This module introduces the foundational concepts of Voice AI and SIP Telephony, covering the transition from traditional PSTN systems to modern SIP-based communication. Learners will explore how voice calls are processed, managed, and secured in real-world AI-powered telephony solutions. The content provides a clear understanding of key components like SIP trunks, call flows, and authentication methods.

This module provides a hands-on guide to setting up AWS infrastructure for a Voice AI agent, covering EC2 instance creation, server access, and Docker containerization. Learners will gain practical experience in configuring and managing a cloud-based AI server environment. Key skills include understanding the speech-to-text-to-speech pipeline and deploying AI applications using Ubuntu and Docker.

This module provides a comprehensive walkthrough of the Voice AI agent code, covering its structure, core functionalities, and integration components. Learners will gain an understanding of how to interpret and analyze the script's logic and design.

This module guides learners through the process of acquiring and configuring API keys for speech-to-text, language models, and text-to-speech services. It covers key steps for setting up and managing these critical access credentials. Learners will gain practical skills in working with API integrations.

This module explores the purpose and functionality of LiveKit in real-time voice communication, its advantages for AI projects, and how to set it up. Learners will gain insight into its problem-solving capabilities and practical implementation steps.

This module guides learners through the essential steps of setting up a Voice AI agent, including structuring prompts, installing dependencies, and containerizing the application using Docker. By the end, learners will have the skills to configure and deploy a functional AI agent.

This module teaches learners how to build, launch, and test a Voice AI agent using Docker. It covers the process of creating Docker images, running containers, and verifying functionality through interactive testing tools.

This module covers the integration of Sarvam AI Bulbul V3 TTS into a Voice AI agent, including configuration, Docker image rebuilding, and testing. Learners will gain hands-on experience in implementing and deploying text-to-speech technology within an AI pipeline.

This module covers the integration of AI voice agents using the SIP protocol, including how to select and configure a suitable SIP provider for voice AI systems. Learners will gain practical knowledge on establishing connections and evaluating provider options.

This module provides learners with the essential steps to set up a VoBiz AI account and purchase a DID number, focusing on managing voice services and understanding call capacity limitations.

This module covers the process of setting up and configuring an inbound SIP trunk for call routing, including linking a DID number, configuring an origination URI, and updating a containerized application. Learners will gain practical skills in telecommunications infrastructure setup and call handling.

This module guides learners through the process of configuring an inbound SIP trunk in LiveKit, including creating the trunk, linking it, and testing the call flow. It provides hands-on experience with call routing and voice AI agent integration.

This module guides learners through the setup and configuration of an outbound Voice AI system for an eye hospital. It covers the creation of SIP trunks, Docker images, Python environments, and the implementation of agent scripts to enable automated call handling. Learners will gain hands-on experience with building and testing an AI-powered outbound calling system.

This module focuses on enhancing voice clarity and effectiveness in text-to-speech systems through adjustments to pace and pitch. Learners will also test the final call outcomes after applying these optimizations. The content provides practical skills for improving speech output quality and ensuring reliable communication.

This module explores the principles and strategies for scaling and optimizing call handling systems. Learners will gain insights into system architecture, performance testing, and various scaling techniques to ensure efficient and reliable call processing.

This module guides learners through the process of deploying a dashboard to monitor voice call records, covering installation, configuration, and security setup for web-based access. Learners will gain hands-on experience with NGINX, Git, and Certbot to ensure secure and functional dashboard deployment.