Editor’s note: In this post, I’ll be showing some amazing ways Document AI can help you extract meaning from your documents  – keep reading, or jump directly into a tutorial using the Cloud Console!

Documents are a crucial part of most businesses, used to store and communicate important information. The variety is vast: invoices, contracts, receipts, applications, plus documents unique within industries and geographies. Unfortunately, making the information contained in these documents accessible can be a time-consuming and manual process.

Document AI is a document understanding platform in Google Cloud that takes unstructured data from documents and transforms it into structured data, making them easier to understand, analyze, and consume. By using this technology, you can streamline your document processing workflows, reduce errors, and unlock insights that were previously buried in mountains of paperwork. 

Whether you’re a small business owner or an enterprise looking to bring efficiency to your operations, Document AI has something to offer. So let’s take a look and see what it can do!

Understanding documents with Document AI

When we say that Document AI can understand documents, we mean that it is able to analyze the content within documents and derive meaningful insights from it. This goes beyond simply recognizing the characters and words within a document (which is what traditional OCR technology does) – Document AI can actually comprehend the meaning behind the text.

For example, let’s say you have a contract that needs to be processed. Traditional OCR technology might be able to extract the text from the document, but it would not be able to understand the legal terms and clauses within it. Document AI, on the other hand, can actually interpret the meaning of the text and extract key information such as parties involved, terms and conditions, dates, and signatures.

Document AI offers several pre-built models and processors that are specifically designed to extract different types of data from various document types. Within the specific document types, the processors can perform several tasks such as Optical Character Recognition (OCR), form parsing, splitting, classification or entity extraction. These processors can be customized and combined to create powerful document processing workflows that are tailored to a business’s unique needs.

Let’s look closer at a few of the processors available in Document AI, including the Form Parser, Invoice Parser, Expense Parser, Identity Document Proofing Parser, and Intelligent Document Quality Processor.

Form Parser

This general processor is designed to extract structured data from forms such as application forms, surveys, and questionnaires. It automatically identifies and extracts data from form fields (key-value pairs), such as names, addresses, dates, and other types of structured data; even checkboxes and tables. This processor also leverages deep learning models to extract generic entities that are common in various document types, meaning it can identify if something is an email address, phone number, datetime, organization, quantity, price, person, and more.

In this visualization of the API response, you can see that Document AI has identified several key value pairs that correspond to the form’s fields and the responder’s answers.

Also of interest is that the form parser recognized certain generic entities including: several dates, an address, phone numbers, email, and two people (the responder and their listed emergency contact).

Invoice Parser

This parser is designed to identify and extract relevant information from invoices including a large number of typical invoice fields, but can also be customized (uptrained) to recognize different invoice layouts, languages, and data fields.  Invoices are a critical part of the accounts payable process, making this functionality valuable across industries and companies building A/P features into their products.

In this visualization of the API response, you can see that Document AI has extracted a large number of key-value pairs and even provided normalized values for several of the fields.

Expense Parser

This specialized processor is designed to extract data from receipts and invoices, such as vendor name, date, and total amount paid. It can also identify line items within an invoice and categorize them based on the type of expense (e.g. meals, travel, office supplies). The expense parser makes it easier for you to process expense reports and other financial documents, and it can integrate with other tools and systems to completely automate the entire expense reporting process.

In this visualization of the API response, you can see that Document AI has extracted the text from the receipt and identified several typical entities such as purchase date and time, payment type, and total amount.

Identity Document Proofing Parser

This processor is designed to help predict the validity of ID documents with four different signals.

1. is_identity_document detection: Predicts whether an image contains a recognized identity document.
2. suspicious_words detection: Predicts whether words are present that aren’t typical on IDs.
3. image_manipulation detection: Predicts whether the image was altered or tampered via an image editing tool.
4. online_duplicate detection: Predicts whether the image can be found online.

If suspicious words are detected or the image can be found online, additional information is provided to explain these signals. 

This can be particularly useful for businesses that need to verify the identity of customers or employees as part of their operations. This processor could be used in conjunction with other processors that extract key information such as name, date of birth, ID number, and expiration date from specific identity documents (US Driver License Parser, US Passport, France National ID Parser, etc.).

In this visualization of the API response, you can see that Document AI has passed the document on the first detection point (is_identity_document), but failed the document for the other three items and provided additional information in the evidence fields.

Intelligent Document Quality Processor

This general use processor is designed to detect a variety of document quality issues, such as missing pages, blurry images, low contrast, inconsistent formatting, and incorrect data, and flag these potential issues which could affect their usability, accuracy, or compliance. It can also identify sensitive information that has not been redacted or missing information required by regulatory standards.

This quality assessment is returned as a quality score from 0 to 1, where 1 means perfect quality. If the quality score detected is lower than 0.5, a list of negative quality reasons (sorted by the likelihood) is also returned.

In this visualization of the API response, you can see that Document AI has determined a quality_score of 0.006 and also provided a list of several reasons including document and text cutoff, blurriness, and glare (among others).

Next steps

These are just a few examples of the types of processors that Document AI offers. Perusing the current documentation, you can find it has more than 40 processors, with each providing several functions, to explore. Document AI also offers you the ability to uptrain certain processors and supports the option to build your own custom processor. Across the board, if a document contains structured or unstructured text, Document AI has the capability to extract valuable data from it. 

Get started and learn more by heading to our tutorials in the Cloud Console:

• Get started with Document AI in the Google Cloud console
• Build a document processing pipeline with Workflows
• Uptrain a specialized processor
• Create a Custom Document Extractor in the Google Cloud console
• Create a Custom Document Classifier in the Google Cloud console
• Get started with Human-in-the-Loop

May is Asian Pacific American Heritage Month —a time for us to come together to celebrate and remember the important people and history of Asian and Pacific Island heritage. This feature highlights three AAPI founders from the Google For Startups community.

Read on to learn how these three founders built their businesses, leverage Google tech, and suggestion they have for aspiring entrepreneurs.

CultivatePeople 

Founder: Lola Han

Description: CultivatePeople’s compensation software, Kamsa, provides global market pay rate data and helps companies make data-driven salary decisions so they can attract and retain their most valuable asset: employees. 

Why GCP: “CultivatePeople began using GCP when integrating SSO/SAML authentication after  our SaaS product became a high priority. We were able to exceed our clients’ expectations and increase their level of trust in our platform’s capabilities. We’re currently investigating additional machine learning products, such as Cloud AutoML, that will allow us to quickly deliver exciting features.” 

Note from the Founder: “I grew up with immigrant parents who value stability and are risk-averse. My parents discouraged me from starting my own company because they didn’t want to see me struggle financially or see my health suffer (due to stress). I felt strongly about what CultivatePeople could do, so I started the company as a sole founder in 2017 and watched it double in size year over year since. While the ones I love most may not have cheered me on initially, it was important for me to hang on to the encouraging words of former bosses, executives, and founders to keep me focused on my mission. 

My advice for other AAPI founders is to be a “silent assassin” and believe in the mission and values of your organization. Always remember to stop along the way and: 

 1) Enjoy the journey by celebrating wins and giving yourself credit;

2) Follow your intuition—it’s (almost) always right; 

3) Recognize and invest in your people regularly (ie. give increases more than once a year, if warranted);

4) Give regular words of affirmation to employees on even small achievements.”

Swit

Founder: Josh Lee and Max Lim

Description: Swit is a team collaboration platform that seamlessly combines team chat with task management by allowing teams to turn their conversations into trackable tasks and share tasks to chat with simple drag-and-drop functionality, ensuring everyone is on the same page and projects get done faster.

Why GCP: “Swit is a cross-category hybrid work tool for chat and tasks. This functionality requires more complicated and heavier architecture for performance. So, configuring and managing virtual machines was really challenging to scale up our systems, while handling occasional unexpected traffic surges and frequent updates. Eventually we divided our monolithic architecture into 35 microservices when we launched our official product. The migration to GKE took around one month, and it turned out to be well worth the effort—our systems became able to offer high scalability and enough resilience to keep its uptime no matter what happened. Now we’re operating 84 workloads and 252 microservices with high stability with remarkably low downtime – less than 0.00001%/year.”

Note from the Founder: “As an AAPI founder based in Silicon Valley, I feel proud of the work ethics and diligence fellow Korean American entrepreneurs and professionals have long demonstrated here. Especially with K-pop breaking into the mainstream, I feel even more proud of our culture that strives toward an absolute perfection molded through years of training and dedication. The mission-driven culture of Silicon Valley coupled with Google’s edging technology and creativity really helped us build a product that not only encompasses verticals but also transcends cultures. Swit is growing at an unprecedented rate, and we hope  to join the long list of successful AAPI entrepreneurs here. Swit’s close network with the AAPI community wouldn’t have been possible without Google support. We are grateful for this collaborative environment, and we hope to become the next-generation ambassador for collaboration after Google.” 

Check out more from Swit in their founder story.

WISY 

Founder: Min Chen

Description: Wisy develops technology to bring digital efficiency into the physical world, supporting consumer products businesses and making them thrive in the new economy. All of us have a bad experience when we can’t find the product we want to buy. That is a $1.9T problem in the consumer-packaged goods industry that Wisy is solving with AI and analytics to help manufacturers and retailers sell more by reducing out-of-stocks and waste at a global scale.

Why GCP: “GCP has an intuitive, easy to use interface, was lower cost, and offered preemptible instances with flexible compute options. Some of the reasons why Wisy decided to use GCP include instance and payment configurability, privacy and traffic security, cost-efficiency, and Machine Learning.

Wisy has been able to advance quickly with product development, as well as collaborate better and iterate faster in the creation of our AI models, while reducing costs by 40%. At Wisy, we are solving a problem that affects everyone who shops at a store.“

Note from the Founder: “Two years ago, I moved to San Francisco to expand my second startup, Wisy. This is when I learned that my name ‘Min’ stands for ‘minority.’ I was born in China, raised in a Black community in Panama, received scholarships to attend both Carnegie Mellon and UC Berkeley. I worked for 20 years in several countries, but I have never felt so discriminated against due to my race, ethnicity, gender and age than during my time in Silicon Valley. However, this is also the place I learned that my diverse life experience is my competitive advantage. My background enables me to recruit and relate to people in different countries, create scalable and flexible products for multinational customers, and run global operations efficiently.

My recommendation to AAPI founders is to find strength in their multicultural background. Don’t hide what makes you unique, do not limit yourselves, and do not let others limit you. You will lose your edge when trading authenticity for validation. Be proud and own your story.”

If you want to learn more about how Google Cloud can help your startup, visit our Startup Program application page here and sign up for our monthly startup newsletter to get a peek at our community activities, digital events, special offers, and more.To learn more about how you can help #StopAsianHate during Asian Pacific American Heritage Month and beyond, visit their website here.

One of the largest telecommunications companies in the world, Vodafone is at the forefront of building next-generation connectivity and a sustainable digital future.

Creating this digital future requires going beyond what’s possible today and unlocking significant investment in new technology and change. For Vodafone, a key driver is the use of artificial intelligence (AI) and machine learning (ML), enabling predictive capabilities in enhancing the customer experience, improving network performance, accelerating advances in research, and much more.

Following 18 months of hard work, Vodafone has made a huge leap forward in advancing its AI capabilities at scale with the launch of its “AI Booster” AI / ML platform. Led by the Global Big Data & AI organization under Vodafone Commercial, the platform will use the latest Google technology to enable the next generation of AI use cases, such as optimizing customer experiences, customer loyalty, and product recommendations.

Vodafone’s Commercial team has long focused on advancing its AI and ML capabilities to drive business results. Yet as demand grows, it is easier said than done to embed AI and ML into the fabric of the organization and rapidly build and deploy ML use cases at scale in a highly regulated industry. Accomplishing this task means not only having the right platform infrastructure, but also developing new skills, ways of working, and processes.

Having made meaningful strides in extracting value from data by moving it into a single source of truth on Google Cloud, Vodafone had already significantly increased efficiency, reduced data costs, and improved data quality. This enabled a plethora of use cases that generate business value using analytics and data science. The next step was building industrial scale ML capability, capable of handling thousands of ML models a day across 18+ countries, while streamlining data science processes and keeping up with technological growth.

Knowing they had to do something drastically different to scale successfully, along came the idea for AI Booster.

“To maximize business value at pace and scale, our vision was to enable fast creation and horizontal / vertical scaling of use cases in an automated, standardized manner. To do this, 18 months ago we set out to build a next-generation AI / ML platform based on new Google technology, some of which hadn’t even been announced yet.

“We knew it wouldn’t be easy. People said, ‘Shoot for the stars and you might get off the ground…’ Today, we’re really proud that AI Booster is truly taking off, and went live in almost double the markets we had originally planned. Together, we’ve used the best possible ML Ops tools and created Vodafone’s “AI Booster Platform” to make data scientists’ lives easier, maximise value and take co-creation and scaling of use cases globally to another level,” says Cornelia Schaurecker, Global Group Director for Big Data & AI at Vodafone.

AI Booster: a scalable, unified ML platform built entirely on Google Cloud

Google’s Vertex AI lets customers build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified platform. Built upon Vertex AI, Vodafone’s AI Booster is a fully managed cloud-native platform that integrates seamlessly with Vodafone’s Neuron platform, a data ocean built on Google Cloud.

“As a technology platform, we’re incredibly proud of building a cutting-edge MLOps platform based on best-in-class Google Cloud architecture with in-built automation, scalability and security. The result is we’re delivering more value from data science, while embedding reliability engineering principles throughout,” comments Ashish Vijayvargia, Analytics Product Lead at Vodafone

Indeed, while Vertex AI is at the core of the platform, it’s much more than that. With tools like Cloud Build and Artifact Registry for CI/CD, and Cloud Functions for automatically triggering Vertex Pipelines, automation is at the heart of driving efficiency and reducing operational overhead and deployment times. Today, users simply complete an online form, and then, within minutes, receive a fully functional AI Booster environment with all the right guardrails, controls, and approvals.

Not long ago it could take months to move a model from a proof of concept (PoC) to launching live in production. By focusing on ML operations (MLOps), the entire ML journey is now more cost-effective, faster, and flexible, all without compromising security. PoC-to-production can now be as little as four weeks, an 80% reduction.

Diving a bit deeper, Vodafone’s AI Booster Product Manager, Sebastian Mathalikunnel, summarizes key features of the platform: “Our overarching vision was a single ML platform-as-a-service that scales horizontally (business use cases across markets) and vertically (from PoC to Production). For this, we needed innovative solutions to make it both technically and commercially feasible. Selecting a few highlights, we:

• completely automated ML lifecycle compliance activities (drift / skew detection, explainability, auditability, etc.) via reusable pipelines, containers, and managed services;
• embedded security by design into the heart of the platform;
• capitalized on Google-native ML tooling using BQML, AutoML, Vertex AI and others;
• accelerated adoption through standardized and embedded ML templates.”

For the last point, Datatonic, a Google Cloud data and AI partner, was instrumental in building reusable MLOps Turbo Templates, a reference implementation of Vertex Pipelines, to accelerate building a production-ready MLOps solution on Google Cloud.

“Our team is devoted to solving complex challenges with data and AI, in a scalable way. From the start, we knew the extent of change Vodafone was embarking on with AI Booster. Through this open-source codebase, we’ve created a common standard for deploying ML models at scale on Google Cloud. The benefit to one data scientist alone is significant, so scaling this across hundreds of data scientists can really change the business,” says Jamie Curtis, Datatonic’s Practice Lead for MLOps.

Reimagining the data scientist & machine learning engineer experience

With the new technology platform in place, driving adoption across geographies and markets is the next challenge. The technology and process changes have a considerable impact on people’s roles, learning, and ways of working. For data scientists, non-core work now is supported by machines in the background—literally at the click of a button. They can spend time doing what they do best and discovering new tools to help them do the job.

With AI Booster, data scientists and ML engineers have already started to drive greater value and collaborate on innovative solutions. Supported by instructor-led and on-demand learning paths with Google Cloud, AI Booster is also shaping a culture of experimentation and learning.

Together We Can

Eighteen months in the making, AI Booster would not have happened without the dedication of teams across Vodafone, Datatonic, and Google Cloud. Googlers from across the globe were engaged in supporting Vodafone’s journey and continue to help build the next evolution of the platform.

Cornelia highlights that “all of this was only possible due to the incredible technology and teams at Vodafone and Google Cloud, who were flexible in listening to our requirements and even tweaking their products as a result. Alongside our ‘Spirit of Vodafone,’ which encourages experimenting and adapting fast, we’re able to optimize value for our customers and business. A huge thank you also to Datatonic, who were a critical partner throughout this journey and to Intel for their valuable funding contribution.”

The Google & Vodafone partnership continues to go from strength to strength, and together, we are accelerating the digital future and finding new ways to keep people connected.

“Vodafone’s flourishing relationship with Google Cloud is a vital aspect of our evolution toward becoming a world-leading tech communications company. It accelerates our ability to create faster, more scalable solutions to business challenges like improving customer loyalty and enhancing customer experience, whilst keeping Vodafone at the forefront of AI and data science,” says Cengiz Ucbenli, Global Head of Big Data and AI, Innovation, Governance at Vodafone.

Find out more about the work Google Cloud is doing to help Vodafone here, and to learn more about how Vertex AI capabilities continue to evolve, read about our recent Applied ML Summit.

Earlier this year, we launched the Google Cloud skills challenge, which provides 30 days of free access to training to build your cloud knowledge and an opportunity to earn skill badges that showcase your Google Cloud competencies. Today, we’re adding a Native App Development track to the skills challenge, joining the Getting Started, Data Analytics, Kubernetes, Machine Learning (ML) and Artificial Intelligence (AI) tracks. 

The Native App Development track is designed for cloud developers who want to learn to build serverless web apps and Google Assistant applications on Google Cloud using Cloud Run and Firebase. Specifically, you’ll have an opportunity to earn three skill badges in the Native App Dev track: Serverless Firebase Development, Serverless Cloud Run Development, and Build Interactive Apps with Google Assistant. To earn a skill badge, you complete a series of hands-on labs and take a final assessment challenge lab to test your skills.

Here’s an overview of each badge.

Serverless Firebase Development

To earn this skill badge, you’ll learn how to build serverless web apps, import data into a serverless database, and build Google Assistant applications using Firebase, Google’s backend-as-service platform for creating mobile and web applications.

Serverless Cloud Run Development

For this badge, you’ll discover how to use Cloud Run, a fully managed serverless platform, to connect and leverage data stored in Cloud Storage. You’ll learn how to use Cloud Run to build a resilient, asynchronous system with Pub/Sub, build a REST API gateway as well as build and expose services. 

Build Interactive Apps with Google Assistant

To earn the final skills badge, you’ll build Google Assistant applications by creating a project in the Actions console, integrating Dialogflow, testing your action in the Actions simulator, and adding Cloud Translation API to your assistant application. 

Ready to jump into the skills challenge? Sign up here. 

You can also check out this quick video below to learn how to join the skills challenge.