If you’ve been exploring recently-launched consumer generative AI tools like Bard and thinking about how to build similar experiences for your business, Generative AI App Builder, or Gen App Builder for short, is here to get you started.

Gen App Builder is part of Google Cloud’s recently announced generative AI offerings and lets developers, even those with limited machine learning skills, quickly and easily tap into the power of Google’s foundation models, search expertise, and conversational AI technologies to create enterprise-grade generative AI applications. 

“Google Cloud’s leading AI technology enables STARZ customers to discover more relevant content, increasing engagement with, and the likelihood of completing the content served to them,” says Robin Chacko, EVP Direct-to-Consumer, STARZ. “We’re excited about how generative AI-powered search will help users find the most relevant content even easier and faster.”

Gen App Builder is exciting because unlike most existing generative AI offerings for developers, it offers an orchestration layer that abstracts the complexity of combining various enterprise systems with generative AI tools to create a smooth, helpful user experience. Gen App Builder provides step-by-step orchestration of search and conversational applications with pre-built workflows for common tasks like onboarding, data ingestion, and customization, making it easy for developers to set up and deploy their apps. With Gen App Builder developers can: 

• Build in minutes or hours. With access to Google’s no-code conversational and search tools powered by foundation models, organizations can get started with a few clicks and quickly build high-quality experiences that can be integrated into their applications and websites. 
• Combine the power of foundation models with information retrieval to find relevant, personalized information. Enterprises can build apps that understand user intent via natural language, and surface the right information with associated citations and attributions from a company’s public and private data. They can also fully control what data their applications access and the content or topics they want to address.
• Build multimodal apps that can respond with text, images, and other media. Gen App Builder supports not just text, but also other modalities such as images and videos. It allows developers to build apps using a combination of text and images as inputs to find information across documents, photos, and video content, enabling richer customer interactions. 
• Combine natural conversations with structured flows. Developers can granularly blend the output of foundation models with controls to ground answers in enterprise content, and step-by-step conversation orchestration to guide customers to the right answers.
• Provide the ability to transact and connect to third party apps and services. Gen App Builder makes it simple to create digital assistants and bots that not only serve content, but also connect to purchasing and provisioning systems to enable transactions from the conversational UI, and escalate customer conversations to a human agent when the context demands.

A new generation of conversational AI experiences and assistants 

Consumers of enterprise applications expect to interact with technology in a seamless, conversational way to quickly find the information they need and act on it. Gen App Builder can help reinvent these customer and employee experiences by ingesting large, complex datasets that are specific to your company–from websites, documents, and transactional systems like billing and inventory, to emails, chat conversations, and more. These AI-powered apps can synthesize information across all of these sources to provide specific, actionable responses, using only the data you have provided. 

Some of the most popular uses are in customer service, where generative apps can contribute to increasing revenue, customer satisfaction, and customer loyalty. For example, if a retail customer reaches out to modify an order, a virtual agent can help them change it to another product. The customer doesn’t even need to provide the new product name—they can just upload an image and let the agent guide them through the rest. Watch this demo to see how a retail chatbot can use multimodal capabilities to help a consumer navigate various options on the website, including giving the customer ideas on how to use the product and even helping them complete the purchase with the ability to transact within the conversational UI. This scenario could apply to multiple industries and use cases, ranging from consumer goods and public services, to finance and internal corporate systems like intranets.

Combining the power of Google-quality search with foundation models

Finding the right information from data across the organization is a critical requirement within any enterprise. Yet it can be challenging to build high-quality enterprise search experiences with existing tools. Current systems struggle to understand user intent, are difficult to implement and customize, and don’t provide a high-quality user experience. 

One of the most exciting features of Gen App Builder is the ability to combine the power of Google-quality search with generative AI to help enterprises find the most relevant and personalized information when they need it. With Gen App Builder, enterprises can build conversational search experiences across their public and private data in minutes or hours with no coding experience. 

Enabling multimodal search across text, images and video within the enterprise is a key aspect of the search experiences in Gen App Builder. In addition to providing high-quality search results, Gen App Builder can conveniently summarize the results and provide corresponding citations in a natural, human-like fashion. Gen App Builder also automatically extracts key information from the data and enables personalized results for users. Watch this demo to see how these capabilities can come together to transform the search experience for employees at a financial services firm. The ability to integrate Google-quality search within the enterprise’s applications means they can enjoy a new level of data utilization, drive increased process efficiencies, and provide delightful experiences to their employees and customers.

“Customers have been shopping at Macy’s for generations. Being able to deliver 360° personalization and contextual recommendations will help ensure that Macy’s is still providing future generations of shoppers with a seamless, exceptional experience,” said Bennett Fox-Glassman, Senior Vice-President, Customer Journey, Macy’s. “We’ve already realized an increase in revenue per visit and conversion rates had great success using Google Cloud’s AI technology and are looking forward to exploring how these latest announcements bring together Natural Language Processing and Generative AI capabilities to deliver next-gen search and conversational experiences for our customers.”

The ability to intuitively interact with complex data across a variety of sources allows organizations to better serve their customers and deliver more relevant offerings. Combined with conversational and fulfillment abilities, the potential for improving customer engagement and employee productivity is immense. We’re excited to see how developers and enterprises use a mix of these capabilities to power new experiences and revenue opportunities.

If you’re interested in a closer look at the Gen App Builder, tune into this session at the Data Cloud & AI Summit. Take a step forward to getting hands-on and join the waitlist for our trusted tester program. And finally, bookmark our generative AI landing page to keep abreast of the latest news, updates and possibilities from this exciting new world of Gen Apps.

Google Cloud introduced Document AI to automate document processing and to streamline workflows with state-of-the-art machine learning models. With the deep neural networks, the models generalize the learning from seeing hundreds of thousands variations of the documents. But when information is missing or ambiguous on a document – like a missing address or entity name – a human may need to search for it…often on Google.

With Document AI, we are bringing the power of this “Google search” to help customers understand their documents. This means that the same Google knowledge graph technology that helps you find the name, address or phone number of your favorite restaurant can now enrich your document extraction with the right name, fully qualified address, and updated phone number.

Here is a sample payslip…

Imagine a bank employee entering this to capture a customer’s income to qualify them for a loan. When extracting information from this payslip, what employer name should she key in? She might take the time to go into Google and find the right correct legal entity name; or she might just guess and move on, potentially creating data reconciliation headaches down the line.

With Document AI, there is a better way. Our native integration with the knowledge graph means that we can deliver both the specific text from the payslip as well as Google’s best understanding of the actual name of the company that operates at this address. This is an important step to translate from “what has been said” on a document to “what does it mean”.  By normalizing the value as you process millions of documents, you are improving accuracy and consistency at the beginning of the data processing workflow, making downstream integration, data analytics and business intelligence tasks at ease.

How EKG Enrichment Works

In a nutshell, Knowledge Graph is a knowledge base that uses a graph data model to integrate interlinked entities, including objects, events, processes or abstracted concepts. Google announced its Knowledge Graph in 2012 as a way to organize information from the Web and to enhance Search results. Different from Google Knowledge Graph, Cloud EKG (Enterprise Knowledge Graph) focuses on entities that are more relevant to enterprise customers, such as organization, product, people, locations, etc.

In EKG, every node is called an entity. Each entity in the graph represents an object, such as an organization. EKG aggregates all the information about a thing into a single entity, thus each entity represents a distinct and identifiable real world concept. The uniqueness of these entities in the graph is one of the reasons that make EKG useful. The edges between nodes are called relationships. When representing attributes, the relationships can be considered as properties, such as the name of a company, the price of a product, etc. When representing relationships, they connect entities in the graph, such as the CEO of a company, the seller of a product.

Entity linking, as its name suggests, is the task of assigning a unique identity to entities mentioned in text, images or videos. In the context of EKG, it connects the text mentions to entities in the graph. Under the hood, the recognition takes both the mention and its context information into consideration for linking to the best matching candidates in the graph.

Entity Enrichment, is essentially linking entities in EKG to documents, and using the attributes of linked entities to enrich the extracted information.  The entity linking on documents is based on the understanding of both documents and the entities in the graph. First the document parser annotates entity mentions, which provides semantic meanings to the content of the document. Then Entity Linking selects a list of entities from EKG based on the types of these mentions, and matches to the best entity by comparing the attributes found in the document with the the relationships of the selected entities.

How to leverage the enrichment result

Knowledge graph enrichment is a built-in feature for Lending DocAI , Procurement DocAI and Contract DocAI today, and we are actively working on expanding it to cover more document types and parsers on the platform. To use the knowledge graph enriched values, look out for the entities fields under normalizedValue, returned by the API.

  {
      entities: [
         {
          "textAnchor": {
             "content": "Google Singapore"
          },
          ….
           "normalizedValue": {
              "text": "Google Asia Pacific, Singapore"
          }
        }
      ]
    }

To learn more, check out the Document AI webpage, and EKG Enrichment page to see the list of supported parsers and fields.

Editor’s note: February is Black History Month—a time for us to come together to celebrate the diverse set of experiences, perspectives and identities that make up the Black experience. Over the next few weeks, we will highlight Black-led startups and how they use Google Cloud to grow their businesses. Today’s feature highlights Acclinate and its founders, Del and Tiffany. 

As patients, as caregivers, and as parents taking our own children to the doctor, we want recommended medications to be safe and effective. It’s a right everyone deserves.

It’s known that certain medications don’t work in the same way in all populations. For example, Albuterol, a medication often prescribed for asthma, is less effective in 67% of all Puerto Ricans and 47% of Black Americans. These problems—which can have deadly consequences—result from historically limited diversity in pharmaceutical clinical trials. 

We founded our startup Acclinate to integrate culture and technology to achieve more inclusive clinical research. Help pharmaceutical companies and healthcare organizations access and engage communities of color so research is more inclusive.

Bridging the health equity gap by building trust

It’s important that health research organizations access and engage communities of color so their efforts reflect all the people they serve. Take a disease like diabetes, which affects a significantly higher proportion of Black Americans. When you look at even recent clinical trials for diabetic drugs, the representation of Black Americans among participants is only in the low single digits, despite comprising 13 percent of the U.S. population and more than 40 percent of diabetes patients in this country. Industry leaders have been aware of the lack of diversity issue, but some have chosen to ignore it or brush it aside. The biggest problem, in our opinion, is that there have been no penalties for not achieving higher diversity figures in clinical trials, and only minor financial repercussions to pharma/biotech companies when their treatments either do not work across all groups once approved, or there is a lack of uptake by all groups due to the lack of testing in those groups. The lack of clinical trial diversity has adversely impacted the reputation of the industry and the ability to recruit diverse populations in the future. 

Acclinate integrates culture and technology to promote diverse patient representation in medical research. Our approach is not transactional. We build trust through our  #NOWINCLUDED community, which is an ongoing, ever-expanding digital platform that educates and engages with communities of color on health issues.

#NOWINCLUDED includes a website app, and social media presence where members can learn information about diseases, particularly those with greater negative impacts on people of color, such as cancer, diabetes, and cardiovascular diseases. Members can share stories and ask questions. By providing access to trusted resources about these health issues and the latest clinical research, we empower Black people to take control of their health and consider  participating in research that is shaping the future of healthcare.  

For healthcare-related organizations, we offer the opportunity to better understand the attitudes, aspirations, and unmet needs of underrepresented minority communities. Data from #NOWINCLUDED feeds our HIPAA-compliant SaaS platform, e-DICT™ (Enhanced Diversity in Clinical Trials), which uses predictive analytics and machine learning to identify individuals matching the requirements and most likely to be receptive to participation in a particular clinical trial.

Acclinate scales its platform with Google Cloud

We rely on Google Cloud services, including Vertex AI, to know whom to ask, when to ask, and how to ask for clinical trial participation. With Vertex AI, we enjoy a unified platform for developing our artificial intelligence models, including tools for preparing and storing our datasets. We can easily train and compare models using AutoML, which requires minimal ML expertise or effort with its intuitive graphical interface. This allows us to leverage more than ten ordinal and categorical data points to determine in real time a community member’s likelihood to enroll, which we call our Participation Probability Index (PPI). Our models evolve in an iterative process the more we interact with, and learn about, our community members.

We follow the pay-per-use Google Cloud Platform architecture model using serverless technology, which helps reduce infrastructure management costs and lets us focus on product development and engaging with communities across the U.S.

CloudSQL, a fully managed relational database service, integrates easily with BigQuery so we can glean insights for our clients in real time, all with Google Cloud’s robust security, governance, and reliability controls. Virtual Private Cloud (VPC) gives us scalable and flexible networking for our cloud-based resources and services. We also use Identity and Access Management (IAM) to simplify oversight of Google Cloud resource permissions for different user groups and roles, with appropriate security protections. 

API Gateway manages our APIs using Cloud Functions, which both use consumption-based pricing, plus give our developers consistent and highly secure access to our services through a well-defined REST API. We use Memorystore for Redis to reduce platform latency. This is done with a fully managed service powered by the Redis in-memory data store, which builds application caches for fast data access. All of this comes together to provide an outstanding experience for our platform’s users and contributors.

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Expanding influence with the Google for Startups Accelerator for Black Founders

Three months of one-on-one Google support and mentorship as part of the most recent Google for Startups Accelerator : Black Founders cohort not only helped us build our product, but also helped us earn external credibility. People use Google every day, so whether we’re trying to engage in conversations with industry experts or with somebody in a rural community, it is helpful to have the buy-in of a globally-recognized brand as we take on a historically difficult, systemic issue with challenges around trust. Getting access to the products, best practices, and people we need to build and grow through the Accelerator program has been priceless. For example, working with the Google AdWords team helped us generate important traffic from people interested in learning more about #NOWINCLUDED or sharing their story with us. Jason Scott, who leads the Google for Startups Accelerator: Black Founders program, is still connecting us to people in his network and identifying key opportunities for us months after the program wrapped. He continues to demonstrate that he is invested in seeing us succeed. 

Our company has made great progress against our goals, in part thanks to receiving capital from the Google for Startups Black Founders Fund. We received $100K in non-dilutive funding along with Google Cloud credits, Google.org Ads grants, and hands-on support. We used the funds to pay for the transition and development costs associated with moving to Google Cloud. The Google support and accountability has been incredible. After receiving the Google for Startups Black Founders Fund award, we’ve gone on to raise another $1M and moved our cloud from Salesforce to Google Cloud. 

We also had the amazing opportunity to be selected as one of six companies to take part in a face-to-face web conference with Sundar Pichai, Google’s CEO. We were thrilled to hear him explain his vision around health equity and the role Google plays. Ultimately, for us, it’s not just about the funding we get, but we are also gratified to receive support from an entity that truly believes in addressing this issue. We know Google is aligned with our mission of health and racial equity. 

Championing diversity in clinical trials

Our 2022 looks bright. We expanded our presence to Washington, D.C. as part of the Johnson & Johnson Innovation JLABS ecosystem. We were also selected to take part in the BLUE KNIGHT initiative created between Johnson & Johnson Innovation and the Biomedical Advanced Research and Development Authority (BARDA) under the U.S. Department of Health and Human Services. 

Acclinate is also on track to have contracts with five of the top 25 largest biopharmaceutical companies in the U.S this year. They’ve taken note, as has the Food and Drug Administration (FDA), that the lack of diversity in clinical trials represents a significant health concern—to the extent that the FDA has provided strong guidance for pharmaceutical companies to  diversify their clinical trials. At the same time, the industry is also responding to pressure from communities of people of color to make equitable representation a priority.

Today, we are in the fortunate but challenging position to have significant inbound opportunities coming our way. In response, we continue to recruit and hire talented people to join our team. On the technology side, we are happy to be aligned with Google Cloud to have powerful cloud infrastructure that will scale with us, as well as high-caliber champions united in partnership. With people’s lives at stake, we are passionate in our commitment to helping ensure medications do what they are supposed to do: heal and improve the quality of life for everyone who takes them. 

Hear Acclinate cofounders Del Smith and Tiffany Whitlow chat with Google’s Head of Startup Developer Ecosystem Jason Scott and fellow Black Founders Fund recipient Bobby Bryant about building on Google Cloud in a recent Google for Startups Instagram Live. 

If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more

Data science can be applied to business problems to improve practices and help to strengthen customer satisfaction. In this blog, we address how the addition of Looker extends the value of your Google Cloud investments to help you understand your customer journey, unlock value from first-party data, and optimize existing cloud infrastructure.    

Data is a powerful tool 

While everyone doesn’t need to understand the nuts and bolts of data technologies, most people do care about the value data can create for them — how it can help them do their jobs better. Within the data space, we are seeing a trend of ongoing failure to make data accessible to “humans” –   the industry still hasn’t figured out how to put data into the hands of people when and where they need it, how they need it. 

What if everyone in your organization could analyze data at scale, and make more-informed, fact-based decisions?   Data and insights derived from data are valuable but only if your users see it.  We think Looker helps solve that. 

Solutions tell a larger story of how it all fits together 

Across all verticals and industries,  businesses benefit from knowing and understanding their customers better. Many business goals are to increase revenue by improving product recommendations and pricing optimization, improving the user experience through targeted marketing and personalization, and reducing churn while improving retention rates.  To help reach  these goals, key strategies should focus on understanding customer needs, their motivations, likes and dislikes, and using all available data – in other words, put yourself in the shoes of your customer.    

Our goal with Looker solutions is to offer the right level of out-of-box support that allows customers to get to value quickly, while maintaining the necessary flexibility. We aim to offer a library of data-driven solutions that accelerate data projects. Many solutions include Looker Blocks (pre-built pieces of code that accelerate data exploration environments) and Actions (custom integrations) that get customers up and running quickly and lets you build business-friendly access points for Google Cloud functionality like BQML, App Engine and Cloud Functions.  

Below, you’ll find a sampling of the newest Looker solutions. 

Listening to customers by looking at the data

Looker’s solution for Contact Center AI (CCAI), helps businesses gain a deeper understanding and appreciation of their customers’ full journey by unlocking insights from all their company’s first-party data. Call centers can converse naturally with customers and deliver outstanding experiences by leveraging artificial intelligence. CCAI‘s newest product —CCAI Insights — reviews conversations support agents are having, finding and annotating the data with the important information, and identifying the calls that need review. We’ve partnered with the product teams at CCAI to build Looker’s Block for CCAI Insights, which sets you on the path to integrating the advanced insights into your first-party data in Looker, overlaying business data with the customer experience.

Businesses can better understand contact center experiences and take immediate action when necessary to make sure the most valuable customers receive the best service. 

Realizing full business value of First-Party data

Looker for Google Marketing Platform (GMP) provides marketers the power to unlock the value of their company’s first-party data to more effectively target audiences.  The Looker Blocks and Actions for GMP offer interactive data exploration, slices of data with built-in ML predictions and activation paths back to the GMP.  This strategic solution continues to evolve with the Looker Action for Google Ads (Customer Match), the Looker Action for Google Analytics (Data Import) and the Looker Block for Google Analytics 4 (GA4).

• The Looker Action for Customer Match allows marketers to send segments and audiences based on first-party data directly into Google Ads. Reach users cross-device and across the web’s most powerful channels such as Display, Video, YouTube, and Gmail.  The entire process is performed within a single screen in Looker, and is able to be completed in a few minutes by a non-technical user. 
• The Looker Action for Data Import can be used to enhance user segmentation and remarketing audiences in Google Analytics by taking advantage of user information accessible in Looker, such as in CRM systems or transactional data warehouses.
• The Looker Block for Google Analytics 4 (GA4) expands the solution’s support with out-of-the-box dashboards and pre-baked BigQuery ML models for the newest version of Google Analytics.The Looker Block offers up reports with flexible configuration capabilities to unlock custom insights beyond the standard GA reporting. Customize audience segments, define custom goals to track and share these reports with teams who do not have access to the GA console.

From clinical notes to patient insights at scale

Taking a look at the Healthcare vertical, the Looker Healthcare NLP API Block serves as a critical bridge between existing care systems and applications hosted on Google Cloud providing a managed solution for storing and accessing healthcare data in Google Cloud. The Healthcare NLP API uses natural language models to extract healthcare information from medical text,  rapidly unlocking insights from unstructured medical text and providing medical providers with simplified access to intelligent insights.  Healthcare providers, payers, and pharma companies can quickly understand the context and relationships of medical concepts within the text, such as medications, procedures, conditions, clinical history, and begin to link this to other clinical data sources for downstream AI/ML.   

Specifically, the natural language processing (NLP) Patient View (pictured below) allows you to review a single selected patient of interest, surfacing their clinical notes history over time. It informs clinical diagnosis with family history insights, which is not currently captured in claims, and captures additional procedure coding for revenue cycle purposes.

The dashboard below shows the NLP Term View which allows users to focus on chosen medical terms across the entire patient population in the dataset so they can start to view trends and patterns across groups of patients. 

This data can be used to: 

• Enhance patient matching for clinical trials 
• Identify re-purposed medications
• Drive advancements for research in cancer and rare diseases
• Identify how social determinants of health impact access to care

Managing Costs Across Clouds

Effective cloud cost management is important for reasons beyond cost control — it provides you the ability to reduce waste and predictably forecast both costs and resource needs. Looker’s solution for Cloud Cost Management offers quick access to necessary reporting and clear insights into cloud expenditures and utilization. 

This solution brings together billing data from different cloud providers in a phased approach: get up and running quickly with Blocks optimized for where the data is today (Google Cloud, AWS or Azure) as you work towards more sophisticated analysis for cross-platform planning and even cloud spend optimization with the mapping of tags, labels and cost centers across clouds.

The Looker Cloud Cost Management solution provides operational teams struggling to monitor, understand, and manage the costs and needs associated with their cloud technology with a comprehensive view into what, where and why they are spending money.

Making better decisions with Looker-powered data

Leading companies are discovering ways to get value from all of that data beyond displaying it in a report or a dashboard. They want to enable everyone to make better decisions but that’s only going to happen if everyone can ask questions of the data, and get reliable, correct answers without using outdated or incomplete data and without waiting for it. People and systems need to have data available to them in the way that makes the most sense for them at that moment.  

It’s clear that successful data-driven organizations will lead their respective segments not because they use data to create reports, but because they use it to power data experiences tailored to every part of the business, including employees, customers, operational workflows, products and services.   

As people’s way of experiencing data has evolved, more than ever before, dashboards alone are not enough. You can use data as fuel for data-driven business workflows, and to power digital experiences that improve customer engagement, conversions, and advocacy. 

From Nov. 9 – 11th, Looker is hosting its annual conference JOIN, where we’ll be showing new features, including how we help to:

• Build data experiences at the speed of business
• Accelerate the business value with packaged experiences 
• Unleash more insights for more people in the right way – Deliver data experiences at scale

There is no cost to attend JOIN.  Register here and learn how Looker helps organizations build and deliver custom data-driven experiences that goes beyond just reports and dashboards, scales and grows with your business, allows developers to build innovative data products faster, and ensures data reaches everyone.