It transcribes calls in real time, identifies customer intent, provides real-time, step by step assistance (recommended articles, workflows, etc.), and automates call dispositions.
For customers in regulated industries, Agent Assist can remove the risk of agents providing inaccurate information (which can happen due to high agent turnover and limited training). Agent Assist can also surface the latest discount information, deals and special offers, which can be hard for agents to keep track of as this information changes frequently.
Custom Voice, available in beta, is a new capability for CCAI and our Text-to-Speech API that lets you create a unique voice to represent your brand across all your customer touchpoints, instead of using a common voice shared with other organizations. By taking advantage of the custom Text-to-Speech model created with Custom Voice, you can define and choose the voice profile that suits your business and adjust to changes without scheduling studio time with voice actors to record new phrases.
At Google Cloud, we are committed to ensuring that our products and features are in alignment with our AI Principles. If you are interested in Custom Voice, there is a review process to ensure that your use case is aligned with our AI principles. Learn more about Custom Voice and how to sign up here.
Bring Google’s groundbreaking speech models on-prem
For many customers, an all-in approach to public cloud is not an option, which is why we’re extending our AI capabilities to run on-prem. Last week we announced Speech-to-Text On-Prem, the first of our hybrid AI offerings, now generally available. Speech-to-Text On-Prem gives you full control over speech data and since it runs in your own data center, it’s easy to comply with your data residency and compliance requirements. At the same time, Speech-to-Text On-Prem uses state-of-the-art speech models from Google researchers that are more accurate, smaller, and require less computing resources to run than existing solutions.
Take a look at how businesses saved costs and improved customer experience with CCAI in the new commissioned study conducted by Forrester Consulting: “New Technology: The Projected Total Economic Impact™ Of Google Cloud Contact Center AI.” To learn more about CCAI and conversational AI, see how our customers are using the solution and check out these sessions at Next OnAir:
Canadian Bank’s SAP Workload Moved to BigQuery Helps Unlock New Business Opportunities
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When ATB Financial decided to migrate its vast SAP landscape to the cloud, the primary goal was to focus on things that matter to customers as opposed to IT infrastructure. Based in Alberta, Canada, ATB Financial serves over 800,000 customers through hundreds of branches as well as digital banking options. To keep pace with competition from large banks and FinTech startups and to meet the increasing 24/7 demands of customers, digital transformation was a must. To support this new mandate, in 2019, ATB migrated its extensive SAP backbone to Google Cloud. In addition to SAP S/4 HANA, ATB runs SAP financial services, core banking, payment engine, CRM and business warehouse on Google Cloud.
In parallel, changes were needed to ATB’s legacy data platform. The platform had stability and reliability issues and also suffered from a lack of historical data governance. Analytics processes were ad hoc and manual. The legacy data environment was also not set up to tackle future business requirements that come with a high dependency on real-time data analysis and insights.
After evaluating several potential solutions, ATB chose BigQuery as a serverless data warehouse and data lake for its next-generation, cloud-native architecture. “BigQuery is a core component of what we call our data exposure enablement platform, or DEEP,” explains Dan Semmens, Head of Data and AI at ATB Financial. According to Semmens, DEEP consists of four pillars, all of which depend on Google Cloud and BigQuery to be successful:
- Real-time data acquisition: ATB uses BigQuery throughout its data pipeline, starting with sourcing, processing, and preparation, moving along to storage and organization, then discovery and access, and finally consumption and servicing. So far, ATB has ingested and classified 80% of its core SAP banking data as well as data from a number of its third-party partners, such as its treasury and cash management platform provider, its credit card provider, and its call center software.
- Data enrichment: Before migrating to Google Cloud, ATB managed a number of disconnected technologies that made data consolidation difficult. The legacy environment could handle only structured data, whereas Google Cloud and BigQuery lets the bank incorporate unstructured data sets, including sensor data, social network activity, voice, text, and images. ATB’s data enrichment program has enabled more than 160 of the bank’s top-priority insights running on BigQuery, including credit health decision models, financial reporting, and forecasting, as well as operational reporting for departments across the organization. Jobs such as marketing campaigns and month-end processes that used to take five to eight hours now run in seconds, saving over CA$2.24 million in productivity.
- Self-service analytics: Data for self-service reporting, dashboarding, and visualization is now available for ATB’s 400+ business users and data analysts. Previously, bringing data and analytics to the business users who needed it while ensuring security was burdensome for IT, fraught with recurrent data preparation and other highly manual elements. Now, ATB automates much of its data protection and governance controls through the entire data lifecycle management process. Data access is not only open to more team members but it is faster and easier to acquire without compromising security. And it’s not just raw data that users can access. ATB uses BigQuery to define its enterprise data models and create what it calls its data service layer to make it easier for team members to visualize their data.
- AI-assisted analytics and automation: Through Google Cloud and BigQuery, ATB has been able to publish data and ML models that provide alerts and notifications via APIs to customer service agents. These real-time recommendations allow customer service agents to provide more tailored service with contextualized advice and suggested new services. So far, the company has deployed more than 40 ML models to generate over 20,000 AI-assisted conversations per month. Thanks to improved customer advocacy and less churn, the bank has realized more than CA$4 million in operating revenue. During the ongoing COVID crisis, the system was also able to predict when business and personal banking customers were experiencing financial distress so that a relationship manager could proactively reach out to offer support, such as payment deferral or loan restructuring. The AI tools provided by BigQuery are also helping ATB detect fraud that previously evaded rules-based fraud detection by using broader sets of timely and accurate data.
Thanks to the speed and ease of moving data from SAP to BigQuery, ATB is using artificial intelligence (AI) and machine learning (ML) to do things it previously hadn’t thought possible, including sophisticated fraud prevention models, product recommendations, and enriched CRM data that improves the customer experience.
Using the power of Google Cloud and BigQuery, ATB Financial has been able to draw more value from its SAP data while lowering cost and improving security and reliability. Speed to provide data sets and insights to internal team members has improved 30%. The bank also has seen a 15x reduction in performance incidents while improving data governance and security. Dan Semmens projects that the digital transformation strategy built on Google Cloud and BigQuery has both saved millions compared to its on-premises environment and has also realized millions in new business opportunities.
Semmens is looking toward the future that includes initiatives like Open Banking and greater ability to provide real time personalized advice for customers to drive revenue growth. “We see our data platform as foundational to ATB’s 10-year strategy,” he says. “The work we’ve undertaken over the past 18 months has enabled critical functionality for that future.”
Learn more about how ATB Financial is leveraging BigQuery to gain more from SAP data. Visit us here to explore how Google Cloud, BigQuery, and other tools can unlock the full value of your SAP enterprise data.
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Supercharge Marketing with Ready-Made, Centralized Smart Analytics
Your company has lots of valuable marketing data, but it’s spread across many systems and teams. That’s a problem. In fact, only 13% of organisations feel like they’re making the most out of their available customer data today.
And the reason is that data–marketing and customer data–is spread across many different places.
With Cloud, you can bring this data together in one place so you can better analyze and activate it.
Find out how new GCP tools for marketers and hear directly from brands, including Rituals and Waitrose, who are using them to solve use cases like audience segmentation, purchase prediction, and measuring lifetime value.
You’ll learn how Google Cloud is helping IT and marketing teams accelerate digital transformation within their organizations, to be data-driven, and customer-centric.
You’ll walk away with an understanding of how to adopt technology that transforms the way your company delivers marketing campaigns and customer experiences.
How the Telegraph is Reimagining Media with Google Cloud
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Whether they’re reading the newspaper on the way to work, or catching up on the latest headlines on their smartphones, readers expect up-to-the-minute news wherever and whenever makes the most sense for them. As a result, media companies are increasingly looking for ways to improve, expand, and simplify their offerings, and they’re increasingly looking to the cloud to do it.
For more than 160 years The Telegraph has been counted on by readers across the United Kingdom and globally for award-winning news and journalism. An early adopter of cloud technology, it’s been a G Suite customer since 2008 and has already been using Google Cloud Platform to analyze digital behaviors to improve engagement and advertising performance since 2016.
Recently, The Telegraph announced it’s migrating fully to Google Cloud. By migrating all their production and pre-production services, they aim to deliver content faster, provide compelling experiences to readers, and reduce environmental impact.
“We are delighted to announce our newest collaboration with Google Cloud,” said Chris Taylor, Chief Information Officer, The Telegraph. “We have always worked closely with Google as they help us to provide our readers with great experiences on our digital products, collaboration software and internet scale through search. Their continued leadership in projects such as Kubernetes are enabling us to build flexible development environments that truly support DevOps.”
Powering the Digital Publishing Ecosystem
The Telegraph produces large volumes of digital content every day. It was imperative for them to find a cloud provider they could trust to support this ecosystem. By working with Google Cloud they have changed the way they see and engage with data: they can collect new information about their products every second and use that to continually hone their strategy. The Telegraph are placing more confidence and trust in the data captured about their content and now have one of the best available pieces of technology for capturing and analyzing the stories they publish in real-time.
Leveraging AI to support journalists
Time is critical when journalists are on a story, and The Telegraph wants to put important data in the hands of its journalists right when they need it. To do this, it will be using AutoML to classify content for journalists and make it more discoverable. For example, a reporter will be able to bring up relevant assets that link to their stories. It will also apply AutoML to classify Telegraph stock photos to help journalists attach compelling visual content to their stories faster.
Building compelling reader experiences with the help of APIs
Readers have an ever-increasing expectation of personalization. To meet this need, The Telegraph launched My Telegraph, currently live in beta, to offer registered readers personalized news experiences based on their interests or the particular journalists they want to follow. My Telegraph was developed on an API management platform provided by Google Cloud’s Apigee. You can learn more about how it’s applying API management to My Telegraph, in this blog post.
Working for environmental good
The Telegraph is the biggest selling quality newspaper in the UK, an accolade which requires it to print and distribute hundreds of thousands of copies each day. Optimal management of print production is important, and by using a combination of the cloud and machine learning, The Telegraph is better able to predict demand for physical newspapers, maximizing sales and minimizing waste. This makes great business sense for The Telegraph but also has great environmental benefit.
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Value Realization with Google Cloud for Retail SAP Data
Retail engagements have changed drastically over the last few years, and by the virtue of COVID-19 pandemic, retail data and customers’ expectations plummeted. To drive value and transformation across the entire value-chain retailers can make most of Google Cloud’s secure, reliable IaaS by migrating their SAP systems and taking advantage of integrations, insights and innovations. In times of change retail companies can gain maximum visibility of SAP data unlocking Google Cloud’s infrastructure modernization and Big Data and analytics capabilities. Watch the video to understand how Google Cloud and SAP partnership is a golden handshake for retail businesses’ future.
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