AI Voice Agent Fundamentals: Automate Communications with No-Code AI

1. Welcome to the Class!: So AI agents are here, and let me tell you something. Communication across all industries will never be the same again. Welcome to the A to Z master class on understanding, creating and using your own custom AI agents. The scores right here is tailored for creators and business owners who want to understand and adopt AI agents on their businesses and on their systems, and be able to automatically reach out to more people at scale, qualify better leads, automatically book more calls and close more deals. And again, completely automate every single repetitive human communication inside of your systems. So why should you take this course? We all know that the AI space is exponentially evolving every single week, if not a day. With this latest discovery of AI agents, you are now able to completely automate any human interaction inside of your business and start operating scale, like those big tech companies that have hired human communication departments and haven't adopted this tech yet. And again, in the next 2 hours, you will be learning everything about AI voice agents. I assume that you're a complete beginner and you have absolutely no idea around AI agents. And we're going to start by analyzing, first of all, what are AI agents? How do they operate? The different types of AI agents, and how can they be absorbed by pretty much every single industry out there? Then I'm going to show you how to access the software that enables you to use these AI agents and how to create custom AI voice agents tailored to your I'm very excited to have you here. I genuinely believe that this is one of the most revolutionary courses that I've ever created, and I'm gonna see you in the first lesson of the course. 2. Your Class Project!: Welcome to the course. In this very short first lesson, I'm going to be assigning the class project that you're called to complete by the end of this course. And again, in the next lesson, we're going to be discussing what are voice agents, how they can be adopted by your industry, how you can create voice agents, how you can deploy voice agents, and all that stuff. And the class project that you're called to complete again by the this course right here is to actually go ahead and create your custom AI agent and describe it, right? Describe the custom voice agent that you created in the class project description of this course right here. So again, a successful class project, right, will include you describing, giving us information here in the class project description about the AI agent that you created. So I will be personally reviewing every single class project that you guys submit. So there's also a perfect chance to interact with me and start like an awesome conversation, right? So thank you so much for being here. I'm going to see you in the next lesson of the score. 3. Discover What AI Voice Agents Can Dov: So I would like to welcome you to the first lesson of this course right here. Now, again, this course elaborates on a topic that is truly cutting edge and super groundbreaking and can help pretty much every single business that involves human communication. And this is AI agents. Now, in this first introductory lesson of the course, we're going to be setting basic foundation around the topic again of AI agents, and I'm going to be discussing with you about what are A agents and how they can be leveraged by any pretty much business that involves human communication. I'm super, super excited to have you here, and let's dive with analyzing what are AI agents, right? The rise of intelligent conversational automation, right? Because that's at the end of the day what we try to achieve with the implementation of AI voice agents, conversational automation, which is, again, not an easy task considering that conversation is something done, you know, from one human person to another, and we want to automate this, right? And this is where AI comes into play. So AI has advanced, right, to the point where machines can now talk, listen, and understand us, right? So, back in the day when only Cha GBT was around, it was a language model, but you can only type down stuff to interact with GBT. Now we have open ended artificial intelligence that is available to us, and it can talk, listen, again, and understand us. So what voice agents are is the next evolution of communication, right, in the space. It's software, hold natural conversations, provide information, and even take action, right? So you can imagine how many stuff can be automated and tackled with the usage of this technology right here. So let's define what a voice agent, again, is. An AI voice agent is a computer based system that's capable of interacting and speaking with humans through real time voice interaction, right? So it's pretty much an AI that interacts with human beings on real time and can be prompted automate tasks based on these interactions. So it combines, again, automatic speech recognition, natural language understanding and text to speech technology to simulate a genuine two way conversation because guess what? Even on genuine human interactions between two human beings, some answers and some data that you are going to be inputting in this interaction of yours, like we're not talking about AI here, right, are going to feel kind of robotic, right? Kind of not that continuous because you have been repeating yourself multiple times if you're laborating on a topic that is super straightforward, right? So AI is here to replace these robotic repetitive again interactions of yours. So unlike simple phone bots, these agents can adapt, learn from context, and personalize their responses based again on data they're capturing on real time from these interactions. So what is the purpose of AI agents? These agents, again, exist to automate those repetitive human conversations that we don't want to have in the first place, simply because there is no creativity involved. And my theory, right, with AI is that AI will replace, that's my personal theory after interacting with all of these different AI models, and adapting AI to my business, I genuinely believe that artificial intelligence will replace everything that is robotic and repetitive, right on our human lives. And that's what as humans, we don't want to do. We don't want to do robotic repetitive tasks. We want to tap in to our creative nature, and that's what AI allows us to do, right? Creative people can only do creative stuff nowadays. Everything else that's robotic and repetitive to allow us to be creative right now can be outsourced to AI, right? And that's what AI voice agents do. So they enable businesses to communicate at scale, right, while keeping a human tone because guess what? What is robotic and repetitive and can be automated through AI can be also scaled. Right? So by taking over, again, routine calls are again repetitive and again robotic, they free people to focus their creativity on complex or emotional interactions that require empathy and judgment, which are values that us human beings have and, you know, these A agents simply don't, right? Because this is where you can really express your true human nature rather than trying to outsource, you know, empathy to AI. It's not that easy, right? So let's talk about the mechanics behind a conversation of these AI agents before we break them down into next lessons of this course. So every single conversation follows four quick stages, right? Four quick stages around these AI agents. The first one is listening. Now, obviously, AI works by gathering data, analyzing the data that it has gathered, and then giving you the correct, again, output, right? And the input used to be prompt, right, back in the day. When I'm saying back in the day, I mean, like, you know, a year ago, because AI is super new. Back in the days pretty much a year ago, if you understand how fast the space, you know, has evolved. And back in the day, meaning a year ago, you need to understand how correctly create prompts and engineer prompts to input the correct information. So AI understands what you're saying. But nowadays, AI is so good at understanding that you don't even have you know, to give out the best inputs, right, for AI to receive information. That's why we're now able to have normal conversations with AI, right, because the processing per of artificial intelligence, right, has evolved, right? So the first step is listening. So speech, again, is converted through text through a ASR software. So automatic speech recognition software. So I'm talking to the AI. AI transcribes what I'm saying to text through the ASR, right? Then this transcribed text, again, is analyzed, and this system, right, this natural language understanding system or NLU identifies the intent that you have and the context of this conversation. So this is the data processing, if you will, that you just gave to the AI or a prospect gave to the AI or whoever is interacting with this agent gave. So then comes the reasoning, right? An AI model determines what's the most relevant response to give to this prompt that has been analyzed or action to be taken because these AI agents can also take action, right? And finally, after listening, understanding and reasoning has taken place, it is time for the AI to speak and give you data back. So this is where we have the so called DDS or text to speech technology. And this technology delivers again, natural sounding reply instantly. So if you think about it, what this whole system is structured on is a language AI model, right? What these agents do is that they analyze text and they create text based on the context that they understood out of this text. But this text that they analyzed has originated through this automatic speech recognition software. So we have sourced text through automatic speech recognition, and we have given out our output right by the DDS, so by text to speech technology. And again, the process happen in milliseconds, so it's super genuine, right? They create a fluid, human like exchange. And isn't this exactly how us humans process information? First, we listen, right? Some people don't listen, but first we listen, then we need to understand what we listened. Then we add reasoning, we add our own layer of creativity, and then we speak back. That's how humans interact. And that's how AI also interacts. So you can see why so again, valuable in today's market. So, what can AI agents actually do? Where can we apply these AI agents? Now, if you have been listening to this lesson right here, you might have so many ideas already on what these agents can do. So the first thing that they can obviously do is make and receive phone calls, like, automatically. They can hold multi turn conversations, adapting tone and wording depending on who they're talking to. Or they can qualify your leads, for example, if you give them a big lead list, they can go ahead and actually qualify these leads. They can schedule meetings or collect information from different people and sort this information because, again, we can add more actions to these AI agents, right, more automations, right? They can access databases that you give them and CRMs to update records on real time based on the information that they collect and detect sentiment and just by phrasing, again, adjust their phrasing accordingly depending on who they're talking to, which is kind of scary, to be honest with you. They can also provide cold summaries and analyts after each interaction. So you are in charge of what's happening, right? And how your leads are doing, how your clients are doing, how is the quality of the information that you're gaining from these AI agents. So let's talk about some real world application here before we end with this lesson. These A agents have already been used in multiple different fields like sales, customer support, healthcare, education, and we're going to be diving deeper in these different fields in just a second here in the next lessons of the scores. But, for example, in sales and marketing, these agents have been used in cold outreach, right, reaching out to people who don't know you, lead nurturing, reaching out again to people who already know like and like you yous trying to nurture their trust, right? A agents can be used into this or follow ups of people that have shown interest, but have disappeared, right? It's super easy to follow up with these AA agents. Regarding customer support, if you have someone, you know, asking questions, those frequently asked questions can be answered through AI or ticket riots on boarding. This can be really super helpful because all of these against steps right here, for example, in healthcare, appointment reminders, right, patient check ins, cold outreach in sales and marketing, lead nurturing follow ups, all of these processes, right? Are applied based on a framework, on a mental framework that we follow as human beings, right? And it's super, let's say, scripted what we're going to be doing, regardless of who we're talking to if we're performing gold outreach, we're following a specific mental framework, right? And this mental framework can be noted down, distributed to AI, and just created and applied in bulk. Right? It can be scaled, right? Because there's no creativity involved into these tasks right here. There's no creativity on patient check ins. You're just literally following a list. There's no creativity on appointment reminders. You're literally just following a guide, right? And ecommerce, for example, order confirmations, right, delivery updates, feedback calls. There's no creativity involved there. Which means that you can really outsource this to AI agents, and virtually any process that depends on voice communication can be 100% automated by using these AI agents. So why do companies adopt these agents? The first reason why, like, companies adopt these agents, and they have been adopting these agents, right, for multiple years now, they weren't AI agents, but companies have been adopting, again, agents, voice agents on their phones. For example, you can recall sometimes when you're calling a bank, right, or when you're calling, you know, a service, usually an automated voicemail comes into place, but now this has AI. So first of all, these agents have 247 availability right? There's no downtime. There's no holidays. There's no, like, crazy payments. There's no kids, no pregnancy leaves, right? They they just work forever. It's super easy to scale with these agents, so they can literally handle hundreds of calls simultaneously, which is something that if you wanted to do back in the day, you needed huge manpower, right? They have consistent tone, consistent messaging. Their tone doesn't change based on how much they slept last night, right? They of course have lower operational costs compared to human agents. This makes total sense. Their cost is substantially. It's crazy how cheap these agents are, right? You instantly document, again, through automatic transcriptions and summaries, what's happening in these calls. So it's super easy for the admins, right, people who use these agents to document what's happening, right, and be able to tweak some stuff based on the results that you're getting. And they deliver data driven insights into customer behaviors and needs, which would be something not that easy to do if you were a human being, or at least if you wanted your human agents to do it would be way, way more expensive. So it's good for us to note at this part of this lesson right here at this final part of this lesson that these agents are here to augment humans, not replace humans. And I think that I made it pretty clear in this first son of the scores that these AI voice agents are here to replace robotic repetitive tasks, right, not genuine human creativity. If you are genuinely a creative person, and by the way, you don't need to be a creative director or a creative entrepreneur, right, to be a creative person, all of us human beings, have you know, creativity built in to our software, if you will. So you are a creative dude. You're a creative person or a creative lady. Right, you do not need to worry about these agents replacing you. AI agents complement human teams rather than eliminate them, right? Yes, they can handle high volume repetitive tasks, but people manage complex and emotional situations, and whenever creativity is involved regarding placement of these agents, distribution of these agents, analysis of the data, human beings are super, super involved there. And again, this hybrid model combines AI efficiency with human empathy, resulting in better customer experiences and improved productivity. That's the goal of us. And this one I'm going to show you how to do in this course right here. So what is the future of voice communication? Well, here's the thing. We can't really speculate when the future will come, and we can't really speculate when this next generation of voice agents will be here. But at the rate at which artificial intelligence is improving and evolving on a daily basis, until this point, this isn't that far away from now. So agents will become more realistic and emotionally aware at some point. They will literally have persistent memory, so they will never forget, right? And they will also have context across conversations, right? So they will have a huge dataset of context, which is, like, super powerful if you're communicating with multiple people. There will be an integration with multinodal AI, for example, video text data in these agents will make them again more powerful at some point, there will be a real time language translation available, which just will expand your reach into multiple regions, multiple jurisdictions, right. Of course, there will be continuous learning for each interaction, and this already exists, and voice communication is steadily shifting from this scripted automation to a fully intelligent conversation taking into place. Right? The goal of these AI agents and what you can actually achieve right now with this course right here is to implement these AI voice agents to be better the human being, it makes sense, right they don't make mistakes, and they have endless context, right? So these are the key takeaways of this first lesson right here. The first one is that AI agents can, again, simulate human conversation through listening, understanding, and speaking. They automate communication at scale without losing personalization, which is super important if for example, if you want to nurture leads or to attract more people or to close deals, right? They are powered by ASR, NLU, LLMs, and DTS technologies, which we're going to be analyzing in the next assen of this course, like in depth, and they're used across multiple industries for sales, support, and customer engagement. We talked about ecommerce, business, healthcare, hospitality, and we are going to be talking about these, again, different industries that use AI voice agents in just some minutes in the next lessons. And again, they represent the future of business communication. They're super fast, super scalable, and human sounding, which is crazy to think about. It's crazy I'm creating this course right here, right? And after again analyzing these agents for multiple weeks, engaging with them, I can tell you that this course will completely blow your mind, right? So coming up in the next lesson, we're going to talk about this technology behind voice agents, right? The ASR, the NLU, the LLM, the TDS technologies. Those are concepts that you need to understand if you want to apply these agents correctly to your business and to your daily life, right? So I'm super excited to have you here, seeing in the second lesson of the 4. Uncover the Tech Powering Voice AI: First lesson of this course, we discussed about what AI agents are. How do they operate and all of the different applications that they have, right? And they can have in every single potential business that involves robotic, repetitive, human conversational interactions. Now, in this second lesson right here, we're going to be discussing about the technology behind these AI agents. How do these agents actually operate? And how can we gain value out of these agents? It's super important for you to understand how these agents operate be able to position them in the correct departments of your business, right to gain the optimal outcome from these agents. And that's exactly what we're analyzing in this lesson right here. So enough with this introduction, let's dive into this presentation of, again, the technology behind AI voice agents, right? We're going to be talking about how these machines listen, how they think, and how they're able to speak Again, like human beings, and we had a small snippet, if you will, of this technology in the previous lesson of discourse, right? Behind every AI voice agent, there is a combination of powerful technologies, right, that allow it to understand language, interpret meaning, so contextualize the information that it receives and communicate naturally. At the end of the day, if we break things down, you will see that these AI voice agents take voice and transcribe voice into text. They analyze this text. They add context based of all of the datasets that they have around the topic to this text, they generate replies, and then they give out these replies as again, voice, right? But all of these tasks are done in milliseconds, so it literally feels like a genuine human interaction. So let's actually go ahead and break down these systems right here. These are the four components of voice artificial intelligence. ASR NLU, LLM TTS. So what is ASR? ASR is automatic speech recognition, and what this does is that it pretty much again, recognizes voice and converts this voice into text because these again voice agents are built in the back end of, like, language processing models and text processing models. So we need to convert human voice when somebody is interacting with our agents into text. Then the natural language understanding system comes into place and extracts, again, the intent and the context from the text that these agents have converted from voice, right? After the NLU, has done its job. It's time for the large language model to again come into play. And this large language model, which is this dataset of information generates intelligent human like replies, which will then be converted into speech with the TDS, right, the text to speech feature. And this turns, again, the AI's words, right, text back into realistic voice output. Now, as you can imagine, there are different levels of ASR, different levels of NLU, different levels of LLM and TTS right, with different bagwids and different capabilities, and depending on the quality of these four components, we have different types of AI voice agents and better voice agents or worse voice agents. But these, again, four pillars work together in real time to power every single conversation that you have with these agents or these agents have with other people, right? These four components in order to work, they require power. They require energy. But if you have enough power and enough energy, you can scale these agents to operate, again, through these systems. To multiple people, right, which is something that you can do with human power. You can't exactly scale human power like that, right? So let's actually dive and analyze each and every single one of these four elements, right? The ASR, the NLD element, DS. Let's start with ASR, right, which stands for, again, automatic speech recognition. ASR systems, as we discussed about capture audio and convert spoken words into accurate text. It's super straightforward. When I'm interacting with a voice agent or when a potential client, a potential lead, a potential patient, depending on the business and, you know, the industry that these agents operate on speak, right? They give information to the agent, and these words are converted into accurate text. And these modern ASR engines use deep learning models that have been trained on millions of audio samples. This way, when, for example, I have, let's say, a small accent, right, when I'm talking or I do a small I make a small, let's a grammatic mistake, right? Then these ASR engines understand it and correct it while giving this prompt back to our AI. So they handle, again, accents. They handle background noise and various speaking speeds with remarkable, again, precision, which is super, super important when we're prompting these agents because imagine that these agents are prompted by normal human people, right? Then there's background noise. There are accents, right? There's like, various speaking speeds, and these agents need to be able to understand right, and break through these barriers. So the better the ASR, the smoother and faster this entire conversation between the agents and us feels, right? So then after we've converted, again, speech into text, it's time for the natural language understanding to put into place. So NLU again, interprets the transcribed text to understand what the user truly wants. So now we have the text, we pretty much need to understand the context of this text right here. So it detects intent, for example, book an appointment or the name, the title, the location. It gathers data, it extracts the data, extracts intent out of the text that we have, again, gathered through automatic speech recognition. And again, it also understand context from prior messages. So by analyzing the sentence structure and tone, it ensures that the AI responds logically and appropriately to the prompt or to the information that each person has given to the AI. So now it is time for the LLM, the large language model to put into place. And the LM is pretty much like how this whole system operates, right? Because this was just data, right, conversion from, again, speech to text. This was data processing, if you will, or at least data extraction, and now it is time to again process with the brain of the system. So LM pretty much describe decides how to respond based on the recognized intent and conversation history, right? So again, we gather data, we gather intent, we gathered again, more information, and now it is time to process it and see how we will respond, right? So modern models like B four or proprietary fine tuned LLMs can reason, summarize, and adapt, tone dynamically. That's why we use AI. That's why again, like AI, there is huge application of AI in these voice agents. This is where a simple voice agent becomes an AI voice agent due to the fact that there is an LLM involved, and it can reason, it can summarize and it can adapt tone again dynamically. So this component gives again, the agent its conversational intelligence and creativity that, again, is the most important thing out of this whole framework. So finally, now, we have the context and we have intelligence, and there is reason and the agent knows exactly what it's ready to output, it is time to convert this answer of this agent to speech. So this is where the DDS comes into play, and the DDS converts the generated text into a life like voice, right? So, pretty much, again, takes the text that has in its mind, if you will, from the LM, and it just translates it and transcribes it into voice. So neural TDS engines, model human rhythm, pitch, emotion, right, well producing voices that sound natural and not robotic, which is a huge, huge plus compared to previous generation of agents. So again, many systems offer multiple voices, multiple languages, and emotional styles to match brand identity or use case. And as technology evolves, you can see that the TDS software will get only better and better. The LLM will get better and better. NLU will get better and better, and of course, the ASR will get better and faster. And we're not talking about just better meaning, you know, more beautiful or better context. We're talking about faster, too, because when, you know, a human is interacting with another human being, you see that this whole processing, right, just it's so fast because it's done in the neural network of your brain, which has all of these features if you will integrate it. So now that we're trying to replicate these features, these neural features of the human brain with artificial intelligence, you will see that, of course, there's a lagging indicator between these two. But in the future, I'm pretty sure that, you know, it's going to be almost impossible to figure out if you're talking to an AI or not. So let's talk about some supportive technologies that are into place, right, to aid and facilitate the usage of voice agents. The first one is the SSML or the speech synthesis markup language. And what this SSML pre does is that it fine tunes, pauses, and emphasis, right? So this comes in the TTS part of things, right? It's just good for you to know that there's that, right? Or dialog management, for example, dialog management tools track, again, the confersation flow and context across turns. So it sees, how do you respond with this AI, sees your pauses, right? And makes the whole dialogue just feel more genuine or sentiment analysis features, right? They detect user mood and adjust the tone based on the mood. These are super cool and high tech features that we didn't have a year or two years ago. I'd say, technology knowledge bases, right, and APIs, they allow real time data lookup and task execution on real time as you're having this conversation with the AI. So together, again, all of these supportive technologies ensure that each dialogue feel feels natural and super like context aware. Now, this is how pretty much like voice agents operate. Let's now talk about how these voice agents can also learn over time. So as they're evolving, they're also learning. And again, these voice agents can improve through exposure to more conversations. The more context AI has, the better it becomes. So machine learning models analyze transcripts to reduce errors and enhance response accuracy. Those feedback loops that they're always generated by these agents always working enable adaptation to specific industries, languages, and customer patterns. And this makes like super dull sense. Because if you're using these agents, let's say, only in the industry of healthcare, these agents will have huge exposure on how to interact with patients, and patients have different ways of communicating, right, of communication, especially verbally than normal people. You know, these agents, for example, that have been trained on a huge dataset on how to interact with patients will understand emotion better than other agents. So it's super again, important to have these feedback loops into place, and you don't need to do anything like, have these feedback loops. I'm just saying that, you know, these feedback loops exist and your agents will get better and better if they're working on the same task, right? So continuous training is what reforms a generic agent into a specialized expert. And this is exactly what happens also in human beings, right? Training and spending time and energy into again, subnising into a specific set of tasks will make you better at the specific set of tasks, compared to someone who hasn't subnised, right? So let's talk about the tech stack behind the scenes. These modern agents run on cloud based architecture. This means that you don't need any hardware, right? You don't need any hardware to deliver this speed and reliability that these agents has. All of this architecture that they're leveraging is up there. It's on the Cloud, and you can use it with just your phone or okay, not a phone, probably, like, a computer and maybe actually even your phone and simple computer access. Like, that's it. All of the data that they're using is up there in the cloud. So they rely on APIs, secure datasets, and real time processing pipelines, right? So again, AJI and low latency servers allow responses in less than again, half a second, which is super fast, creating this seamless human like interaction in these conversations, right? And I want you to imagine this first image of a First computer that came out, which was a whole room filled with, you know, data and storage and whatnot, right? And now, you can access like the same groundbreaking type of technologies, right? Because again, the first computer was as groundbreaking as access to artificial intelligence that we have right now, you can access it through the cloud, right, just by having access to computer, which is absolutely awesome. So again, these security layers protect voice data through encryption and compliance protocols. So what are the key takeaways of this lesson right here? The first key takeaway is that these, again, agents operate through a chain of advanced systems. We talked about these systems. On top of these systems, there are some supportive tools like dialogue management, for example, sentiment analysis, and these APIs refine the whole experience to make it as seamless as possible. It's super important to make the experience as seamless as possible when we're talking about AI to human interaction. It's super important. So machine learning enables continuous improvement over time. These agents will be trained and will be refined because there are feedback loops that help them understand context and memorize context and become better and better every time that they engage with the same type of human being. This cloud infrastructure and real time processing make instant natural communication possible, and understanding all of these technologies help you just design better. Smarter voice interactions that will help you scale your business, like, organically with these voice agents. Now, I'm super excited that you made it this point of the scores. And in the next lesson, right, we're diving a bit deeper, and we're comparing we're analyzing the evolution of again voice AI and comparing what we had, right, again, regarding voice agents with Siri back in the day to these modern GPT powered agents, right? And you will see the true power of LLMs, right? So thank you so much for being here. I'm going to see the next lesson of the scores. 5. See How Voice AI Evolved from Siri to GPT: The prevision of this course, we focused on analyzing the back end of these AI voice agents, right, how these AI voice agents operate. And what was clearly established is that what really makes these AI agents special is the LLM, the large language model, right, that is operating in the back end again, which enables the data to be processed with these AI features and given back to us. Right with context. Now, the biggest difference between traditional voice agents like Siri, Alexa, and those types of agents with AI voice agents is this LLM right here. And in this lesson right here, we're going to be discussing about the evolution of voice AI. Where did it start from? And how did we end up in this awesome place that we are right now, right? So thanks so much for being here. Let's analyze this evolution of AI, how we went from simple commands to intelligent conversation. And here's the thing. Voice AI, of course, has dramatically, right, advanced over the last two decades, pretty much a literally day night difference. What began as command based assistant like Siri Alexa, without artificial intelligence integration in Sraplm is now capable of free flowing human like dialogue, right? So in this lesson, again, we're exploring these milestones that shaped this transformation of command based assistant to again, human like genuine dialogue. And this first command era started in 2014 with introduction of serial, let's say, and these first voice agents and ended in 2018. So again, we had Amazon Alexa, Google Assistant, Cortana. All of these assistants entered the scene, and these voice commands triggered specific actions. For example, history, play music or set a timer, right? Cloud computing these again, voice assistant, these primitive voice assistants, and by the way, it's so funny to call primitive something that was made in 2014 to 2018, right? But these first primitive, let's say voice assistant, right? Again, were based on cloud computing, and this improved speed and accuracy. But despite their popularity, assistants remained rule based and reactive, right? No conversational. And if you think about it, Siri for example, is reactive, it's not conversational, Alexa. It's reactive. It's not conversational. You cannot have a conversation with them, right? You cannot prompt them. They cannot analyze information correctly, right? They can do this on a very superficial level, but they don't have deep understanding or learning capabilities, right? And machine learning completely transform AI, right? This LM adaptation and L M processing completely transforms AI. The shift from keyword systems to machine learning models marked a turning point in this whole game, right? Neural networks, learned accents, right? Context and emotion from massive datasets, right? And these datasets become bigger every single day, right? For the first time, voice systems began to understand meaning instead of just memorizing phrases and being able to recall, like, Oh, yeah, this is a phrase that I have been coded to understand, so now I'm reacting to it, right? They can memorize, right? They can understand context. And this was, again, the foundation of today's conversational agents that we now are able to leverage. And you know, it's insane, right? So conversational AI emerged this way. 2018-2020, there were huge advances in natural language processing, right, which just brought more flexible dialogue to the scene. Systems, then, right? They could handle multi turn conversations. They can ask clarifying questions and remember short term context. Again, this was the primitive era of AI. AI was just introduced introduced in the world. Of course, the crossing pw of these LLMs wasn't that high, but still the natural language processing, again, applications were available. So industries started exploring practical use cases from call centers, let's say, to sale support, right? And then came the GBD evolution, right? And large language models started interacting with natural language processing models, right? So again, LLMs just completely changed everything. The arrival of GPD three and GPD four ushered a new era, right, of voice AI. Those large language models, as we talked about, they understand nuance. They understand emotion, they understand reasoning, right? And they can give this feedback back to us, right? They enable voice agents to generate context aware answers, not just recite some scripts that they have been coded once to recite, right? These, again, LLMs have access to huge datasets of information, and they are aware to understand changing human emotion. So voice AI became creative, adaptive, and surprisingly human. So now this led to the rise of modern voice agents. And these modern voice agents blend LLMs with real time speech and again, neural text to speech software. So now we have the processing power of LLMs of GV three, B four, and we can, again, give this information back to human beings with a text to speech software. They can listen, they can analyze intent. They can speak with emotion in literally under seconds, which is huge, and this gets faster every single day. So again, tools like Voice Gene, for example, which we will be using in this course right here, bring this power to business applications, right? We're talking about sales, calls, customer service, lead qualification. Everything is completely fully automated with these agents right here, which leverage LLMs, and TDS, and all of these different again. Pillars that we talked about in the previous lesson of this course. So this is the shift in intelligence that we talked about, right? Earlier systems waited for commands. They were coded once to understand certain commands, and they waited for these commands in order to operate. If you can recall, if you want to, let's say, engage Siri or engage Alexa, you need to say certain keywords that I'm not gonna say right now because I'm recording on a MacBook and Siri will pop up. But these new voice agents can initiate contact, right? They can ask questions and take autonomous decisions, which is huge. They combine predictive AI with CRM data to reach out proactively for follow ups, reminders, and sales outreach, right? And this is again, just the beginning of AI sales reps, for example, as a new workforce layer and all of these different adaptations of again, agents on your business. So what defines voice AI in 2025, first of all, in 2026 and 2027, first of all, ultra realistic voices generated by neural TDS engines, real time emotion detection and sentiment response, which gives this human layer, if you will, to the AI voice agent, integration with CRM and marketing automation. So you can automate more tasks rather than just data collection, right? Multilingual support and instant translation is a feature currently available that will be evolving, right, as years pass. And again, voice memory that retains context across sessions. Those are the five key characteristics of voice AI in current times, and in future times, those are what sold us, if you will, in voice agents, and that's what we're going to be leveraging in this like course right here, right, when in Next Multile we're going to be applying these agents to our business. So the key takeaways of this lesson right here is first of all, that voice AI evolved from again, basic command systems that were coded once we're given data once, right, to intelligence to intelligent two way conversations, right, that only get better and better and better. Machine learning and LLMs enabled understanding, context, and creativity, which is huge by the application of LLMs in these voice agents, the fact that we can use V three and GV four to understand context and give out better results. And today's agents, the agents that we currently have bradly available, can think, reason, and speak like genuine human beings in real time, right? So the technology is now mature enough for practical businesses and also cheap enough or again, to be distributed broadly across the whole world for small businesses to mid businesses, right to large businesses to leverage. And voice again, is one of the leading platforms bring these agents into life, and we're going to be again using in this again, course right here. So this concludes this comparative lesson right here. Now it's time to actually dive deeper in the utility of these A agents and analyze the different industries that we can leverage them. This will be the final lesson of this module, and I'm super, super happy that you made them until this point. Let's move to the next. 6. xplore Real-World Voice Agent Use Cases: In the previous installment of this course, we talked about the evolution of AI voice agents, right? How voice agents pretty much started without having in the back end the LLM playing and inputting all of this information and data processing from GBD three and GB four, they started by only being hard coded to follow commands and then they evolved into these beautiful A agents that we're going to be leveraging again in this course right here. Now, at the end of the lesson, we also talked about the utilization of these AI agents across various industries, and that's exactly what we're going to be elaborating on in this video right here, in this lesson right here. In this lesson, I'm going to show you exactly all of the different applications of AI agents on all of the different industries that you can imagine. Right? And I want you to follow along because by understanding how different industries can take advantage of these AI agents, you will just unlock new ways to think, right, around on how to utilize these agents yourself, right? So enough introduction, let's talk about the use cases of AI voice agents across different industries. So again, these agents have been transforming the way business operate, right, on simply every major sector because all of those different sectors of pretty much any business involve human interactions, right? And human interactions can be automated at some point with these voice agents. So you can pretty much like from automating sales calls to providing 247 customer support, they literally handle, again, thousands of conversations simultaneously at scale while maintaining and human like done, which is a huge competitive advantage over businesses who don't leverage these agents. So let's talk about how these agents can, first of all, work on sales and lead generation. This will be the most common use case of these agents. And, of course, sales and lead generation applies very much like in every single business out there. So voice agents can handle the full spectrum of sales outreach, and by the way, you understanding how to use these AI agents, right, AA voice agents apply them into any sales and any lead generation mechanism out there regardless of the industry is a skill that you can leverage to sell to other businesses, right? So, pretty much AA agents can handle the full spectrum of sales outreach. They can make cold calls, which means calls to people who don't know about you, right? And then to duce automatically products, which again, magnet would be done by human beings, which is not that productive. Right? They can qualify leads based on preset criteria that we can set to these agents, right, super important. They can qualify leads, so you know exactly if these leads are qualified, if they are warm, if they want to buy, or if they don't have, let's say, if they don't check the box that you want to have them qualified, they can schedule appointments and follow ups not only in pre qualified leads, but on current clients of businesses. And they can also handle objections with again, enrolled warm leads to human reps, right? So this is awesome, a new way, for example, to use these agents, not to close the sales with these agents. These agents wouldn't be here to close $10,000 sales, $20,000 sales, but they would understand, let's say objections, and then forward these people to human reps. So again, they create scalable always on sales teams that never need breaks or any shifts. These agents can also be applied in customer support and service. This is just another huge field of any business, pretty much, but you can also apply these agents. So again, in customer support, AI voice agents resolve repetitive issues without the need of human intervention. Isn't this exactly what customer support people, right? Are doing right now. They have a framework. They have an algorithm in mind. They know the exact process on how to solve problems. And usually what happens is that the exact same problems keep coming again and again and again and again. And these people happen to repeat themselves, right? So they perform, again, robotic repetitive tasks, which are exactly the tasks that we can now outsource to AI agents. Again, you can answer frequently asked questions instantly, right? Without any delay, without showing any signs, right, of misinformation, right? They can process returns or provide order updates immediately. They can gather feedback as they get service interactions and transfer again those complex cases which potentially might need genuine human interaction to human agents, right, with full context. So by having, like, a full breakdown on what this customer wants, what is, let's say, the point that this customer wants to deliver, why this AA agent cannot do this, right? They can give a full breakdown of context to human agents. So again, this dramatically reduces wait times and boosts satisfaction, again, across all industries. So again, sales and legion, customer support and service can be applied into multiple different industries, right? An example of an industry that could leverage AI agents massively, right, to again, decompress the system itself is healthcare and wellness, right? So voice AI is, again, increasingly used in healthcare for routine communication. You can imagine what happens in healthcare systems, right? All of these people, they're frustrated. They're in pain, right? They want to see their relatives or they want to get a check of themselves. And there's a huge, let's say, bottleneck. Right, by having human people try to tell everyone where to go and what to do. So you can schedule or confirm appointments, again, with A agents here. You can remind patients to take medication or attend checkups automatically with agents. You can deduct post visit follow ups and schedule those post visit follow ups with agents. You can answer general questions again, securely and consistently. And again, if you run a clinic, for example, this can help clinics reduce administrative workload while maintaining patient engagement. And I want you to focus here for a moment and just think of yourself running a clinic. Imagine being a doctor, and now suddenly, right, all of this complex system of follow ups and appointments and rescheduling and reminding patients take their medications and the checkups and everything being completely outsourced to a system that is simply flawless. How better would you be able to do your job and only your job, focus on only the skills, you know, that you want to practice, right? And you know, it would be awesome, and it's possible. Educational need learning is another space which can heavily leverage AI agents and has been heavily leveraging AI agents, especially in the new rise of E learning. Seeing education, A agents create more accessible and personalized learning environments. Personalized learning is, you know, on the rise right now. Every single e learning platform, every single e learning software out there tries to make the again, learning experience as personalized as possible. So, for example, you can onboard new students and explain course structured structures in a more effortless way using these AA agents. You can answer, again, enrollment or payment related queries, which are, you know, super like streamlined. You know exactly what you answer. You just don't have to invest your time and energy in every single one of your students. You can provide learning reminders, motivational checkups. You can support teachers with attendance or test updates. And again, they can also act as visuals assistants with academic systems, AI agents themselves, right, because the ability, again, to understand your speech and transcode it into text and analyze this text, give feedback to this text, and context to this text, and then give again, personalized answers back is a huge part of the teaching experience itself, right? Real estate, property management, pretty much like there's not a single field or niche out there in business where humans right now are not performing robotic repetitive tasks, right? And all of these robotic retive tasks can be outsourced to age. And so in real estate, for example, these agents automate communication between clients and property management. They can call prospects about new listings immediately. Imagine this, right? Imagine being a real estate agent, and now every single time that there's a new listing, you can call all of these people. They collect availability or schedule viewings. You just need to attend the viewing yourself, right? Everything else in the back end is scheduled by these agents. They can send reminders for or maintenance appointments that can follow up with interested buyers automatically. And again, this just makes the whole thing smoother, right? Hospitality, traveling, hotels and travel agencies they use AI agents to handle large volumes of bookings and guest interactions. And now, with the rise of AI agents, as they become better and better and better, this will just start you know, to be more automated, right, and scaled so the A agents can confirm reservation and process cancellations, right? Without making a single mistake, they can suggest upgrades, right, or promotions dynamically based on exactly what each customer needs. They provide 247 support in multiple languages, right, which is a huge plus, especially in the hospitality sector and the travel sector, where you have to deal with all of these people that speak all of these different languages. You don't need to have, like, expensive personnel who just because, like, these people know like four languages, they cost three times more than an average employee, right now, this can be done by AI. And guess what? Like, if a problem occurs and if there's a problem that, you know, needs, genuine creativity and genuine thought, these AI agents can forward it to genuine people who can solve this problem. So they're not here to replace, right? They're here to aid, right? They can collect post, stay reviews and feedback and automatically post them, for example. Again, this just creates a seamless experience where hotels can do what they do best, which is accommodate people rather than trying to connect the dots and build a whole system from the beginning, banking and finance is another field which is hugely, hugely embitted by AI agents. You know, these agents can balance inquiries and transaction confirmations. And this is already done in Fintech, right? So whenever you place, let's say, a statement in your bank or you want to purchase stocks, right? This is already processed by tech in the back end, but this can also now be done by A agents, fraud alerts, right? Identity verification, right? Of course. This can also be done by A agents, right? Loan eligibility and application updates can also be filtered and analyzed by agents or customer service feedback collection. Again, this voice automation process improves response time and maintains compliance standards even in banking. Now, of course, if we're talking about, you know, security issues, and accessing your bank accounts, you wouldn't have an AA agent, you know, help you with that. But the back end of FinTech is heavily, heavily inspired and aided by the usage of A agents. Of course, like ecommerce and retail, we don't even need, I think, to elaborate on these points on how much agents can help in ecommerce, which is already something that needs to be scaled to be profitable in retail itself. So again, again, ecommerce platforms use A agents to manage customer journey. At scale. This is something that is done at scale, especially in all of these new ecommerce stores that pop up every single day, every single week. You know, ecommerce, people who work in ecommerce want to test multiple websites, and they want to scale with these websites, and agents are extremely useful in this case. So they can confirm orders, shipping details, they can recommend, right? Complimentary products, depending, again, based exactly on the persona and the avatar of the buyer. They can handle refund or exchange requests in bulk and they collect post purchase reviews, which they can also post. I'm going to show you how. And the next mousle of this course, right? So again, AI driven voice interaction increases sales and strengthens customer loyalty, right? So in this session, again, we pretty much talked about the proven effective, of AI agents across all of these different industries, how effective these agents are in sales, support, healthcare, education, real estate, finance, right? And these were just some industries that I mentioned for the sake of this right here just to broaden your horizons, right then open your minds on what these agents can do. So again, they automate repetitive interactions. Personalized communications and literally operate 247, right? 258. So by interacting, right, integrating with existing systems, these agents enhance both customer experience and business efficiency, and they just pretty much setting the new bar, they create a new standard in the modern era. Now that we're done with analyzing the different applications of these agents across different industries, I think that you're starting to have a circular understanding of the real power of these agents. And if I were you, I would be super eager to start checking out the software, which we're going to be using to leverage these AI agents, which is super cool and very cheap, considering what you're getting, right? Before we do that, let's move with the final lesson of this Multi right here, which are ethical and privacy considerations when using voice AI and A agents. It's super important for you to understand this, so you don't stumble across any roadblocks or any bottlenecks in the future when you're doing this at scale, right? So this will be the final lesson of Module one. Thank you so much for being here. I'm going to see you there. 7. Protect Privacy and Stay Ethical with AI Voice: Come to the final lesson of the first module of this course around AI agents. Now, this first introductory module couldn't be concluded after a short lesson around the ethical and privacy consideration of AI agents, right? These AI agents will be communicate with real genuine human beings. And whenever we're interfering with real genuine human emotion, connection, and outsourcing of this human interaction to AI agents, we definitely need to be aware both ethical and privacy considerations, especially if you are adapting these agents to your company, right, to your business. So let's close this chapter right here with this small note, and then we can move into the cooler again modules which are going to be more practical and more tactical, right? So again, as voice agents become more human like, Questions around ethics, transparency and data protection grow increasingly important, right? This is a problem that has risen due to the fact that these agents are more human like every single week that AI evolves, right, that large language models become better and better at data processing and understanding context, right? So the SssmaR here explores how to use voice technology reasonably and maintain user trust. So let's talk about the importance of ethics in AI. Of course, we'll know that ethics define the boundaries of responsible innovation, right? AI is uh, you know, partnered with huge innovation. That being said, you want this innovation to be responsible. So AI voice systems influence real people, right? And often in also sensitive context, you know, they can influence buying decisions and buying decisions, spending money can really have an impact on a person's, you know, life. So designing ethically ensures fairness, accountability, and respect for human autonomy. Unethical implementation can damage brand reputation and also genuinely create some legal risks, apart from, of course, ethical risks. It's not only unethical to, you know, apply and design these AI agents in a harmful way, but it can also lead to legal trouble, right? So let's talk about data privacy, right, and consent. Every single AI voice agent processes personal information through speech. This is well established. I'm until this point of this course. Comply with privacy laws and not get into any legal trouble and maintain trust, you should always disclose that users are speaking with an AI system. This is super important, right? You don't want to risk with this. You need to obtain explicit consent for call recording or data storage. You might recall, whenever you're talking to a voice agent doesn't need to be an AI voice agent, right? You need to obtain explicit consent if you're recording, again, information or if you're storing data, you should follow GDPR or local data protection regulations, and of course, allow users to request data deletion at any time. Yes, we're processing information. Yes, we're gathering information. This information is super valuable to our businesses, but we need to be upfront with this, right? This needs to be a transparent process in order for you to practice these awesome systems, right, without having any fears and any legal implications. So regarding transparency and disclosure, again, it's super, super important if you're taking one thing from this session right here is that you need to be transparent with this, right? So people deserve to know when they're talking to a machine, right? That's just basic human rights. Because in the future, when everything is AI and, you know, we have AI agents doing everything like, it's super important to know that you're talking to human being or a machine and for people to also adapt accordingly. So transparency builds credibility, and it also prevents manipulation. So AI agents need to clearly identify themselves at the start of an interaction and state the purpose of the conversation. Now, you might be thinking that this might be a turnoff right now for someone, you know, to be like, Oh, my God, I'm talking to an A agent. But I can guarantee you that in one year, two years, five years, I don't know the rate of, again, innovation in this AI space. People will want to talk to AI agents. Right? It will be a turn of in some years to talk to a normal human being just because these A agents will become better and better and better and flawless, right? So again, hidden information erodes public confidence in AI technology, and you don't want to be that guy, right? So let's talk about data security and storage now. Voice data must be protected like any other sensitive information, right? This is done I'm going to show you how in the next module, but you should be using encryption during, again, transmission and storage of voice data. It really depends on the voice agent software that you're using. You should store recordings on secure compliant servers, limit internal access, and anonymize where possible, right? So you don't have data leaks, and you should also implement regular audits and penetration tests. Finally, responsible storage safeguards both users and companies for breaches. You do not want to have data breach because this is something that will harm your business in the long run and might also cause legal implications. So it's super, super important for you to use an AI agent software service, right, that checks all of these boxes. Regarding bias and furnace, right, AI systems can unintentionally reflect bias, right? It really depends on how you have trained these AI agents. And this can lead to again, unfair or discriminatory outcomes. So it's super important for you to make sure as a developer to train your AI agents correctly. You should diversify their training datasets. You should test responses across, again accents, genders, and languages, and you should continuously monitor these agents for biased outputs, right? This is a continuous thing, and guess what, like, once you implement you know, the development of these agents, once you have developed these agents and you have positioned them correctly across your business, this is a relatively automated process. Like, you won't need to be doing so much work as these agents are working. The only thing that you should be doing is, again, testing, monitoring, changing. So again, fair AI systems respect all users equally. So now let's talk about human oversight, and what is your involvement into this? Because if you think that you're going to be just placing these agents and going for vacation forever in Maldives and never coming back, you're mistaken. You need to have human oversight here. So AI systems should always remain under human supervision, right? Because at the end of the day, you also don't want to be replaced by AI, right? And again, genuine human creativity, genuine human emotion will never be replaced, but it's good to supervise these agents. So human review ensures accountability for errors and edge cases. Organizations should maintain protocols for escalation. Quality checks and manual intervention whenever this is necessary. And again, remember that these agents, these are tools. They're here to make your life easier not to replace your life again. Humans remain responsible for the behavior of their AI agents, if this makes sense. Talk about regulatory compliance now. AI voice technology is governed by emergence emerging reulations worldwide. It's not a free for all anymore, like it was like one year ago or two years ago. Regulations are emerging every single week, if not every single day, right, regarding artificial intelligence, because this is a box that has opened, right? AIAPIs are now available worldwide, so they need to regulate them somehow. For example, in Europe, we've got the GPR. It protects personal data and consent. So you need, again, to follow DDBR rules. In the EU, we have the AA Act, which classifies and regulates AI risk levels. And you can check out more information about all of these rules and regulations depending on your jurisdiction. In the US, there's CCPA, which grants rights over personal information, right? And we got also the ISO and IEC standards. These defined global AI governance frameworks. And you need to have a look at these, right, or at least download the PDF and fit it into an AI and ask the AA to summarize this. But you need to keep up with compliance, right? This will ensure the long term sustainability of your business and of these agents working on your business. What I suggest is downloading the PDFs of all of these guides, fitting them into an AI, asking for a summary, making sure that your business, again, applies to all of these regulations. You do not want to, you know, mess this part up. It's important, right? So let's summarize what happened in this lesson. Ethical design builds trust, right, and protects both users and organizations from stuff that we do not want when we're in business, right? You need to be transparent about AI identity and data usage. You should prioritize consent, privacy, and strong security practices, address bias, maintain human oversight, and follow regulation. Again, responsible AI development is your foundation if you want this, you know, whole AI agent thing to work on your business. You need to be responsible. This was the end of the first module of the scores, right? I'm super happy to have you up until this point. I think this was a very educational first module. And in the second module, we are going to start by analyzing the business application of voice agents, and gradually, I'm going to be introducing you to the software that we're going to be utilizing itself and starting to work on our agents together, right? I'm very excited to have you here, seeing the next module of the scores. 8. Learn Why Smart Businesses Use Voice Agents: Ladies and gentlemen, congratulations on making it in the second module of the scores. Now, for the next four lessons, what we're going to be discussing about is, first of all, why modern businesses are adopting AI voice agents and how these AI voice agents can be actually applied to your business. And then we're going to be breaking down each step of the funnel, each positioning of the funnel that we can actually put these agents into place. We're going to be talking about top of the funnel, middle of the funnel, and lower again on the funnel. Now, again, if you don't know what a funnel is, right, or have no idea again how these agents can be applied to your funnels and to your businesses, do not worry about this because we're going to be tackling again everything in this module right here. So again, this module is mostly centered around the application of these agents to your business and how they can just smooth the whole process of someone learning about your business until he actually becomes a buyer. So at this first lesson right here, we're going to be discussing why businesses are adopting AI agents. Now, again, organizations across every single industry are embracing artificial intelligence and more specifically, AI agents to modernize communication. That's pretty much what AI agents have been doing and can do, and we have established this from the previous module of the scores. They can save time by again communicating with multiple people at the same time. They lower costs because they are AI agents. They're not actual people, you know, who need to be paid. Way more than them and they can create consistent, high quality customer interactions at scale. So in this has I've been exploring the main reasons behind this rapid adoption of AI agents in modern businesses. Now, the first reason why these agents have been so organically adopted by modern businesses is this growing demand for automation, right, that characterizes the current market. Today's businesses handle thousands of, again, repetitive conversations daily, right? And if you have taken something home from this course is that whenever we have something that's repetitive and robotic, it needs to be outsourced and automated and can be automated and outsourced with Autoficial intelligence, right? So again, this stems anywhere from sales calls to support requests to scheduling and updates. And these AI voice agents automate these tasks again instantly and accurately. So this automation pretty much enables businesses to grow at scale, right? Growing at scale by automating robotic repetitive tasks. So we can only focus on those human elements such as creativity, strategy, and human level decision making, right? The second reason why these agents have been rapidly absorbed in today's market is the fact that they're available 247, right? They never sleep, they do not eat, they operate continuously across time zones, right? They don't need days off, and they're also super cheap. So this constant again, accessibility increases the engagement of AI agents, improves satisfaction and generates more opportunities without you needing to hire additional staff. They're 247 again, available for you to deploy to your business. Right? On top of that, due to the fact that, again, they're always available. And they're relatively cheap, these AI agents can help your business scale and can help you exponentially increase your efficiency, the efficiency of your tasks. So again, these AI systems scale effortlessly. A single platform can manage hundreds of calls simultaneously, right, with consistent tone and zero waiting time. So you can eliminate all bol necks that were centered around this human limiting factor, right? No misleads. And again, you can ensure that every inquiry receives instant attention from your agents. On top of that, there is the huge point of cost reduction. Voice automation significantly lowers operational costs. So we have fewer manual calls, right in shorter queues, reduced training and turnover expenses, a paper use pricing instead of full time salaries, right? Which can deviate from what you have calculated as a business, where when we have paper use pricing, you know exactly what you're paying and what you're getting, and then businesses achieve higher productivity at a fraction of the traditional cost while maintaining, again, service quality. Now, let's talk also about consistency and accuracy of these agents. Again, the human tone can never, like, completely be replaced. Let's be honest, you're talking to a human being and seeing a human being or at least talking in a human being and understanding the tone and the tonality and the emotion behind of his voice will never be 100% replaced. But these agents are really damn close to that, right? Again, these voice agents deliver uniform communication that is precise, polite, and always on brand. And these agents, they are not here to fool people, right? You don't want to fool people thinking that they're talking to a human being, and then you just have an agent there, right? As we talked in the last lessons of the previous module, these agents need to be disclosed whenever they're used, right? So what they do is that they eliminate human error. They follow conversation guidelines flawlessly and ensure that every customer receives the same high standard, again, experience. They pretty much offer consistent perfection across communication, right? Now, each AI handled call of all of the calls that these agents perform is recorded, transcribed, and summarized. So managers can gain access to just view all of the data on call outcomes, sentiment, and conversion rates. All of the data that will be extracted out of these agents can be measured, right? And when you measure something, this is how you can improve it. So these insights help refine marketing strategies, optimize scripts, and again, identify performance strengths that drive measurable return on investment. You pretty much can measure exactly what happened in a call and take action according to these results, something that would be very, very hard when you had real human agents, right? And on top of that, this offers the ability to personalize those messages at scale, modern agents personalize, again, conversations using customer data for CRMs that we can import, right, or CRMs that it extracted from previous interactions. So these agents, right, the key element of AI and these agents having LLMs working in the end is that they understand context and they can learn from other experiences. So the more these agents work for you, the more they understand how to perfect their outcome. Right? So again, they can greet by name, reference past orders, and adapt tone to context. It really depends on the context that you will be giving them. So, again, this individualized communication builds stronger relationships and improves engagement without all of this extra manual effort that a human being, right, would need. Of course, a huge reason why businesses adopt A agents and why I believe in like 25 years, all businesses that reliant communication will be relying on these agents is the fact that it just provides a huge competitive advantage over other businesses. Again, early adopters of voice automation are gaining a clear market edge. As these agents respond faster, they serve more customers on bulk at scale, right, and collect richer data than competitors, still, again, relying solely on human labor, right? So you cannot just rely on human labor if you want to scale communication. And as AI becomes standard practice, businesses that ignore it risk falling behind in both efficiency and customer expectations. So what did we talk about in thisineson Ratios was an introductory lesson on the business applications of AI agents. And again, these agents are here to automate repetitive communication and scale outreach. Apart from just, again, automating repetitive communication, we talked about their 247 availability, the fact that they can reduce cost right? They're consistent throughout their communication, right? You can measure analytics and the key performance indicators of every single call as these agents record data and can send data back to databases where, again, manual creative people who are here to strategize, can analyze this data and act, right? Upon the outcome, and they can also be super personalized based on the information you have regarding any customer that they communicate with. So again, AI voice agents enhance productivity. They improve customer experience and deliver measurable return on investment. This leads to smarter operations and faster growth powered by this conversational artificial intelligence, right? Now that we've had this small introductory lesson on the business application, AI voice agents, it is time to start analyzing each funnel that they can apply, and we're going to start by the top of the funnel. This means, right, that in the next lesson of this module, we're going to be talking about how these agents can be applied for lead generation and outreach, right? I'm super excited to how you here. Let's actually dive into the analytics of a business and how AI voice agents can be applied there. See you in the next lesson. 9. Generate Leads Automatically with Voice AI: Now finally time to talk about the top of the funnel applications of AI agents, and top of the funnel applications pretty much include lead generation and cold outreach, right? Client acquisition pathways. How do we acquire leads, right? And how do we turn people who do not know us and aren't aware of us suddenly, you know, be aware of us, right? How do we raise awareness about our products, our services, or whatever we're selling in our businesses. AI agents can actually be used in the top of our funnels, right, as awareness tools. And that's exactly what we are analyzing in this lesson right here. So let's talk about, again, LeGen and Outreach using these AI agents. So again, at the top of every sales funnel lies awareness and first contact. Pretty much the first time that our potential customers are exposed to our offers, to our products, to our services. And AI voice agents are redefining how businesses approach this first stage, the top of the funnel, right? Turning what used to be a slow manual outreach process into a scalable, automated data driven one, right? We talked about the scaling effect of AI agents and how everything is automated, right? And that's exactly what's going to be happening. In the top of our funnel. So what is the goal of the top funnel stage? Again, the top of the funnel focuses on attracting and initiating contact with our potential customers. These customers usually they do not know us, right? They aren't aware of our offers, and that's again, exactly what we're trying to do at this stage. So here, the objective isn't to close a sale or to collect cash. It's to identify interest, qualify prospects, and open up a conversation with them. And AI voice agents help teams reach thousands of potential leads instantly and filter qualified ones, right? So how do these agents transform outreach? As we know, top of the funnel and raising awareness usually relies on what we call cold calling, right? So reaching out to an audience that is cold who doesn't know us. And traditional cold calls are super time consuming and super inconsistent, right? AI voice agents automate this whole cold outreach process by calling prospects from a lead list automatically. So you can import a lead list. You can give data to these agents, and they will actually go ahead and call these prospects automatically. They can deliver a consistent and friendly opening pitch, which can also be personalized on exactly the tonality, the messaging, right, and the context that we have already give these AI agents, they can handle objections or questions super intelligently, like they're having like a genuine conversation with the leads. Again, on a live basis, they can schedule callbacks or follow up calls for, again, qualified leads so they can qualified leads on real time, let's say that they call a complete stranger, and by this conversation that they're having, they understand that, right, this guy is interested in the services, right? So they can actually qualify him and put him in a list of more qualified again leads. So this creates predictable, scalable outreach without increasing headcount, right? Of course, speed and volume are super important when we're called calling people when reaching out to multiple different people, right? AI systems can conduct hundreds of conversations simultaneously. This means that outreach can be used which used to take a week can now be done in a single afternoon. All we need is the data of people to target, right? If we have all of this data of people to target, these agents can do this automatically. So speed increases the chances of connecting with decision makers before competitors do what's super important, right? There's critical advantage in those modern sales pipelines, speed and volume, two key characteristics when we're targeting top of the funnel, people who we're talking about, again, a huge number of heads rather than, again, qualified leads or people who are ready to purchase, which is a way lower percentage of, you know, from the people that we're referring to at the top of the funnel. Now, lead qualification is, of course, super important because we cannot start investing our creative time and our creative energy talking to leads who aren't qualified. And that's actually something that these voice agents can also help us with, right? Lead qualifications. They don't just call, right? They don't just call people. They actually listen to what people have to say, and they can actually detect buying intent through keywords, tone, and responses. And whatever is detected in the conversations that these AI voice agents have, it's measured and stored in databases. And we have access to these databases. So qualified leads are tagged automatically and sent to human reps for follow up. So this is where the genuine human creativity can play a role, right? And we can tap in this genuine human creativity when the leads are qualified. And this qualification process of leads can be done by agents. So again, this removes guesswork and ensures sales teams spend time only on prospects who are genuinely interested. And on top of that, we can not only qualify leads based on interest with these agents, but we can also qualify leads based on how actually well our services will apply to these people. So not only can we just bring more castle to our business, but we can also bring better potential clients who can serve much, much greater, right? These personalized conversations, of course, are also a key characteristic of key characteristic of agents because, again, these agents use CRM data to personalize each and every single one of these calls that they conduct. And by the way, they can conduct multiple calls at the same time. So they can mention the recipient's name, the company or their past interactions that they had with these agents or with even a normal human being if we also have the data. This transforms cold Atwach into warm relevant conversation, increasing trust and conversion rates right from the first contact that they had with these people, super, super important and a just completely game changing advantage that AI voice agents have over just, you know, some random person who hasn't been trained and on every single person that he calls, right? We're talking about human beings. So voice agents also interact seamlessly with tools like HubSpot, Salesforce, Pipe drive, and each call result outcome, again, interest level and notes is logged automatically. So we have automatic, again, data processing and data collection, which enriches the CRM in the first place, providing teams with a full picture of pipeline health and next steps. Regarding compliance and tone, again, the automated outreach can remain ethical and compliant, and AI agents are programmed to follow consent rules, respect opt outs, and maintain polite, professional tone. This is a, again, key baseline when we're using these agents, the fact that they can consistently maintain great tonality and a great approach when talking to human beings. So again, consistency in tone protects brand reputation while ensuring every interaction feels authentic and respectful. So this consistency in tone protects brand reputation while ensuring every interaction feels authentic and replaceable. Now, where are the real world results of actually applying these agents in the top of your funnel, right? Businesses who actually use AI driven voice outreach can report three to five times more initial contact rates, more than 60% reduction in cost per lead, which is very substantial. High response rates due to quick, consistent follow ups because imagine, like, you get an email and immediately you can bring this person to communicate with an AI voice agent, better data accuracy for future campaigns. And again, AI voice agents turn outreach from guesswork into a measurable optimized process. These are the real world results that you can expect from these AI voice agents. So to summarize what happened in this son right here, the top of the funnel is all about awareness and first contact. That's just an awesome summary of what happens in the top of the funnel. And you can imagine as we analyzed in disson right here, how agents apply to the top of the funnel. So they automate outreach, they qualify leads and scale conversation. So we're talking about personalization, speed, and data integration that make them far superior to traditional cold calling. They create always on fully compliant legion engine that continuously fills the sales pipeline with more and more qualified leads, and that's exactly what we're going to be analyzing in the next lesson. In the next lesson, we are discussing the middle of the funnel, right? So qualification and appointment booking. How can we leverage AI agents to qualify people furthermore, and potentially be book appointments to close on sales calls, right? More information about this in the next lesson of this course. 10. Automate Qualification and Booking Calls: We talked about the general business applications of AI, and we also analyzed the top of the funnel applications of AI voice agents. It is now time to move to the middle of the funnel, right? So we talked about cold calling and cold approaching and raising awareness through these systems with AI agents. Now it is time to talk about qualification and appointment booking, right? How can we scale qualification and appointment booking through the utilization of AI voice agents? That's exactly what we're talking about in this lesson right here. So again, once initial contact is made, the next step here in the funnel is qualification and scheduling. So further qualify the leads, and you potentially schedule a sales call with them. And artificial intelligence voice agents play a key role here as they can filter serious prospects and nurture relationships and book these appointments completely automatically, right? Now, let's talk about the purpose of the middle of the funnel. The middle funnel focuses on moving interested leads closer to the purchase. So you can imagine a funnel going like this the top of the funnel, of course, involves more people as it has a way bigger diameter, but these people aren't qualified. So as we move further down the funnel, people, right, become less and less and less, but they're more qualified, right? So the middle of the funnel is where again trust is built. Objections are handled. And the value of the product or service is reinforced, and AI voice agents act as intelligent intermediates bridging this gap between curiosity and commitment in the middle of the funnel. So let's now understand lead qualification itself, right? Qualification determines whether a lead is ready to buy or not. And, of course, there is level, right, there are levels in lead qualification. It's a completely different thing to talk about lead awareness and lead qualification. But AI agents can move, again, the need from the awareness stage to having leads actually being qualified, so they can evaluate leads by asking structured questions like, are you currently using a similar service? What's your timeline for implementation or who's a decision maker on your team, right? Their ability to adapt to responses identify genuine opportunity quickly and accurately, right, without having them lose interest or move away to the fact that these agents can be applied automatically at scale immediately, right? And of course, they also offer dynamic conversations to ensure that these leads will be qualified. So unlike chat box, who have been prompted once, AI voice agents can hold multi turn conversations completely naturally. They just based on tone, hesitation or keywords allowing fly realistic discussions to take place, which of course are also recorded, right, and transcribed. So this conversational flow mimics human interaction and helps extract valuable insights without again, sounding too robotic, which is something that we don't want, right? On top of that, they can automatically book appointment, which is huge. Because imagine back in the day when a normal human being, a normal agent wanted to book an appointment, he would call the potential lead, qualify him himself, and then try to persuade him to have an appointment set, right? But now Again, one of the most powerful middle funnel functions is this automated scheduling that these agents provide. These agents can analyze your available time slots on real time, they can sync with calendars like Google Outlook or Notion or whatever other calendar you have. You just input this information in the API of these agents. I'm going to show you how to do it later on in this course. Agents can confirm appointments pretty instantly and send reminders or reschedule any appointments when needed. And again, these tasks can be handled in bulk. Right? So again, this streamlines work flows and eliminates scheduling back and forth that is something that would happen if you had a normal human agent do this role. So you can imagine at these admin tasks how easy it is to leverage the power of an A agent, right? Of course, you can integrate CRMs and calendars to give more data to these agents. So all conversation outcomes are automatically logged into CRM systems, right? So lead status updates, qualification scores, appointment details. These appear instantly on sales dashboard. So this ensures a seamless collaboration between the agent and the human sales reps, right? Because at the bottom of the funnel, we will be using human sales reps. You can see you will see about this in the next asson of the scores. But for now, we just want to have this beautiful assist between agents and normal human beings that will be taking the funnel after the middle of the funnel part from the AI voice agents. So now let's talk about handling objections and frequently asked questions, which is something. It's a natural part, let's say, of the lead qualification system in the middle of the funnel. So Airboye agents can be trained with data from past calls to recognize common objections that may be into place. They can respond with tailored answers. They can redirect to benefits or escalate to a human rep when needed, because again, we're talking about these agents not completely again taking the job out of, like, genuine people who can not only communicate, but solve problems with their creativity, they will be able, though, to respond to answers and redirect people to normal human reps. This keeps again conversations productive and ensures no lead is lost due to hesitation or confusion. And finally, nurturing warm leads. So at this stage of the funnel, the middle of the funnel, we have leads that have turned from cold to warmer. So again, warm leads doesn't mean that people are ready to buy immediately, right? This might be this might mean this might be an indication that they're ready to move to a sales call. So again, a voice agents can schedule follow up calls, they can share additional information or check back after a set time, right? This ongoing engagement keeps leads warm and nurtured until they're ready to convert pretty automatically, which is exactly what we're looking for right now. So what is the business impact of the middle of the final applications of AI agents in this case? Companies that are leveraging AI in the middle funnel report 50 to 70% faster qualification time. They report 30% higher appointment show up rates, improved CRM accuracy through automated updates, consistent follow up sequences without human oversight. And again, this stage directly increases sales efficiency and conversion potential. This is what you can expect if you apply these agents in the middle of your funnel. So what should you gain from this lesson read here? Again, the middle of the funnel is where interest turns into intent. We talked about the top of the funnel when, again, we just raise awareness and people move from being aware to being interested. Now we converted interest to intent, or at least I described you how agents can do that, right? They do that by qualifying leads by handling objections and by booking appointments automatically by redirecting people, right, to genuine human beings that will close the sales calls automatically. They can also integrate with CRMs, which ensures this smooth collaboration between artificial intelligence and sales teams, and by automating this stage by automating the middle of the funnel, businesses can now save time, reduce friction and maintain constant engagement with warm prospects, right? So now that we're done with analyzing the middle of the funnel, it is time to talk about bottom funnel, right, which pretty much means retaining the customers after the sales call has been conducted after you've closed the sale and follow ups. And that's what's going to be happening in the final lesson of the second multiple of the scores. So I'm super happy to have you here, seeing the next lesson. 11. Boost Retention and Follow-Ups with AI Agents: Bottom of the funnel is where conversion happens and a lead is converted into a buyer, but it's also where retention actually is introduced into businesses because you want to retain clients for as much as you want, right? And as you can, you want to increase the lifetime value of every single one of your clients. And this is something that actually AI agents can really help you with, right? In the bottom of the funnel in this lesson right here, we're going to be discussing about customer retention and follow ups that will increase the lifetime value of every single one of your clients with the utilization of AI agents. So again, conversion happens and retention begins at the bottom of the funnel. AI voice agents play a crucial role here by ensuring no it goes cold, every customer feels valued and relationships continue after the sale to increase the lifetime value of every single customer. The goal, again, of the bottom funnel is to nurture existing customers and maximize their lifetime value. So retention, renewals and re engagement are the key priorities if you want to create this evergreen system of not only inquiring clients, but also retaining them. AI voice agents automate all of these processes, so they automate retention, they automate renewals, and they automate re engagement with timely, personalized communication that keeps customers connected to your brand. And again, we do this all of these, right, points here, retention renewals, re engagement, we do this with communication. So let's talk about the post purchase engagement. Once a sale is made, right, and your client has now exited your pipeline, right, this late has exited your pipeline because it is now a client. Consistent follow up is super, super essential if you want to retain this client. And voice agents can deliver consistent follow ups as follow ups are just part of communication. So they can call customers to confirm delivery or completion. They can offer on boarding assistance or next step instructions, they can ask for feedback or reviews or identify to cross sell, for example, any, again, other offers you have or to upsell opportunities. And these just simple check ins can really strengthen your trust and loyalty, right you have between yourself, your brand, and your customers, and, of course, increase their lifetime value. Those are just some tasks that these agents can really offer, right? So let's talk about automating renewals. Again, renewal reminders are a great way to just have more and more of your customers renew their contracts with you, sir, it's easy, again, it's easy to overlook them if you do them manually if you tackle your renewals manually. And AI voice agents can automatically notify customers of upcoming expirations, for example, they can present renewal or upgrade options to them, right, specifically tailored to their again, services, right? They can present, again, renewal or upgrade options. Specifically tailored to their taste, they can answer any related questions that they have that might keep them from renewing. Again, transfer interested users directly to a live agent for payment if they still want to talk with a live agent before processing the payment, which is completely normal. So this proactive outreach, this again, lies under the umbrella of retention rather than conversion, but this proactive outreach prevents churn and boosts recurring revenue. So let's talk about upselling and cross selling people to other offers of yours. After the initial purchase, AI voice agents can also recommend complimentary products or higher tier services to again increase the lifetime value of your clients by not only retaining them, but also upselling them to other offers. They identified needs based on various purchases and usage data, which is something that these agents can really do, right? They analyze data that you give them. And by timing these calls strategically, they increase customer value without feeling again, intrusive or sales driven, right? Customer feedback and service are awesome an awesome way to feed these voice agents with information, right? Because again, they are very, very effective for collecting structured feedback for your clients. They can also conduct short satisfaction surveys, right, or ask open edited question if you want to find, let's say, a frictionless way to extract data from your clients, right, to see what worked, what didn't work, how your business performs. So responses are transcribed and analyzed automatically giving businesses real time insights into customer experience and improvement areas, right? They can also handle complaints, support escalation to reduce churn. So when issues arise and it's completely normal for any business to have issues, right, these voice agents act as the first line of contact, immediate line of contact that they can immediately, I can initiate contact with your clients to make sure that these issues are going to be resolved. So they can acknowledge complaints, provide quick troubleshooting or escalate urgent matters to a human agent. And you always need to have a human agent backbone, again, of your business when you're running these AI agents. You do not want only to have AI tackle these things. So this ensures every problem is addressed promptly improving brand reputation and retention, right? So now let's talk about personalized re engagement campaigns. AI systems can segment customers by behavior or purchase history and deliver tailored follow ups. For example, right, they can categorize customers based on if they bought, if they added to the card, right, or if they bought two things or three things, right, and create tailored follow ups for them. It's been a while since your last purpose, for example, or a new version of the B product is now available. So these are custom products based on different buyer personas that have been created and have been established based on all the data that you have extracted and analyzed, or would love your feedback. On your recent experience, this type of automation, again, keeps communication alive long after, again, the first sale, which increases your chances of retaining customers, right? What are the business benefits of AI driven retention, right? Companies that actually use AI for customer retention report a 40% higher repeat purchase rates, right? So again, 40% increased potential clients buying, again, lower churn and support costs, definitely, as these agents are super very much cheaper than having a normal again, human being communicating with clients and resolving their problems, stronger long term relationships between business and clients, higher average customer lifetime value. And again, retention powered by automation is one of the most profitable uses of AI voice technology. This covers the whole again, funnel from the top of the funnel to the middle of the funnel. To the bottom of the funnel, right? This lesson right here. So what we did in this lesson is that we pretty much focused again on maintaining and growing customer relationships with AI. We closed the funnel by conversion. And again, conversion, we didn't have a lesson on conversion because usually conversion, converting a lead to a client, right? So processing this payment usually involves human interaction. But after conversion, we also analyzed retention. So again, these AI voice agents can handle renewals, up sales, service and support follow ups automatically. So this proactive communication can build loyalty and reduce churn which will lead to an increased lifetime value of a customer and increased engagement and retention. So again, smart automation at this stage, transforms one time buyers into long term advocates. And this, ladies and gentlemen, concludes this whole chapter that we opened on how these agents can be applied to your business from top of the funnel to middle of the funnel to bottom of the funnel. We talked about, again, raising awareness about our products and our services in cold outreach with these EA agents, then we moved into, again, qualifying them and making sure that these people are interested in our offers and they're ready to move down in our funnel, deeper in the rabbit hole, if you will. And finally, after these clients, these leads who are actually converted into clients, we're talking about retention and increasing their lifetime value with AI voice agents in this lesson right here. So this right here was the final conclusive lesson of the second module of the course in which, again, we discussed about the business application of AI agents in your funnels, and now it is time to actually start our AI agent engine, if you will, and actually engage with these agents. I'm going to show you an overview of the software. We're going to be using how to create these agents, how to deploy them. And I think it's going to be super cool and super, super educational and super practical, too, right? Enough of a theory. So I'm going to see you in the next son of the scores. 12. Get Started Inside VoiceGenie: Welcome to the lesson in which we're going to be actually analyzing the interface of voice Gini. And I'm going to be elaborating on every single category of the software, every single, let's say, thing that you can tweak in order to maximize your reach with your AI agents. Now, of course, this lesson right here will set foundation for the next lessons to come, during which we're going to be creating our campaigns, tweaking our voice agents, and actually creating inbound and outbound campaigns with our AI voice agents. So again, this is going to be a general overview of voice Gini which is the software of choice regarding deployment of AI voice agents. So I'm super, super happy to have you here. Let's launch Voice Gini. Let me show you exactly how to navigate in the software. So this is what you see once you join Voice Gini, right inside of Voice Gini. And as you can see, in the left side of our screen, we have all of these different buttons. So we can choose assistant. We can access our knowledge base. We can tweak our operational hours, connect our phones, connect our contacts, launch campaigns, again, either inbound or outbound, monitor our calls, integrate different softwares, ABIs, and choose the members of our organization. So let me show you in general, how to navigate through all of these different again, categories, the built category, the deploy category, the monitor category, and the system category. Everything is super self explanatory. And if you join Voice Gene, you can navigate through this by yourself. But for the sake of this demonstration right here, I'm just going to show you, let's say, superficially what every single one of these categories and buttons do. So let's start with the build category. In the build category, you can see that you can access all of those different assistant either default assistants that have already been pre prompted, right, by Voice Genie, or you can go ahead and create a new assistant, which we will be doing in later lessons again, of this course. Now, the default assistant, as you can see, for the sake of this demonstration, again, it's a lead qualification agent, which, as you can see, identifies leads, understand challenges, connects and connects with suitable sales reps, right? So this is what this lead has been prompted. And as you can see, below the lead itself, you can see which LLM is running right behind the agent. And in this case, it is GBD 4.1 mini. And, of course, the language that this agent again, speaks. And again, in later lessons, I'm going to show you exactly how these agents, operate and how you can communicate with these agents. We get the customer service agent, which acts as a customer support and again, solves customer queries or the service and feedback collection agent. And again, these are default assistant that if you want, of course, you can use with your campaigns, but we can also create our new assistant here, which is going to be a way more in depth process. I'm going to show you how to do this again in next lessons. Now, the knowledge base is where pretty much you can import and create new knowledge base to train your assistants. So pretty much, you can manage again, your knowledge bases for your AI assistant. If you want to add more detail rather than just the simple prompting that we're going to be doing for our assistants. This right here is the knowledge base. And you can create new knowledge bases from here. By again, describing a name for this knowledge base and providing a clear description of what this knowledge base contains. Something like prompting, but it will just give more information and organize this information right here for your AI assistant. And you will see that when we create these AI assistants, you can actually choose different knowledge bases to give to your assistants. It's like prompting, let's say, automated prompting for your assistants. Moving on, we got the operational hours, and pretty much it again, configures times when you're available for bookings and appointments, and this will play an awesome role when we also integrate other software because multiple times you will see that those voice assistants will actually and can be prompted, right, to take cold leads and make them book calls with you, right? So, you know, voice Jenny needs to know when you are available, so you can create working hours. And if you click on Create Working hours, you can pretty much describe, again, your working hours name, choose your time zone. So these AI agents know exactly when to book calls with you, right? So you're on the same page here, pretty straightforward. But again, these again, especially in outbound campaigns, they need to know when you're available to book a call in order to also communicate with potential clients and leads. Now, in the deploy section, you can see that we have the phone settings. And again, you can connect your phone to again, send these AI voice agents to take phone calls either with Twilio or Blevo, which are two different software options to connect your phone. And it's super straightforward, how to connect Twilio and how to connect Blevo, right? Both of these, we have like editorial here, which you can follow with a step by step guide. It's super easy, super straightforward. Just enter this softwaate here, either Pleivo again or Twillo to connect your phone. The phone is connected, and now your agents can utilize your phone, right, your phone's number or your business's phone number, just reach everyone with your campaigns, right? So we got this guide below. Again, it's super straightforward. I'm going to do this for safety reasons. I'm not going to give my phone here, but it's very, very easy to connect your phone. And again, if you connect your phone, the next step to deploy these voice agents is to also add a contact list. So in the contacts, you can again manage your contact lists and individual contacts. So you can create, again, new contact lists and import CSV files at bulk, right, of people that you want to target. And of course, when we're targeting someone, if we want to target them with AI voice agents, we need to have this data sourced and collected the data for people who we're going to be targeting, which includes at least their name, their surname, right, and their phone number. And, of course, the more information we have about people that we're targeting, the better, especially in the case of these AI voice agents. So you can see we have the name the description, the total account when this lead was created and what action will be taken, right? So this was the phone. This was the contact section, and now we move to the campaigns. And you get two different types of campaigns that you can create. You can create inbound campaigns and outbound campaigns. So let's take on inbound campaigns. As you can see, again, in inbound campaigns, you receive calls. So when do we use inbound campaigns? Let's say, for example, that you have a business and you want to automate customer service, right? This is going to be an inbound campaign. So you're receiving calls from people, for example, that have frequently asked questions or complaints, and you can outsource the communication to an AI agents. This is done by an inbound campaign. And you can create it from here. You click on campaigns, inbound, and you can create your campaign. I'm going to show you how to do this later on in later lessons. On top of that, you can click on campaigns, outbound, and now you're managing outbound campaigns. And what are outbound campaigns? Are verbs campaigns in which your AI agents make the calls themselves, right? So this would be top of the final deployment of AI voice agents to reach out, for example, as called outreach different people. In outbound campaigns, remember, agents are reaching out to other people and other businesses. In inbound campaigns, you are receiving calls and you're delegating them to these AI agents. So let's say, for example, that you have created your assistants, you have imported your knowledge base, you have again, completed your operational hours so these agents know exactly when you operate and when they can outsource, again, people and forward people to your calendar. You have added your phone and your contact list, and now you are starting to run these campaigns. What happens next, right after you've built and deployed your AI agents? The next step is to actually monitor, right? The data out of these calls that these AI agents have been taking, it's super important to monitor data and extract data from all of this process right here, so you know exactly how to perfect next campaigns of yours. So in the monitors section, if you click on calls, you will see those are some test calls that run, you can see that customer name is like my name, right? You can monitor your outbound campaigns, your inbound campaigns from here, right? And you can see the type of campaign, this was an outbound campaign, for example, the phone number of the person who again, you called in the case of an outbound cmaign, right? The status. So, for example, this call is ended the call duration. This was a four minute duration, as you can see, right? The assistant name, so which assistant did you use or which assistant was used here? The campaign name, customer name, did he answer the call? Yes, he did. And if you click, you can see more information which I'm going to show you. In just a second. So again, he answered the call, right? Voicemail was not detected. Custom SMS was not invoked. I'm going to show you again how to create SMSs. And if you actually click on each again, call here, a new menu will pop up. Let me actually click on this one right here. A new menu with more information about the call will pop up, right? So you can see, let's say the phone number, right? The call over you was 4 minutes and 15 seconds. When did it start? When did it end? Again, we got a whole recording of the call which you can preview, and a again, breakdown of exactly what happened transcribed right here. So we got clonal lyrics, right, and everything, then the whole call with as much information as possible on the monitor call section. So let's talk now about system, right, and in general, the different integration that you can add to Voice Gen, which is where the true magic happens. So if we click on integrations, right, these are the five platforms that you can integrate to Voice Gini. So we have HubSpot, which is a customer engagement platform that helps businesses manage their customer data and drive growth. So again, if you want to measure data, and help your business thrive through measuring data. HubSpot is like the place to go. Go high level is also another option to integrate with landing pages and website. And again, there are other again, easier to use voice assistance on go high level. Caldt com is definitely an integration that you want to have if you want these voice agents to book appointments automatically. So they book appointments through cal.com, which is super easy to use. Zapier is an a software that helps you automate different process with artificial intelligence, and you can definitely connect Voice Gini to Xavier, for example. Let's say that you want to connect Voice Gen with your Instagram DMs, and you can actually program Zapier whenever you get an Instagram DM with someone giving you their phone number to receive an automated call from a voice agent, AI voice agent from Voice Gini. So those are the type of automations that you can drive through Zapier, there is real, real power in Zapier. And, of course, in Level Labs, which is an AIPard Virtual Assistant platform. Now, the most important, again, integrations for you to enable here is definitely cal.com if you want to be booking meetings with these AI agents, and I would also suggest you to connect Zapier because you never know when you're going to be using these voice agents in an automated process, right in an automation. So it's super important to connect these two. The other three I haven't connected. I haven't experimented with, but definitely cal.com and ZAPer I 100% recommend. Now, regarding the APIs, here, you can again, manage tokens and receive the API of Voice Gini if you want to integrate it with other software and other applications, again, manually, and in the members tab, you also choose different members to add to your workspace. For example, I can invite more members to add to my workspace if again, I'm running a business that's more than one person, right? But for the sake of this demonstration, I think that we don't need to invite more members. So this right here concludes the interface overview of Voice Genie. I believe that we set foundation on what this platform can do and how you can leverage voice agents through this software. Now let's actually go ahead and create our first voice agent, create our first assistant, right? So I'm going to be doing this in the next lesson of the score. 13. Create Your First AI Voice Agent: Now it is time to actually create our first AI voice agent using voice Genie. And that's exactly what we're going to be doing in this lesson right here. Again, in the next minutes, I'm going to be walking through a step by step process on how to deploy, create, and prompt your first agent. So I'm super excited to have you here. Let's actually go ahead and do this. Now, inside of Voice Genie, as you can see, in this Again, first homepage of the software, we click on assistance, which again, as we analyzed in the previous lesson, Voice Genie is subdivided into four categories. Build, deploy, monitor, and system. And for the sake of our campaigns, the first step is to build the assistants themselves, and build, we click on assistance, and we're navigated into this page right here. Now, in this page, you can see the default assistance that Voice Genie already has the lead glification agent, right, the customer service agent, the survey feedback collection agent, and we could go ahead and click on this button right here, this blue button to create our first voice agent, our first voice assistant. So once I click on this button, you can see that it asks for the assistant name, the company name, and you can either choose a already pre made template or create a new template by scratch. Now, let's say that we want to create an assistant, right, a customer service assistant for inbound campaigns, right? So when someone calls our service calls our business, I want this call to be redirected into this new assistant that I'm creating right now. So let's name this assistant, for example, right? Customer Service. Agent, right? Company name, let's just call it Lambs, right? And again, we can choose from templates. So this is going to be a lead qualification agent, a service and feedback collection agent or a customer service agent. Now, for this case, we can actually click on customer service agent. But let's start from scratch. For me to show you how you can actually prompt these assistance. So we click on start from scratch, right? And we click on Create. Now, as you can see, right now, this new customer service agent of ours is created and if we go back and I click on Edit Assistant, we can open this menu for us to start editing the ins and outs of our assistant, right? What is the first thing that we can tweak here? The first thing that we can tweak is the LLM, the large language model that will be, again, sourcing this assistant with, let's say, knowledge and context. And in this case, this is hajVty 4.1 mini. And in voice, Gene, you can have habit 4.1 mini, hajbt 40 or Gemini flash. So let's just use 4.1 min. Then you can tweak the language. You can have, Bulgarian, Chinese, Croatian, Czech, you know, Danish Dutch, English. So let's keep English, right? And you can go ahead and actually configure your voice accents, right? So we have English with voice accents, and you can choose which assistant you want to use, which is super cool. And you can see how much in depth you can go with this whole customization process right here. So you can choose, for example, Jenna, or sell or Michael James, right, or Daniel or Denzel. And you can see that this, for example, Denzel, who has a deep voice speaks in English, right? He's from the US, so he has an accent from the United States of America. He's a male, and he's powered by 11 labs, which is this AI voice assistant software that's playing on the back end, right? To help us with the voice assistant. So if I just increase the volume of my system here, we can actually test the deep voice of Denzel, so I'll click on blade. In the vast ocean, the gentle whale reminds us that truth happens. So this is Denzil. For example, Daniel has an Australian accent, right? He's from Australia, male powered by 11 labs. Let's see. Hi. My name is Alex. I have a casual conversational tone with an Australian pretty, pretty pretty self explanatory, right, right? Or let's say someone else with a different accent. You can see we have 729 different accents to choose from right now, I'm playing with Jessica, who's from the US, bad by 11 Labs, and she's a female, right? Adam, for example, right? He's from the United Kingdom, right? Male, bad by 11 labs. Let's check it out. In the silence of midnight footsteps, echoed closer. I didn't really like that one. Let's see Arum the final one, Arum Branham, right? Again, from the USA, male 11 Labs. Don't settle. Use mine for clarity, nuance. I actually pretty like it. So let's select this one, right? This one's selected. As you can see, selected voice agents hits right here, um Branham, right? And we can also add voice filters, right? So again, choose the gender, choose the accent, choose the category, choose the country. If you want to go more specific and want again, direct, let's say, a very specific accent to your voice system. So if you click on save changes, right now are again, customer agent customer service agent is configured. Now let's move to the goal. And again, in this small box right here, you need to define the primary purpose or objective of this assistant. This helps guide its behavior and responses towards achieving specific outcomes. So the goal. The goal is to, let's say, solve any problems that customers may have and don't require human intervention, right? Again, the welcome note. So, for example, how does this voice agent welcome people when they're calling. For example, hi, then he quotes the first name of the person who's calling. How are you doing today? And you can also have a more detailed script. So, for example, opening with a warm welcome, right? Hello, first name, right, which again, will be imported by all of the datasets that we have imported in the knowledge base or in our contacts list, right? Thank you for reaching out. I'm here to assist you because again, this is an inbound campaign. So these people are calling us, right, and we're using this AI assistant to delegate, right? Could you share a bit more what you're looking for or how it might help? And again, the goal. The goal is to start with a friendly, open ended question to understand the customer's needs and set a positive tone. Two, explore customer needs. The second objective. Could you please tell me a bit more about what's most important for you in this area. For example, some customers look for benefits, features relevant to your service such as reliability, affordability, customization, et cetera, right? Goal, invite the customer to elaborate on their needs and priorities without focusing on a specific product. So in this side, we just create the prompt. We prompt the assistant on how he should act, how he should communicate, and what should be the outcome of this conversation right here. And you can go ahead and manually type this prompt or you can use AI to type this prompt for you. You can use JGBT, for example, to create a detailed prompt on how you want to prompt your AI assistant to communicate with people in those inbound campaigns. And if you want, the prompt to look exactly like this one right here, what I suggest to you is to actually go ahead and copy all of this, right, copy, paste it on GPT, and give a prompt to GPT something like, Hey, this is an example of a script that I'm using to prompt my AI assistant. I want you to give me the exact same type or framework of prompt, but by changing ABCD, whatever you want to change. It's super again, easy to create these prompts, and this is exactly how these AI voice assistant will be operating. And again, the variables that we're going to be using in this prompt is the customer number and the first name. That's it. So in these cases, these again, vary the first name and the customer number. So when you add them, the voice assistants will be quoting their first names, and they will also be quoting their customer numbers. And again, as you can see, there are more settings that we can tweak in these AI voice assistants. For example, in the general settings, you can tweak the company name, right? They can also reference, if you will, the company on their scripts, right, by clicking on customized use variables and adding more and more variables or regarding the conversation. Right? What we can tweak here is to allow, again, voice assistant to reengage with the user if the user has not responded or change the response time limit in seconds. For example, again, have a bigger response time minute or a lower response time minute. Let's have 10 seconds, for example, response time minutes. Do you want your voice assistant to re engage, right? Again, you enable this setting to allow the assistant to reengage with the user if the user has not responded. So let's say the user, you know, does not respond, he doesn't hang up, but he doesn't respond. Right in this case, we allow our voice assistants to re engage, right? And how many attempts should they do? Should they do two attempts, more attempts, so you can prompt them to do up to five attempts. So five times if you do not engage, they will try to reengage and re engage and re engage even more. And how do they re engage? For example, in this case, the prompt that we have given for these A assistant to reengage is, sorry, I didn't get any response for you. Are you still there, right? And you can tweak the stuff regarding the conversation. You can see how many different parameters we can change with this awesome software here regarding calling, right? If again, if they reach voicemail, should you leave a message or should you hang up, right? Regarding different action that they can take, right? Which is actually the most important thing of this whole prompting of our assistant. What action do you want this assistant of yours at your custom coding right now to take? For example, let's say that you want to transfer these people that we're talking to a human being, right? And you can see, during call, right, if the contact wishes to talk to human, we will forward the call to these numbers, right? The call will be tried with all the numbers, but only connected to the first agent who picks up the call. So in this case, again, an AI voice agent receives the call, he handles the first engagement, the first impression, right, with the potential customer. And then, again, if the contact wishes to talk to a human, let's say you have a big team of five different people in five different sections waiting to pick up a call, you can have all of these numbers ring, and then one of them just pick up the phone, and the voice agent, delegates the number specifically to this one, which is super cool. And if you click on this and you enable it, it will transfer people to human when this voice system again understands that, okay, it's time now to transfer to a human being, or you can actually enable a custom SMS. If you set up again, customizable trigger prompts, like, send me a brochure or can we set up an appointment during a call, right? When these prompts are spoken, an SMS with your predefined message in a template will be sent automatically or regarding booking a meeting, right, this allows leads to easily schedule meetings via a phone call, right? So it gives them the flexibility to choose a convenient time, streamlining the booking process, and enhancing engagement, right? And this, if you want to enable it, as you can see, you need a C account to attach events here. So with your cal account, which if you remember, in the integrations, it is this scheduling software you can actually have these voice agents work for you and book meetings on call accounts. Or if you want, you can actually have a completely custom call to action, right, with API calls, right? And ABI is if you want to further integrate Voice Gini into other software that haven't been added into the integrations. So this was the action section. Now let's move to the knowledge base. If you want to prompt this assistant with, again, already an already set of data sets, you can do this with the knowledge base, which you can add from here. So in this knowledge base, you add more information and more information regarding context, right, of what this AI voice assistant should know and should understand about your customers. And then you can import this knowledge base exactly right here. I'm going to have a look in the knowledge base in just a second. So again, this is where you input knowledge bases, right, and you can create as many knowledge bases as you want to give context to as many voice assistants as you want, right? Finally, in the setting stab of your voice assistant, we also have the post call analysis section. And for example, in the call inside, right, you can modify the selected question details, right? So if you click on question, right, you can add a question, a shortad type of question, and you can also have a webhook, if you will, again, gain more information about the post call analysis, which we're going to be checking out in just a second. So right here, this is your assistant. This is the assistant that we created, and we can actually go ahead and test this assistant for the sake of this demonstration. So I'll give you a short test, and then in the next that's one I'm actually going to be further engaging with voice assistant to understand better the context behind them, how they communicate, right, and then also be able to monitor the calls in the montor section. So this pretty much concludes the lesson in which we created our voice assistance inside of voice Gini. As you saw, there are so many parameters that we can tweak. And if you also count the fact that you can data, you can input more data through those custom knowledge bases, right, apart from just prompting the assistance yourself, using other AI software, right? You can just imagine how many different types of assistance you can use. Now, I know that we have been working on the back end, and we have been working, let's say, behind the scenes in those assistants. So let's actually go ahead and see how the assistants operate and how people can engage with these assistant in the next lesson, again, of this course. So in the next lesson, we're actually firing up one of these assistants, and we're engaging with him to show you exactly how the tonality of this assistance looks like and how they will be interacting with your customers or your potential leads. So thank you very much for Ming here. I'm going to see in the next lesson of the course. 14. Test and Fine-Tune Your Voice Agent: Previous lesson, we went ahead and we actually created custom created our AI voice agents, and now it is time to go ahead and test their capabilities, right? Test how they communicate, their responsiveness, their context, the elements of this interaction. And we're going to be doing this on a live basis on this lesson right here. The first time that I tested a voice agent, my mind was completely blown away, and the magic won't happen actually in this lesson. The magic will happen in lesson in which we're going to be analyzing the data from this conversation and how categorized and organized all of the data that these agents collect will be, right? So let's actually go ahead and engage with these AI agents in this lesson read here. I'm super, super excited to have you here, right, finally. So we are here in these agents, and for the sake of this demonstration, we're going to be testing out the lead qualification agent. So again, this agent identifies leads, understands challenges and connects with suitable reps if needed. So let's actually go ahead and test this assistant. And once you click on Test Assistant, you can either choose a native dialer or web calling. So for the sake again of this demonstration, let's do web calling, and let's launch. Hi, LambrosKlaz. How are you doing? Hey, how are you doing today? I'm doing great. I'm doing great. How about you? Glad to hear that. Doing well. Thank you. This is NOVA from Edgeway Solutions. We help businesses improve their operational efficiency through custom software solutions. Do you have a few minutes to chat about how we might be able to help your business? I'm actually kind of busy right now, but sure, yeah, let me know. I understand you're busy. Just briefly, the main benefit our clients in your industry see is a significant reduction in manual process bottleneck, improving overall efficiency. Could you tell me a bit about your business and the industry you operate in? Yeah. So we're currently operating in the e learning space. We run a course content production agency, if you will. We produce courses and educate people across the whole world, like, through cutting edge technologies and new upgrades and updates in the world of content and AI. Yeah, so that's pretty much it. Great point about using cutting edge tech and E learning. What systems or processes are you currently using to manage your course content production and client interactions? Yeah. I mean, regarding course production, things haven't evolved in a crazy way. I mean, I personally don't adopt AI, but my course production. I use a traditional camera, traditional microphone. So I guess a bottleneck here could be the fact that my personal time is died into the scaling my business, right? I need to invest like my personal time and energy in order to produce more courses and educate more people. So this definitely is It's an exact. M can you hear me? Yes, I can hear you clearly. You mentioned your personal time as a bottleneck for scaling your course production. How is this impacting your business growth or revenue? Yeah, I mean, definitely we're doing good numbers here and we're educating, like, lots of people, which is what matters. I've never tested trying to remove myself out of the equation of course production and actually producing the courses, like, outsourcing this production thing to a team, cause I really feel like this personal touch of mine is a secret sauce, if you will of why these courses are successful. But, yeah, That's an insightful point about your personal touch being the secret soft. Have you explored any solutions or tools to help delegate or automate parts of your production process, so you can scale without losing that quality? I actually haven't actually haven't tested. I've experimented with AI a bit, but I feel like we're not there yet regarding content production with AI. It's too expensive and the outcome isn't that good. So yeah, the answer is actually no. Thanks for sharing that. Based on what you said. Our operations OS could help by automating workflow task, freeing up your time while keeping your quality standards. Would you be open to exploring how that might fit your need? Yeah, sure. Why not? Sure. Great. What's your timeline for implementing a solution like this? Are you looking to start within the next few months? Yeah, I mean, if it works and if it delivers results, I can start next week. Excellent to hear. Have you allocated a budget for improving your course production processes or would you need to discuss that internally first? I guess we would need to discuss that internally, yeah. Understood. Who else would be involved in evaluating a solution like ours for your business? Honestly, just me, just me. I am the decision maker here. Thanks for clarifying that. If you were to implement a new solution, how would you measure success in improving your course production? I guess I could measure the course production speeds and also the revenue generated from each course if it's, you know, as substantial as it is right now. A solid approach. Based on our conversation, I think it would be valuable to have you speak with our specialist in workflow automation. Who can provide a tailored overview of how operations OS could help with scaling your production efficiently? Would you be available for a 30 minute call tomorrow at 10:00 A.M. Or Thursday at 2:00 P.M. Yeah, actually, we can do Thursday at 2:00 P.M. I think this would be perfect. Is it Greek time or like UST? Good question. 2:00 P.M. Slot is in your local time zone, Greek Time. Range for our specialist to connect with you then. I'll send you a confirmation shortly. Thank you for your time today, and I look forward to our next conversation. Great. Thank you. Thank you for your time today. Looking And that was the call. How cool was that? I mean, not only and here's the call summary, right? Exactly when the call is done, you get this call summary. But before we analyze the call summary, how cool is the fact that it doesn't only give me, like, you know, custom coded answers, right, and already coded answers, but it understands, right? It understands context. And it was super fast, right? I feel like this is I was pretty cool and actually pretty scary because, like, AI is gonna just keep getting better and better and better and better. And these agents, at some point, I feel like we're not going to be we wouldn't be able to distinguish them from, you know, like, real human beings. So that was an experience, right? So this concludes, like, the test lesson in which we tested these agents. And let's actually go ahead and analyze the call summary now to see the data that we can extract from this call summary, right? I'm super excited. Let's move to the next lesson. 15. Feed Your Agent the Right Data: Previous lesson, we learned how to monitor our calls and how to gather all of the data that have been extracted from the potential leads that are AI agents, again, targeted and talked to. Now, let's say that you want to give out specific information to these AI agents of yours. You want to train them on specific details around your business or you want to train them around specific tasks, right? So potential leads and potential clients do not talk with generic AI, if you will. You want to train your AI. Well, this is completely possible, right, with these AI agents here invoice Genie, and I'm going to show you exactly how to do with the knowledge base. Now, with the knowledge base feature, we will be inputting information directly to the LLM of our again, voice agents, and these voice agents will act with complete, again, context around the business that they're serving and again, how to achieve the let's say, ultimate outcome that we want them to do so. So let's open Voice genial. Let me show exactly what I'm talking about. So there's the com page again, and in the built section, as you can see, right below the assistant tab, which we have open right now, you can access the so called knowledge base. As you can see, the knowledge base states, manage your knowledge bases for all of the AI assistants, right? So we click on to create a new knowledge base, and by the way, you can have multiple different knowledge bases, which can be inputted into different assistants of yours. It's not that we have just, you know, one knowledge base or of course, one assistant, you can have different knowledge bases going to every single one of your assistants. And if I create a knowledge base, as you see, it asks for a name and a description. So let's say for the sake of this demonstration right here that I'm running a course production business, right? And I want to train people around exactly what we do as a business and how we can serve clients. So let's say that the name, for example, would be of the knowledge base. The name would be Lamb's course creation, again, knowledge, and the description would be this right here. A central hub, training data covering course creation, production and marketing processes. And Aclicon save and we have the knowledge base right here. Now, again, you can have multiple different items per page, more than ten per page, you can have multiple pages. So again, you can imagine you can input all of the data that you have around your business. You're going to input data around, again, specific tasks of your business. Let's say that you want to have a knowledge base around editing, right online course editing or online course production or online course sales, online course marketing to train your agents right across these different datasets you can do so, right? But just in this video, I'm just giving you a general demonstration of how to create a knowledge base. So once you click here, you click on the knowledge base, you get to actually input information to the knowledge base itself. So you want to train, if you will, your knowledge base, and this is the button that we're going to be clicking. Train knowledge base, but not now. First, we need to give context and context and context and data as much data as we have around, again, our knowledge base. So if you will, you want to give knowledge. At this point. And we do this with either inks or PDFs. So if you click on PDF, you can upload files, right? Again, you can manually upload PDFs from your computer that have information around what you're going to be teaching to your AI agents, or you can do links. So, let's say, again, for the sake of this demonstration, we're doing a link of my landing page right here, which is the course creation services that we offer, right? And I'm going to be pasting the link right here, right? So, paste the link, add URL, and the URL is now added. So as you can see, right here next to URL, you can see the status, right? Of this training. And now the status is red, which means that it hasn't been trained. The knowledge base hasn't been trained around my website. Let's actually open more pages of my website. For example, right now, I'm opening the creators page of my website. I'm going to be copying this and pasting it again as a URL. So this is another URL. I'm going to be opening the SAS page of my website, right, again, copying this, pasting it right here. So I'm giving as much information around my website as possible, right, for consulting, let's say, again, right, pasting every single page of my website and some case studies, right, right here. I'm going to be copying this and pasting it right here. So we have all of these different URLs of my website, which now we can train the knowledge base upon. As you can see here, indicated, but this number next to the links, we have five links. And what we're going to be doing right now is that we're going to be clicking on Train knowledge base. And just like that, the knowledge base is slowly and steadily being trained around all of the links that we have given it. It needs a minute, right, or two, it says, training your bolt to be smarter or hang tight or preparing your data. And now the training is complete. So now, again, regarding the Lamb's course creation knowledge, the database has been trained into all of different pages of my website. So this is how you can give more context by adding URLs or PDFs to your knowledge base, which can be applied directly to the custom voice agents or the already prebuilt AI, again, voice agent assistants that already are inside of voice Genie, right? So now that you know how to give out more context and more information to these agents to deliver their jobs perfectly, I think that it's time for us to actually create our first campaign, right? So that's going to be happening in the next lesson of the square. 16. Connecting a Phone Number Though Twillio: Now it's time to actually begin our inbound or outbound campaigns with these AI voice agents. And in order for us to be able to do this, we need to register a correct phone number, which corresponds to the correct country or jurisdiction. Who were targeting, right? And in order to register our phone number, we need to get access to either Twilio or Plebo. So that's exactly what I'm going to be showing you in this lesson right here. In this lesson, we are actually going to be integrating our phone numbers inside of Voice Gini. So I'm super happy to have you here. Let's actually go ahead and do this. So inside here on voice Gini, right, you need to access in the deploy category because that's what we're doing right now. We're starting to deploy these voice agents that we have built, right, and in the phone subsection. So in the phone subsection, as you can see, you need to select a service provider to get started. So this will either be Twilio or Pleivo, right. In our case, we will be using Twilio. And in order to set up Twilio, the steps are right here. So again, we go on Twilio. We click on this button right here, and we sign up for Twilio, right? And it will look like this once you sign up. The second step, as you can see, is to go to phone numbers in the left panel, click on Manage, and then buy a number. So again, we click on phone numbers right here, manage and then buy a number. So once we click on By a number, you can see that you will be able to choose which country you want your phone number to be from, right? The capability. So do you want this phone number to only do voice? Again, services, do you want this phone number to be able to send SMS, MMSs, fax, right? And also, you can search by digits or phrases of the number. In our case, we're talking about US numbers right here. And these are all of the different numbers that we can choose from, right? So again, you see this number, for example, offers voice, SMS, MMS, and fax and it doesn't have an address requirement. Now, you click on By the number, right? And once you buy the number, by the way, you can do this with the test account on Plivio without needing to input your credit card, you can actually create a test account. On Plivio, right, you click on By, you agree with the terms and conditions, and again, you click on By. So once you purchase this number, you can go on your account dashboard. You click on account dashboard right here. And if you wait a second, you will gain all of your account information. This is the account SID, the authorization token, and your previous phone number, which is the phone number that you actually purchased. So the next step here, right, let me just go back here. So the next step once you are in your account dashboard and you have purchased your number is to actually go ahead and copy the account SID go back here, right? And upwards, right? Add phone number. This is where you paste the account, SID, for example. We just paste this, right? Then the authorization token, you copy it and you paste it right here, right? And then the leave your phone number. Again, you copy this, paste it right here. And finally, let's do a name. So let's say this is the US ,paignO campaign agent one, right? And you click on Create. So just like that, you have added your phone number, and you have integrated this phone number again on Voice Genie from Twilio. It was super straightforward. And by the way, in order for you to be able to do this, you do not need to pay anything once you again, are in the free trial of Twilio, which offers, again, these phone numbers. So right now, we have our phone numbers setup and you can add as many phone numbers as you want from as many jurisdictions as you want from as many countries as you want, everything is, again, accessible from Twilio. Again, you can integrate to Voice Gene. So now we have our phone number right here. It is time to move and actually start creating our inbound and our outbound campaigns, right? So in the next dozen, I'm going to show you how to create an inbound campaign. So I'm super happy to have it here, see you the next dozen. 17. Setting up Inbound Campaigns: Welcome to lesson in which we're going to be creating our first inbound campaign. And again, what is an inbound campaign with voice agents? It's a campaign during which we set up voice agents to receive calls, right? So an example of an inbound campaign would be if you had a business and you wanted an automated way to answer frequently asked questions with your customer service, for example, right? Whenever you receive a call, of a client, then an AI voice agent would be a perfect fit. So your AI voice agents do not make the calls, they answer the calls for your business, and then potentially they can also transfer people to an actual human being, right? So that's exactly the campaign that we're going to be creating right now after, again, we have inputted our new phone number inside of Voice Genie. So let me show you exactly how to create inbound voice campaigns. Step is again to go in the deploy section inside of Boys Genie. And once you're in the deploy section, you click on campaigns, right? For an inbound campaign, we wouldn't need to have any contact important. The fact that again, we're receiving calls. We're not making calls for the outbound campaign, which we're going to be doing in the next lesson of the courses, we obviously need contacts to know exactly who we're going to be targeting. So we campaigns, inbound. And now we are in this menu right here. And, of course, it's super self explanatory words. What's the next step here, right? You click on Create a campaign, right, and you get to name the campaign. So let's name this, for example. Customer frequently asked question setup, right? So this is the customer frequently asked questions setup. Now, obviously, in order for you to create an inbound campaign, you need to select a voice assistant. And this means that you also before creating the Cbound campaign, you must have created an assistant that is here to answer, again, your frequently asked questions and serve these people, right? In our case, we can use, for example, the customer service agent, right, for inbound campaigns. This is a service agent which was already created from Voice Gene and given us as again, an already made agent, right? So we choose our voice assistant. In this case, it is, again, the customer service agent for inbound campaigns, and then it's time to input our phone number, which in this case, is the phone number from Twilio, right, which is this number right here. So now that we've actually inputted our voice assistant, right? We can choose the campaign actions, right? So this is again, the message content, which really depends on the assistant that you're using. And you can actually choose if you want this assistant to transfer, again, people who call to a human being. So if you click on Transfer to human being and you click on Plus, you can add a new number for this voice agent to again transfer to human being, or you can also use an existing number, right? So again, this only if you want your voice agent to transfer people to human beings, right? Or if you don't click on this button right here, your AI voice agent will just serve completely. Again, people who call for inbound campaigns. On top of that, you can also choose if you want to book meetings. So again, this allows leads to easily schedule meetings via a phone call. Let's say you have a dental office, right, or you're setting up AI voice agents for a dental office. You don't want to be transferring people to human beings. You want people to talk to your AI voice agents and be able to again book meetings via phone calls, right? So again, this feature gives them flexibility. You choose a convenient time, streamlining the booking process and enhancing engagement. So if we again, activate the book a meeting section. You need to integrate a CAL account, which as we talked previously in this again, in previous lessons of this course, a Calcut is an appointment scheduling application which will be integrated with voice Gene and will help people schedule appointments automatically through your voice agents. On top of that, you get if you want in the post call analysis section any call insights, you can add questions from here or the web hook. So we can go ahead and actually create this new campaign right here. And you see that this campaign, the customer frequently asked question setup, which we just created is in progress, right? It's upgraded right now, and it is activated. If you want to pause this campaign, let's say if you don't want, for example, whenever you receive a call on this number to have the customer FAQ setup, you just click on this and the campaign is paused. You get the small notification right here. If you want to reactivate it, you click here. And again, the campaign will reactivated. And this is, again, how you create inbound campaigns. Again, for inbound campaigns, you do not need any contacts. Inbound campaigns target people who call you in the first place, right? And they're served by your AI voice agents, right? So not that we're done with inbound campaigns, let's actually move and discuss how to create outbound campaigns and then how to measure the results again coming from your campaigns. So thank you so much for being here. I'm going to see you in the next lesson of the squares. 18. Setting up Outbound Campaigns: We learned how to create inbound campaigns, it is actually time to start creating outbound campaigns. And outbound campaigns are campaigns during which your AI voice agents will reach out to a select group of people in which you have obtained their contact information. Now, in order to create our outbound campaigns, we need to have an agent, of course, programmed and created again, serve and reach out properly to the specific group of people that we want to target. And, of course, we also need a contact list, a list of people that these agents will be reaching out to, right? And that's exactly what we're doing in this lesson right here. In this lesson, we're going to be taking the scenario of a real estate booking agent, right? So we're going to be developing together an AI voice agent who will be a real estate booking agent. Right? And we will be setting up an outbound campaign to target people who would be interested in, again, real estate bookings, right? So let's actually go ahead and do this. So if we want to create an outbound campaign, we need to deploy campaigns and outbound. But before we actually do this, and before we create a campaign, because if I click on Create Campaign, you can see that a voice assistant is needed here and a contact list is actually needed here. So let's go ahead and program our voice assistant for this outbound campaign. We're going to go on build, right, assistant and create assistant. This assistant will be named real estate booking agent, right? The company name. Let's say it's going to be Lamb's holding, right? And we're going to be starting from scratch, click on Create, and now it is time to actually go ahead and tweak this real estate booking agent. So let's go ahead and edit this assistant, right? We can choose the GBD model again. We did this previously, right in previous version of the scores, so I'm not going to get into too much detail here. The goal would be to again, book, let's say, live meetings for real estate pitching, right? Pitching. So this is the script, and I'm going to copy this script because now we're going to be tailoring this for this real estate booking agent. I'm going to copy this script right here, and I'm going to open GPT to just create a custom prompt for this agent, right? So I'm going to be actually dictating to GBD how to create this custom prompt. So I am trying to program the prompting of a real estate booking agent whose job would be whose goal would be to book live meetings for, again, real estate pitching to close real estate deals. I'm going to be pasting a test script, right, of a previous agent, and I actually want you to follow this exact framework of scripting and provide me the script of a real estate booking agent. So this is how we prompt our agents with GPT, and this is the script, right? Gonna be pasting the script, already created by Voice Genie inputting this to GPT and waiting for the correct responses, right? So here's your real estate booking agent script, building the exact same framework and tone as your sample, but customized for the goal of booking live meetings to pitch and close real estate deals, right? Let's copy this, right, open with a warm welcome. Hello. Thank you for reaching out. I'm here to assist you with your real estate needs, right? I'm not going to go into too much detail here because again, this is again, okay, so we're coming here. This is a test case example of a real estate, again, booking agent. So you can take your time when creating these scripts for your agents. But I just again, pasted this script here, and I'm changing just the layout a bit, so it's easier for Austin understand. Right? And what you need to understand here, tweaking this, right? So I'm just also going through this confirmed contact information, wrap up with gratitude, right. There you go. And I think that's perfect. And the goal again, end with warmth and reinsertness, reinforcing trust and anticipation for the upcoming meeting, right? And again, these are the first name variables. Alright. Awesome. So we got the variables here. We got the script, and we can go through the script. You open with a warm. Again, welcome. You explore the customer needs. Then you offer relevant options or solutions, right? You encourage the next steps if actionable, right? And you confirm contact information if needed, you wrap up with gratitude. That's it. So let's go ahead and actually save this, right? And now this script will be saved for Voice Genie inside a voice Gene for this agent of ours. Now, we can go ahead and actually further tweak this, for example, choose the conversation, how many we engage attempts. We're going to have the knowledge base. We can train this agent on a specific knowledge base that we can, again, create the creation of this agent. But we talked about how to create assistance and how to prompt assistance, right? So we're not going to go into too much detail here. Once this agent is created, we can click on assistant, and you can see that we have this assistant created right here, which is the real estate booking agent. What is the next step here? The next step to create our outbound campaign with a real estate booking agent to obviously import the contacts that we need to be targeting. And do this, you go to contact, create a new contact list, right, and we need to enter a descriptive name. So let's do, for example, real estate warm leads. Let's say you have a lead list, the people have already taken action. They have inputted their name, their surname, their contact details, right, and you want to retarget them with your agents, with your real estate booking agent, right? Let's say, it's a list with people, who have again, opten in a free realist state, let's say, webinar. All right. Just like that, we create the contact list, again, real estate worm leads, right? Then we can go ahead and tweak this contact list. Update contact list. And if I click on here, you can see that we can upload the contacts. So once you click on Upload contacts, you can either I can upload a CSV temple, right? You can upload a CSV. You can download, I'm sorry, a sample template, right? You can upload your file, right? And that's pretty much it. So let's actually go ahead and download this template, right? And I'm going to be pasting it. I'm going to show you in 1 second. Right here, what I'm going to be doing is that I want you base it to drag and drop it to GPT. And in this case, I'm just drag and dropping this CSV template to GPT in order to create, right, my contact list. Because again, obviously this is a test case scenario, so I just want to create a contact list would look exactly like the contact list that you would have, for example, if you were to upload contacts inside of Voice it with GPT. So I attached an example of a contact list of people that we're going to be targeting with our real estate agent outbound campaign. And I want you to create a dummy contact list that I can actually import again, just like this. So we're waiting a bit and I'm sending this to GPT Alright. And actually, now that I think about it, I can just import the contact list that we just created right here, right? So this is the conlex example, right? Let's open the contact list and let me show you exactly how it looks like, and it opens with numbers, actually, right? So this is, let's say, the contact list. This is how it's going to look like. It's going to have the customer number, the email, the first name, last name. That's how you want your contact list to look like in the CSV file, right? And let's actually open GBD to see what it came up with. All right. Yes. So we're going to be creating a new contact list, right M v. So this right here is the contact list that JVD came up with. It has the customer number, the email, the first name, and the last name, and that's ideally how you want your CSV contact list to look like, right? So I asked, again, GBD to create a downloadable version of this contact list, right? And I am going to be doing this and I just again, downloaded this contact list. So now what I'm going to be doing is that I'm going to be taking this dummy contact list, and I'm going to be pasting it right here. I'm going to be dragging and dropping it inside of this contact list of Voice Gini and clicking on Upload. So just like that, I uploaded all of my contacts from my contact list, again, inside of Voice Gini. You can see all the information here. We got the customer number, we get their email, we got their first name, we got the last name. And again, if you collect information of people from people from lead lists, or whatever, it will look like this. So let's assume that this right here is the contact list of warm leads that we want to be targeted for, again, this real estate outbound campaign that we're going to be doing. So how do we create actually the campaign? We go to campaigns, click on Outbound, right, and create campaign. The campaign name will be called Real Estate book meetings, right? The voice assistant we're going to be using is going to be the real estate booking agent, right? The phone number will be, of course, any phone number that you have chosen, and the contact list will be the real estate is warm leads, right? And just like that, you can actually also tweak the actions of your agent, right? So do you want your agent to transfer people to humans? Do you want your agent to again, send custom SMSs to book meetings. In our case, for example, we would want to book meetings, and in order to do this, we would need a call account, which is, again, this appointment scheduling application that we're going to be using, right? Or we can transfer to human beings. And if you want to transfer to human beings, you click on plus, and you add a new number to be redirected to, right? So this is how you tweak the actions of your voice agents on your outbound campaigns. And regarding post call analysis, you can also tweak, again, the settings from here. Now, the difference between an inbound and an outbound campaign is the fact that in inbound campaigns, you are waiting for calls. I outbound campaigns, you are performing the calls. And since this is an outbound campaign, you can add some extras. For example, say that your agent calls someone and he doesn't reply. How many times will your agent try to recall? In this case, it's five times, for example, right? So you can tweak this stuff on your Oban campaign. And just like that, your aban campaign has just been created. Again, we have a custom agent targeting a custom lead list that we have imported inside of Voice Gini, and I will just wait for the agent to perform the assigned tasks that we assign him to. So the next step here is to actually go ahead and monitor how these agents performed in our calls because this is one of the biggest advantages of using AI voice agents, the fact that we can monitor exactly what happened in these calls and how these calls. And that's exactly what I'm going to be showing you in the next lesson of the scores. In the next lesson, we're opening the monitor tab, and we're actually going to be analyzing the metadata of what happened inside of every single call that these agents perform, right? So I'm super, super excited to have you here, and I'm going to see you in the next lesson of these. 19. Monitoring Campaign Analytics: Now we've created our inbound campaigns and our outbound campaigns, and we have deployed both of our inbound and outbound agents, the inbound agents receiving calls and the outbound agents making calls. We will have lots of data to work with and lots of data to analyze. And this is exactly what I'm showing you in this dozen right here. In this dozen, we're opening the motor call section. Voice Genie. I'm going to show you exactly how to gather data from your inbound and outbound calls, right? So let's actually go ahead and dive into this lesson. So how do you access the monitor calls section inside of voice Gene? And in general, the whole process of building and deploying a voice agent is super self explanatory. You have the built section in which we build the assistance. You have the deploy section in which you deploy the assistance. And finally, we have the monitor section which we're going to be accessing right now after we have built and deployed our assistance. So in the motor section, you click on calls, and as you can see, in filters and controls, you can create outbound again or inbound settings and filters to check out and preview your outbound and inbound, again, statistics and data. For example, on the outbound filter and controls, if you can see all of your different again calls that have been created. Now, these were demo calls created, and let's see what we can actually measure. So first of all, you can see the phone number which made the call. We can see, again, the type of the call, was it inbound or outbound? In this case, we have filtered all of our calls to just see outbound calls, right? The status of this call, for example, this has ended, right? The duration of this call, this call was 5 minutes and 3 seconds. This is the name, right, which assistant was assigned. Right for this call. In this case, this was the lead qualification agent, the campaign name, it was a demo campaign, the customer name. So the person who was targeted in the case of an out campaign, obviously. And this was me. Was the call answered? Yes, was voicemail detected? No, was SMS invoked? No. So this is all of the information, all the data that we get before we even open, again, the call to see what happened inside of the call anyways, right? So the next step is to actually click on the call to see exactly what happened inside of the call. And as you can see, the first thing is that you see the phone number, which was used, right? Okay, you see right here a small preview of how many minutes and how many seconds Again, the call duration was, it was a demo call in this case, and you also get a call summary. So that's the most important thing and the coolest thing in my opinion that you get in outbound calls, get an immediate summary of what happened in the call, so you don't need to actually go ahead and check out the transcript and everything, right? So in this case, the conversation involved a sales pitch by NOVA from Edware Solutions, who discussed how their software solutions can help improve operational efficiency for a client in the e learning space. The client described their traditional course production processes and expressed concerns about scaling without losing quality. Expressed openness to explore potential solutions, showing interest in a follow up meeting scheduled for next Thursday to discuss further, right? So you can see the call duration was again, 5 minutes. You can see the start time, 6:30, the end time 635. 637, I'm sorry. And you can also listen to the call recording, right? On top of that, this whole summary right here was created based on the call transcript, and that's also something that you get. The whole call was transcribed, so you know exactly what the agent said, what the customer again, answered, how the conversation went, so you can literally analyze exactly what your agents talked about and how the customers responded. You can potentially also tweak, again, the metadata of your agents if you don't like something here, or if you like something, you can analyze this transcript and copy some of the elements to the rest of your agents. Now, if we click on back to calls here, you will be also able to analyze inbound calls. And in inbound calls, again, we have the exact same statistics here, the status of the call, the call duration, the assistant name that was used, the campaign name, the customer name, if the call was answered, voicemail was detected, and if SMS was invoked. And if I also click on an inbound campaign, again, you get the same data. So a call summary of what happened inside of the call, a call overview of how many minutes the call was, right, and a transcript of the call. So pretty much in both outbound and inbound campaigns, you get the first series of data here, which was a general overview of what happened in the call, and then exactly a call summary with a transcript. And this, ladies and gentlemen, is how you monitor your calls using Voice Genie, which pretty much also concludes this demonstrative series that we started two lessons ago by again creating inbound and outbound campaigns. So again, I'm super happy that you made it up until this point of the scores, and I'm going to see you in the next lesson. 20. Launch and Scale Your First AI Campaign: So, ladies and gentlemen, up until this point, we have analyzed every single piece of the puzzle. We have broken down the framework of creating an AI voice agent and deploying it. And now it is time to actually connect all of the dots together and bring this puzzle to life. And that's exactly what we're doing in this lesson right here. In this final lesson, I'm going to show you all of the steps that you need to take in order deploy these voice agents and actually create your inbound or outbound campaigns. I'm super, super excited that you have reached this point, and I can't wait to show you this whole process around. So again, a small recap. Up until this point, we have created our assistant, right? And this could be custom assistants. This could be the assistance that they're already inside of agent, voice Gene, right? We have created our knowledge base, and we have actually imported this knowledge base into our assistant. So our assistants now, context, right? We have added our operational hours, if you will, if we want to funnel these assistant to book calls with us, right, if we want to make the voice assistant urge clients to book calls with us. And of course, we have integrated cal.com, which is super important if we want, again, to book meetings through these assistants. On top of that, right, the next step is, of course, after analyzing that everything works with our assistance, which we have done so, right, to close the built section. So now we're done with the built section. So how do we actually deploy these assistants? The first step, right, is for you to connect your phone through Twilio or Bleibo, right? And again, these are just operational admin tasks that we're doing right now. The hardest job was to build out the assistant, right? So right now, you need if you want to launch a campaign to connect your phone through Twilio and Playbo and you can do this by following the step guide inside of Voice Genie, right? So once again, your phone is connected through Twilio, right? Or Playbo. The next step is to import all of the contacts that your AI voice agents will be reaching out to if you're doing outbound campaigns. Because if you're doing inbound campaigns, it's a completely different thing. You're just waiting for calls right to come to your place. But for outbound campaigns, you definitely need to import your contact list. And we do this by in the deploy section in the contacts. So in the contacts, as you can see, you can click on Create Contact List, and you can add a descriptive nave, let's say, the contact list, let's call it Lambs, leads, right, and a list of of respects to call right, a list of, let's say, cold prospects call. We created the contact list right here. And if I click on the name, you can go ahead and actually upload the contact. Now, as you can see, you can either import from GHL or from Hotspot, or you can directly, again, add contact manually, right? So their phone number, their name, their last name, their email, right? You can either manually add your contacts or you can import them through, again, either GHL right, Go high level or HubSpot. So once you have added all of your contacts here, you have also, again, added your phone information, the phone that we'll be dialing these contacts through Twilio or Blvo, the next step is to start your campaign. You can either start an inbound campaign or an outbound campaign. So let's say you want to start an outbound campaign. You should create campaign. You get to name your campaign. You get to choose your voice assistant. You get to choose the phone number that will be used for this campaign right here and the contact list that you want to be again contacting for the sake of this campaign, right? Now, you can also tweak different actions you have for different campaigns. For example, you can either choose to transfer people to talk to human beings, right? So to have an initial contact being done by these voice agents and then transfer them to human beings or have a custom SMS being sent as the final deliberative right of this campaign. Or you want to funnel people to book meetings, right? So again, allow easily scheduled meetings via phone call, right? This feature gives them the flexibility to choose a inconvenient time, streamlining the booking process, and enhancing engagement. And of course, you can also check out the post call analysis. Do you want to call inside, right, with more questions? Do you have a web hook if you want to further analyze the calls? And of course, finally, you can add the extras. So, for example, how many retry options you want your voice agents do. Again, five, let's say, in this case, and that's pretty much it regarding your campaigns. You create the campaigns and you run the campaigns. And all the results of your campaigns, every single phone call that will be done through these campaigns will be monitored right here, right, either in the outbound or the inbound, depending on the type of campaign that you choose. And that's pretty much it. This is how straightforward it is to use voice Gini for your AI voice agents. It is, again, super straightforward to create the voice agents give them information, give them context with the knowledge base. And again, it's super easy to prompt them correctly to do the exact task that you're looking for regardless of if this is inbound or outbound calls, right? So this concludes, again, the campaign deployment. So this again, concludes the campaign deployment part of the scores, and I'm going to see you in the final message. 21. AI Voice Agents Thank you message: Genuinely like to congratulate you for not only enrolling in this course and starting to consume the first lessons, but actually playing full out and consuming the whole course. Now that you belong in a very small percentage of people that again, not only managed to enroll, but actually consumed the whole thing, and I'm super, super proud of you. I genuinely believe that AI agents can completely revolutionize the way that we communicate with each other, and again, we'll help you just unlock your full creative potential by outsourcing the robotic repetitive work again, AI agents which don't get bored. Don't get tired, right? And if you enjoy this AI course right here, and you want to learn more about artificial intelligence and how again, to just unlock your full creativity by outsourcing stuff to AI, I would be happy to have you in other courses that I have created in my profile here, right? I'm super excited that you made it. I'm at the end of this course, and I'm going to see you in the next course that I create.