2021 has been a high-stakes year for call centers, with many organizations forced to rapidly scale up their call center operations in response to ongoing pandemic disruptions. We’re proud that 2021 has also been an amazing year for Google Cloud’s Contact Center AI (CCAI), which has helped our customers adapt and thrive, despite the challenging conditions. 

Beginning in January, we launched Dialogflow CX in GA. Agent Assist preview was released in May. Most recently, CCAI Insights GA was announced at Google Cloud NEXT in October. During NEXT, we shared lots of great content on how you can use CCAI to improve your customer experience with these breakout sessions:

• Using CCAI Insights to Better Understand Your Customers 
• Customer Impact with Conversational AI 
• Drive Results by Transforming the Customer Experience with AI-Powered Business Messages

But don’t just take our word for it. We also got a chance to hear how some companies are using CCAI to better reach their own customers, including The Home Depot, TELUS, and Love Holidays. We partnered with CDW to discuss  transforming the contact center with AI and with Quantiphi on how to migrate from Dialogflow EX to CX. Our integration with Looker Block also makes CCAI Insights even more powerful by visualizing contact center metrics. Over the summer, we hosted a Dialogflow CX competition with more than 1,100 participants. Just last month, we showed how we’ve enabled businesses to use AI in their interactions using Google Business Messages. Looking to the future, we talked about the future in our article,“Reimagining your Customer Experience with Conversational AI.”  

Amwell,  a U.S.-based telehealth company that is launching CCAI, including the recently launched CCAI Insights, is among the enterprises harnessing AI to transform its call centers. “With Contact Center AI, we aim to digitize our support for improved operational efficiency and elevated analytics capabilities, while enhancing the customer experience for patients, providers, and staff,” says Paul Johnson, SVP Client Services at Amwell. “Contact Center AI Insights will allow Amwell to better understand why our platform users are reaching out to support and how they feel about the overall experience – valuable insights for our support organization.”

As we recap the momentum of CCAI for 2021, it’s also a good time to review exactly how CCAI works.

What is CCAI?

As the volume of customer calls increases, it’s becoming even more important to make the most of human agents’ time to lower costs and improve customer experiences. CCAI enables you to do just that: it frees human agents to concentrate on more complex calls by providing them with real-time information to better handle those calls. 

Single source of intelligence: Contact Center AI provides a consistent, high-quality conversational experience across all channels and platforms, both human and virtual. Because the “brains” of CCAI are centralized in the cloud, you can apply consistent intelligence across every application in the customer journey. 

Ability to go off-script: Huge cost savings can be realized by having a virtual agent handle voice calls. This is easier said than done, however,  because conversations rarely are completely linear; instead, they meander from topic to topic, which is difficult to handle programmatically in a fixed-path Interactive Voice Response (IVR) system.   

Contact Center AI has the ability to go “off script” — to let callers go down tangents or side paths to the main conversation, while still tracking towards the main objective of the call.  With CCAI, your virtual agents can answer complex questions and complete complicated tasks, including allowing for unexpected stops and starts, unusual word choices, or implied meanings.  Developers can define supplemental questions, and CCAI can easily retain the context, answer the supplemental question, and come back to the main flow.

Versatile fulfillment: CCAI has the ability to handle multiple use cases for the customer with the same virtual agent, which enables you to fully automate routine tasks and deflect calls.  The same virtual agent can take a payment, update information like a phone number, give a customer information on their balance, and process information for other tasks, all within the same conversational flow.

How does CCAI work?

CCAI has three key components:

• Conversation Core: This is the central AI brain that underpins CCAI and its ability to understand, talk, and interact.  It enables and orchestrates high-quality conversational experiences at scale making it possible for customers to have  conversations with a virtual agent that are as good as conversations with a human agent.
  • Understand – Speech-to-text speech recognition understands what customers are saying regardless of how they phrase things, what vocabulary they use, what accent they have, and so on. 
  • Talk – Text-to-speech enables virtual agents to respond to customers in a natural, human-like manner that pushes the conversation along, rather than frustrate them.
  • Interact – Dialogflow identifies customer intent and determines the appropriate next step. You can build conversational flows in a point-and-click interface, and generate automated ML models for human-like conversational experiences.
• Virtual agents with Dialogflow: This component automates interactions with customers, using natural conversation to identify and address their issues. Virtual agents enable customers to get immediate help anytime, day or night. Agent Assist: This component brings AI to human agents to increase the quality of their work, while decreasing their average handling time.  Agent Assist shares initial context and provides real-time, turn-by-turn guidance to coach agents through business processes, as well as full call transcriptions that agents can edit and file quickly. 
• CCAI Insights: CCAI Insights aids your contact center management team in making better data driven decisions for their business by breaking down conversations using natural language processing and machine learning. Having this information allows your business to reduce manual analysis and focus on decision making like which conversations need your attention, where to deploy virtual agent automation to have the biggest impact and how to address your customer needs.  

How does CCAI create experiences for agents and customers? 

When a user initiates a chat or voice call and the contact center provider connects them with CCAI, a virtual agent engages with the user, understands their intent, and fulfills the request by connecting to the backend. If necessary, the call can be handed off to a human agent, who sees the transcript of the interaction with the virtual agent, gets feedback from the knowledge base to respond to queries in real time, and receives a summary of the call at the end. Insights help you understand what happened during the virtual agent and live agent sessions. The result is improved customer experiences and CSAT scores, lower agent handling times, and more time for human agents to spend on more complicated customer issues.

And there you have it: a quick overview of CCAI and its progress in 2021. For more details, check out the documentation or our CCAI solutions page.https://www.youtube.com/embed/6_Gilug2QYw?enablejsapi=1&

For more #GCPSketchnote, follow the GitHub repo. For similar cloud content follow me on Twitter @pvergadia and keep an eye out on thecloudgirl.dev.

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.

NVIDIA GPU-powered instances on Google Cloud provide an optimal platform for organizations to develop their AI applications on the latest hardware and software stack, then seamlessly deploy those applications at scale in production.

Simplifying Workflows to Speedup AI Developments

NVIDIA recently announced the One Click Deploy feature on the NVIDIA NGC catalog, the hub for GPU-optimized AI software. Developed in collaboration with Google Cloud, this feature simplifies the deployment of AI software, to a single click from the NGC catalog.

This allows data scientists to deploy frameworks, software development kits and Jupyter Notebooks directly to Google Cloud’s Vertex AI Workbench, a new managed Jupyter Notebook service on top of Vertex AI, Google’s service for machine learning operations.

Under the hood, this feature launches the JupyterLab instance on Google Cloud Vertex AI Workbench with optimal instance configuration, preloads the software dependencies, and downloads the NGC notebook in one go.

NGC Catalog – One Stop for AI Software

NVIDIA is expanding the rich trove of NVIDIA AI software in the catalog to ensure AI practitioners have everything they need to get started — from frameworks to models.

All of the AI models in the catalog come with credentials. They’re like resumes that show the model’s skills, the dataset that trained it, how to use the model and how it’s expected to perform.

These model credentials provide transparency, which gives developers the confidence in picking the right model for their use case.

The NGC catalog also hosts Jupyter Notebooks tailored for the most popular AI/ML applications. Examples include:

Computer Vision – A collection of models for detecting human actions, gestures and more.

Automatic Speech Recognition – An end-to-end workflow for text-to-speech training.

Recommendation – A collection of example notebooks to help build end-to-end recommendation services.

Serve Robotics uses Vertex AI and the simple easy-to-use interface that the NGC One Click Deploy delivers.

“NGC catalog allows our ML research engineers to launch environments for experiments on Vertex AI with a single click. This saves us the efforts on ML infra setup and lets researchers focus on the ML problem in the computer vision and robotics space more efficiently.”—Kaiwen Yuan, Director of ML/Head of Perceptions & Predictions at Serve Robotics

Accelerate ML Deployments

Explore hundreds of Jupyter notebook examples for speech, computer vision and recommenders and, if you’re just getting started with AI, browse NVIDIA’s collection of Jupyter notebook examples and run it using the One Click Deploy feature on Google Cloud Vertex AI.

Hello! ¡Hola! 你好! नमस्ते! Bonjour! Being greeted in your own language can instantly put a smile on your face, and organizations around the world recognize that messages become more inclusive and powerful when translated into many languages.

As the Head of Product Management for Translation AI at Google Cloud, I am always looking to make our user’s lives easier when it comes to language tools—like Google Cloud’s Document Translation API, which has been used to translate over 2.5 billion documents across Google Translate and our Cloud customers.

But during my time working on translation products, I have seen enterprise users making compromises, like sacrificing cost and speed of translation for quality, or paying much more for high-quality translations and making peace with the slow turnaround times and high costs. That’s why we are excited to see momentum building behind Translation Hub, which launched earlier this year. Built to address organizations’ localization and translation challenges and delight users with an incredibly intuitive and administratively simple offering, Translation Hub makes it easy to translate content at scale—users just need to select the files to be translated, choose the output languages, and then they’re off and running.

In this article, we’ll take a closer look at Translation Hub’s powerful features, and the ways it is helping customers do more with their content.

Enterprise translation, reimagined: fast, lean, and intuitive

Translation Hub is a fully-managed, self-serve translation offering, powered by Google AI and built for the enterprise. With Translation Hub, businesses can instantaneously translate content into 135 languages with a single click, via an intuitive interface that integrates human reviews (i.e., a “human in the loop”) where required.

Translation Hub is designed to be both easy to manage and use. Each organization’s Translation Hub administrator uses the Google Cloud console to onboard and manage business users, typically by adding their email, which triggers an invite to the business user. Once a business user is added, they can sign in and start requesting translations and post-editing reviews.

With advanced features like “Glossary” and “Custom Translation Models (AutoML Translation),” Translation Hub makes it easy for enterprises to control the terminology and inject domain-specific context to meet their unique needs. Furthermore, Translation Hub ensures that organizations own their Translation Memory, for use in future translations or to leverage as a rich, human-validated source of training data for custom translation models (e.g., AutoML Translation). For example, when a user edits a phrase or segment through Translation Hub, this is immediately captured and used in the next translation for all future users who initiate translation where the same content or segment appears.

One of my favorite features is that Translation Hub preserves the layout and format of documents, saving both time and money. And while Translation Hub works with Google Drive (you can ingest and export documents to Drive) and is compatible with all our Google Workspace content sources (e.g., Slides, Docs etc.), it also supports many of the most commonly used formats, like documents and presentations created in Microsoft Office, and scanned and native PDFs.

Organizations need to be able to share the output of AI-powered translation with localization teams or agencies, for review. They need to save time by leveraging glossaries or customer machine learning (ML) models. They need these capabilities in one cohesive platform—and they need all of this to be user-friendly for all kinds of business users. It’s the difference between being able to automate translations in a few scenarios and being able to translate at scale. Google Cloud’s mission is to accelerate every organization’s ability to digitally transform, and with Translation Hub, our customers can leverage the best of AI for their content translations without sacrificing speed, quality or cost.

All of Google’s Translation Innovations within a single product

Translation Hub uses Google Cloud’s Translation API, AutoML Translation, and innovations in Neural Machine Translation Quality Prediction and Translation Memory—all technologies we’ve put to good use inside Google. For example, our localization teams have realized more than $80 million in cost avoidance and savings since 2021, using Google Cloud AutoML Translation and localization expertise.

“In just three months of using Translation Hub and AutoML translation models, we saw our translated page count go up by 700% and translation cost reduced by 90%,” said Murali Nathan, digital innovation and employee experience lead, at materials science company Avery Dennison. “Beyond numbers, Google’s enterprise translation technology is driving a feeling of inclusion among our employees. Every Avery Dennison employee has access to on-demand, general purpose, and company-specific translations. English language fluency is no longer a barrier, and our employees are beginning to broadly express themselves right in their native language.”

To get started with Translation Hub, visit our solution page, and for a deep dive, check out this Google Next ‘22 session, including more details about Avery Dennison’s use case.

Verve Group is an ecosystem of demand and supply technologies fusing data, media, and technology to deliver results and growth to both advertisers and publishers – no matter the screen or location, no matter who, what, or where a customer is. Classifying massive amounts of this unstructured data at scale is the first step in helping to surface relevant, high-quality content to users—and that’s where natural language processing (NLP) comes in.

Verve Group uses the NLP API from Google Cloud’s Vertex AI to fetch data for their internal content classification quality verification and as an additional source for building categorization models. By leveraging the NLP API’s Content Classification models, which are now generally available and offer Google’s latest large language model (LLM) technology, Verve Group powers classification through an updated and expanded training data set with over 1,000 labels and support for 11 languages (Chinese, French, German, Italian, Japanese, Korean, Portuguese, Russia, Spanish, and Dutch join previously-available English). 

Verve Group has been using Google Cloud’s NLP API since day one, because of both the ease of implementation and the quality compared to competing NLP products. With documentation that is “comprehensive and self-explanatory,” the NLP API “allows for fast adoption and implementation, from test models all the way to production,” said Rami Alanko, GM of Verve Group.

Leveraging the NLP API has facilitated Verve Group’s fast go-to-market motions by enabling its customers to quickly discover and classify new content. “Operating on a global level with tens of different regions and languages, we have still been able to maintain high quality for our product and high retention rates with our clients,” Rami shared. “In a recent client case, we achieved 82% improvement in CTR when optimized with content quality measurements enabled by the API. In another client case, we drove brand safety risk down to 0.16% from 4% thanks to classification quality. Along with the new functionalities of the Google NLP, I can only see this trend continuing to strengthen.”

Verve Group is excited to further expand their NLP use cases by leveraging the new Content Classification models, which have already helped them expand their classification inventory, improve the quality and performance of their quality verification for customers, and unlock new use cases for NLP. “Our classification model accuracy improved 41% using Google NLP as a verification partner,” said Rami.  

Additionally, Verve Group is now using the API for metadata analysis on a  large image database. “We browse the database and run the image metadata via our classification. This flow enables us to classify images reliably aligned with our standard classification.  We pretty much use the same data flow for our runtime in-app textual content analysis, therefore allowing for close to real-time consumer engagement,” Rami added.

To learn more about how companies are leveraging NLP API from Google Cloud Vertex AI, click here, and to learn more about Google Cloud’s work with foundation models and generative AI, read The Prompt on Transform with Google Cloud.