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Google Cloud Platform - Contact Center AI

( Duration: 4 Days )

This Google Cloud Platform - Contact Center AI training course provides skills to design, develop, and deploy customer conversational solutions using Contact Center Artificial Intelligence (CCAI). You will also learn some best practices for integrating conversational solutions with your existing contact center software, establishing a framework for human agent assistance, and implementing solutions securely and at scale.

By attending Google Cloud Platform - Contact Center AI workshop, delegates will learn to:

Define what Google Contact Center AI is.
Explain how Dialogflow can be used in contact center applications.
Describe how natural language understanding (NLU) is used to enable Dialogflow conversations.
Implement a chat virtual agent.
Implement a voice virtual agent.
Describe options for storing parameters and fulfilling user requests.
Deploy a virtual agent to production.
Identify best practices for design and deployment of virtual agents.
Identify key aspects, such as security and compliance in the context of contact centers.

Knowledge of Google Cloud Products
Desirable but not required: Knowledge of a programming language such as Python or JavaScript.

The Google Cloud Platform - Contact Center AI class is ideal for:

Conversational Architects
Contact center virtual agent and application developers
Business managers

COURSE AGENDA

Overview of Contact Center AI

Define what Contact Center AI (CCAI) is and what it can do for contact centers
Identify each component of the CCAI Architecture: Speech Recognition, Dialogflow, Speech Synthesis, Agent Assist, and Insights
Describe the role each component plays in a CCAI solution

Conversational Experiences

List the basic principles of a conversational experience
Explain the role of conversation virtual agents in a conversation experience
Articulate how STT (speech to text) can determine the quality of a conversation experience
Demonstrate and test how speech adaptation can improve the speech recognition accuracy of the agent
Recognize the different NLU (natural language understanding) and NLP (natural language processing) techniques and the role they play in conversation experiences
Explain the different elements of a conversation (intents, entities, etc.)
Use sentiment analysis to help with the achievement of a higher-quality conversation experience
Improve conversation experiences by choosing different TTS voices (Wavenet vs. Standard)
Modify the speed and pitch of a synthesized voice
Describe how to leverage SSML to modify the tone and emphasis of a synthesized passage

Fundamentals of Building Conversations with Dialogflow

Identify user roles and their journeys
Write personas for virtual agents and users
Model user-agent interactions
List the basic elements of the Dialogflow user interface
Build a virtual agent to handle identified user journeys
Train the NLU model through the Dialogflow console
Define and test intents for a basic agent
Train the agent to handle expected and unexpected user scenarios
Recognize the different types of entities and when to use them
Create entities
Define and test entities on a basic agent
Implement slot filling using the Dialogflow UI
Describe when Mega Agent might be used
Demonstrate how to add access to a knowledge base for your virtual agent to answer customer questions straight from a company FAQ

Maintaining Context in a Conversation

Create follow-up intents
Recognize the scenarios in which context should be used
Identify the possible statuses of a context (active versus inactive context)
Implement dialogs using input and output contexts

Moving from Chat agent to Voice agent

Describe two ways that the media type changes the conversation
Configure the telephony gateway for testing
Test a basic voice agent
Modify the voice of the agent
Show how the different media types can have different responses
Consider the modifications needed when moving to production
Be aware of the telephony integration for voice in a production environment

Taking Actions with Fulfillment

Define the role of fulfillment with respect to Contact Center AI
Characterize what needs to be collected in order to fulfill a request
Identify existing backend systems on the customer infrastructure
Use Firestore to store mappings returned from functions
Appreciate that the interaction with customers’ data storage will vary based on their data warehouses
Implement fulfillment using Cloud Functions
Implement fulfillment using Python on AppEngine
Describe the use of Apigee for application deployment

Testing and Logging

Debug a virtual agent by testing intent accuracy
Debug fulfillment by testing the different functions and integrations with backend systems through API calls
Implement version control to achieve more scalable collaboration
Log conversations using Cloud Logging
Recognize ways that audits can be performed

Intelligent Assistance for Live Agents

Recognize use cases where Agent Assist adds value
Identify, collect, and curate documents for knowledge base construction
Set up knowledge bases
Describe how FAQ Assist works
Describe how Document Assist works
Describe how the Agent Assist UI works
Describe how Dialogflow Assist works
Describe how Smart Reply works
Describe how real-time entity extraction works

Drawing Insights from Recordings

Analyze audio recordings using the Speech Analytics Framework (SAF)

Integrating a Virtual Agent with Third Parties

Use the Dialogflow API to programmatically create and modify the virtual agent
Describe connectivity protocols: gRPC, REST, SIP endpoints, and phone numbers over PSTN
Replace existing head intent detection on IVRs with Dialogflow intents
Describe virtual agent integration with Google Assistant
Describe virtual agent integration with messaging platforms
Describe virtual agent integration with CRM platforms (such as Salesforce and Zendesk)
Describe virtual agent integration with enterprise communication platforms (such as Genesys, Avaya, Cisco, and Twilio)
Explain the ability that telephony providers have of identifying the caller and how that can modify the agent design
Incorporate IVR features in the virtual agent

Environment Management

Create Draft and Published versions of your virtual agent
Create environments where your virtual agent will be published
Load a saved version of your virtual agent to Draft
Change which version is loaded to an environment

Methods of Compliance with Federal Regulations

Describe two ways that security can be implemented on a Contact Center AI integration
Identify current compliance measures and scenarios where compliance is needed

Best Practices for Virtual Agents

Convert pattern matching and decision trees to smart conversational design
Recognize situations that require escalation to a human agent
Support multiple platforms, devices, languages, and dialects
Use Diagflow’s built-in analytics to assess the health of the virtual agent
Perform agent validation through the Dialogflow UI
Monitor conversations and Agent Assist
Institute a DevOps and version control framework for agent development and maintenance
Consider enabling spell correction to increase the virtual agent's accuracy

Encarta Labs Advantage

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Get jumpstarted from newbie to production ready in a matter of few days

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All our trainings are conducted in workshop mode with more focus on hands-on sessions

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Contact us for delivering this course as a public/open-house workshop/online training for a group of 10+ candidates.

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