Google Cloud Platform Contact Center AI Training

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  • Project Based Learning
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Google Cloud Platform Contact Center AI Training Course Overview

Enhance your skills with Google Cloud Platform - Contact Center AI training by Multisoft Systems. Learn to develop and deploy intelligent virtual agents using Dialogflow, improve customer experiences, and automate support tasks. This expert-led course is perfect for IT and customer service professionals seeking AI-powered transformation in contact center operations.

Google Cloud Platform - Contact Center AI (CCAI) training by Multisoft Systems is designed to empower professionals with the skills to transform traditional customer service centers into intelligent, AI-powered support systems. This comprehensive course covers the fundamentals and advanced functionalities of Google’s CCAI, including integration with Dialogflow, Google Cloud Speech-to-Text, Text-to-Speech, and Agent Assist. Participants will learn to design, develop, and deploy conversational virtual agents that understand natural language and deliver seamless user experiences across multiple communication channels. The course also explores integrating these virtual agents into existing contact center platforms, enabling organizations to enhance operational efficiency, reduce call volumes, and improve customer satisfaction. This training is ideal for IT professionals, system architects, customer experience designers, and support engineers who are looking to leverage AI to automate and optimize contact center workflows. Through hands-on labs and real-world case studies, learners gain practical insights into creating scalable, secure, and responsive AI solutions.

By the end of the training, participants will be well-equipped to implement CCAI solutions that align with business goals and customer expectations. Join Multisoft Systems to gain industry-relevant expertise and stay ahead in the evolving landscape of AI-powered customer support.

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Google Cloud Platform Contact Center AI Training Course curriculum

Curriculum Designed by Experts

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Enhance your skills with Google Cloud Platform - Contact Center AI training by Multisoft Systems. Learn to develop and deploy intelligent virtual agents using Dialogflow, improve customer experiences, and automate support tasks. This expert-led course is perfect for IT and customer service professionals seeking AI-powered transformation in contact center operations.

Google Cloud Platform - Contact Center AI (CCAI) training by Multisoft Systems is designed to empower professionals with the skills to transform traditional customer service centers into intelligent, AI-powered support systems. This comprehensive course covers the fundamentals and advanced functionalities of Google’s CCAI, including integration with Dialogflow, Google Cloud Speech-to-Text, Text-to-Speech, and Agent Assist. Participants will learn to design, develop, and deploy conversational virtual agents that understand natural language and deliver seamless user experiences across multiple communication channels. The course also explores integrating these virtual agents into existing contact center platforms, enabling organizations to enhance operational efficiency, reduce call volumes, and improve customer satisfaction. This training is ideal for IT professionals, system architects, customer experience designers, and support engineers who are looking to leverage AI to automate and optimize contact center workflows. Through hands-on labs and real-world case studies, learners gain practical insights into creating scalable, secure, and responsive AI solutions.

By the end of the training, participants will be well-equipped to implement CCAI solutions that align with business goals and customer expectations. Join Multisoft Systems to gain industry-relevant expertise and stay ahead in the evolving landscape of AI-powered customer support.

  • Understand the core components of Contact Center AI on Google Cloud Platform
  • Design and develop intelligent virtual agents using Dialogflow CX and ES
  • Integrate CCAI with speech technologies like Speech-to-Text and Text-to-Speech
  • Implement Agent Assist to support live agents with real-time suggestions
  • Deploy AI-powered chatbots across multiple channels (voice, chat, etc.)
  • Use Google Cloud tools to manage, scale, and monitor virtual agent performance
  • Enhance customer experience through natural language understanding (NLU)
  • Apply best practices for securing and optimizing CCAI implementations

Course Prerequisite

  • Basic understanding of cloud computing concepts
  • Familiarity with Google Cloud Platform (GCP) services
  • Knowledge of programming (preferably Python or JavaScript)

Course Target Audience

  • AI/ML Developers
  • Cloud Solution Architects
  • Technical Project Managers
  • Contact Center Managers
  • Conversational UX Designers
  • DevOps Engineers
  • IT Professionals working in support automation
  • Business Analysts focusing on customer experience transformation

Course Content

  • 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

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  • 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

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  • 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

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  • 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

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  • 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

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  • 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

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  • 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

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  • 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

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  • Analyze audio recordings using the Speech Analytics Framework (SAF)

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  • 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

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  • 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

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

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  • 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

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Google Cloud Platform Contact Center AI Training (MCQ) Assessment

This assessment tests understanding of course content through MCQ and short answers, analytical thinking, problem-solving abilities, and effective communication of ideas. Some Multisoft Assessment Features :

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Hands-on Google Cloud Platform Contact Center AI Projects

Our Google Cloud Platform Contact Center AI Training course is designed to provide a strong foundation in key concepts with a hands-on learning approach. By working on real-world projects and industry-relevant scenarios, learners gain practical experience and build the confidence to apply best practices in live environments.

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Google Cloud Platform Contact Center AI Corporate Training

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Google Cloud Platform Contact Center AI Training FAQ's

CCAI is a Google Cloud solution that leverages AI technologies like Dialogflow, Speech-to-Text, and Agent Assist to build virtual agents and enhance customer support operations.

This training is ideal for cloud engineers, AI developers, contact center managers, and IT professionals looking to implement AI in customer service environments.

No prior AI experience is required, but a basic understanding of cloud services and programming concepts is recommended.

You will learn how to create intelligent virtual agents, integrate them into contact centers, utilize speech and natural language technologies, and deploy AI-powered customer support solutions.

To contact Multisoft Systems you can mail us on info@multisoftsystems.com or can call for course enquiry on this number +91 9810306956

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