Explore the exciting world of voicebots and learn to create interactive, personalized voice conversations with your customers. In this course, you’ll discover voicebot fundamentals—from strategic planning to practical implementation using DialogFlow. You’ll design effective conversation flows, leverage AI for speech recognition (STT), and TTS to deliver customized responses for an exceptional customer experience. Gain the skills to build intelligent voicebots that boost customer loyalty and operational efficiency. Enroll now to start your voicebot journey!
The student will be able to define various aspects of Artificial Intelligence—its history, areas, and fundamental concepts used throughout the course. They will also learn the techniques and tools available in the market to implement AI in industries such as healthcare, telecommunications, education, and more.
We will review our Conversational AI course. The student will define key concepts like intents and entities, and learn to design conversation flows for Chatbots, Voicebots, and Conversational Agents in general.
The student will learn techniques and tools for automated human language processing and understanding. They will apply NLP, NLU, and NLG conceptually for tasks like sentiment analysis, machine translation, text summarization, and conversation-level generation.
The student will distinguish between IVR, Cognitive IVR, and voice Conversational Agents, implementing conversational flows for each while considering linguistics, behavior design, and conversational UX.
The student will learn about Asterisk, the leading open-source communications PBX used to connect phones, computers, and apps, offering flexibility and scalability for enterprise communications.
The student will install Asterisk 20 on an AWS Amazon Linux EC2 instance, configure files, codecs, and database for course Voicebots, and connect to inbound/outbound call providers and VoIP systems like Zoiper.
The student will configure AGI (Asterisk Gateway Interface) to run automated calls using Python and JavaScript.
The student will implement IVR and Cognitive IVR using Asterisk to compare them with the Voicebots developed in the course.
The student will implement VoiceBots using the Provider–Integrator model for Level 1 VoiceBots applicable to any industry or market.
The student will implement VoiceBots using the Provider–Integrator–Facilitator methodology for Level 1 and 2 VoiceBots, integrating business logic via APIs (Webhooks).
The student will integrate leading industry connectors like DialogFlow, Amazon Lex, and IBM Watson into VoiceBots, learning when to choose each based on requirements.
The student will configure and implement STT tools to transform user speech into text for NLP processing.
The student will configure and implement TTS tools to deliver responses to user requests.
The student will implement NLP processes and techniques for Voicebots using NLPToolkit for Python and the Natural library in Node.js.
The student will build their first VoiceBot using the P–I model, STT, TTS, and NLP with Node.js and Python.
The student will learn what Intents, Utterances, and Prompts are, their differences, and shared characteristics. They will create, manage, delete, and edit intents using DialogFlow or another Conversational AI platform.
The student will create training phrases for various intents, teaching AI models to identify intents and extract relevant information, improving accuracy in Human–Bot conversations.
The student will implement actions in an intent and link custom payloads in P–I and P–I–F models. They will create, delete, and edit parameters for use in responses, contexts, and facilitators.
The student will implement intent responses, creating custom parallel text variants for different providers.
In this module, the student will build an FAQ VoiceBot with ~50 intents to practice all previous concepts.
The student will apply system entities to their intents, learning to use and customize them for specific VoiceBot scenarios.
The student will create custom text, synonym, and list entities and apply them to VoiceBot scenarios.
This module covers building a customer-service VoiceBot to review entity use cases, implementations, and key attributes.
The student will use nested contexts, including fallbacks and parameters for intent responses.
The student will implement custom contexts via intents or context groups, controlling parameters, fallbacks, and responses based on user context.
This module covers building a banking-helpdesk VoiceBot to manage conversational contexts across scenarios.
The student will learn about APIs—their purpose and use for VoiceBots—and how to test and integrate them via Fulfillments.
The student will implement dynamic parameters in conversations, maintain them in contexts, and use them in responses via Fulfillments.
The student will use actions to communicate with Fulfillments, retrieving custom responses based on user intents and parameters.
The student will create custom payload responses for different scenarios, learning their structure and implementation in VoiceBots.
This module covers creating an Orders VoiceBot to explore Fulfillment aspects, actions, parameters, and custom responses.
The student will learn about Machine Learning, its importance, common models in Conversational Agents, and their role in language understanding and processing.
The student will refine Chatbot understanding and accuracy via supervised training, data analysis, and error debugging.
The student will validate user conversations in DialogFlow and other AI platforms to ensure quality.
The student will use conversation history for adjustments, training, and improvements in their Chatbots.
The student will implement VoiceBots for outbound and inbound calls, configuring caller info, call states (Busy, Voicemail, etc.), and call transfers to human agents.
The student will integrate email, SMS, and push notifications within conversations.
To earn certification, the student must build a VoiceBot implementing course learnings.
Copyright © 2025 IA Chile. All Right Reserved