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Build Chatbots Using Google Dialogflow Corporate Training
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The Build Chatbots Using Google Dialogflow Corporate Training by Multisoft Virtual Academy is designed for organizations seeking to enhance customer engagement through AI-driven chatbot solutions. This training introduces your team to the fundamentals of Google Dialogflow, including Natural Language Processing (NLP), intent classification, entity recognition, webhook integration, and multi-platform deployment.
With a strong focus on hands-on learning, this course helps participants understand how to create conversational agents that streamline business processes and improve customer support. Our industry-expert trainers guide your employees through real-time chatbot development projects, preparing them to meet enterprise-level demands.
Whether you're from customer service, sales, IT, or product development, this course is tailored to empower your workforce with in-demand AI and chatbot development skills. Flexible scheduling and live instructor-led sessions ensure minimal disruption to business operations while maximizing learning outcomes.
Build Chatbots Using Google Dialogflow training is a hands-on program designed to teach participants how to create intelligent, conversational AI agents using Google Dialogflow. The course covers core components like intents, entities, contexts, and fulfillment, enabling learners to design, develop, and deploy chatbots across platforms. Ideal for developers and AI enthusiasts, it provides practical skills to automate user interactions and build scalable, real-world chatbot solutions.
- 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 AI.
- 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 on 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.
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- Describe two primary differences between Dialogflow Essentials (ES) and Dialogflow Customer Experience (CX).
- Identify two design principles for your virtual agent which apply regardless of whether you implement in Dialogflow ES or CX.
- Identify two ways your virtual agent implementation changes based on whether you implement in Dialogflow ES or CX.
- List the basic elements of the Dialogflow user interface.
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- List the basic elements of the Dialogflow CX 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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- Use Dialogflow tools for troubleshooting.
- Use Google Cloud tools for debugging your virtual agent.
- Review logs generated by virtual agent activity.
- Recognize ways an audit can be performed.
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- Characterize the role of fulfillment with respect to Contact Center AI.
- Implement a virtual agent using Dialogflow ES.
- Use Cloud Firestore to store customer data.
- Implement fulfillment using Cloud Functions to read and write Firestore data.
- Describe the use of Apigee for application deployment.
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- Describe how to use the Dialogflow API to programmatically create and modify the virtual agent.
- Describe connectivity protocols: gRPC, REST, SIP endpoints, and phone numbers over PSTN.
- Describe how to 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.
- Describe how to 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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- Analyze audio recordings using the Speech Analytics Framework (SAF).
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- Recognize use cases where Agent Assist adds value.
- Identify, collect and curate documents for knowledge base construction.
- Describe how to 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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- Describe two ways security can be implemented on a CCAI 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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- Identify the stages of the Google Enterprise Sales Process.
- Describe the Partner role in the Enterprise Sales Process.
- Detail the steps in a Contact Center AI project using Google’s ESP.
- Describe the key activities of the Implementation Phase in ESP.
- Locate and understand how to use Google's support assets for Partners.
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