Revolutionizing Businesses with Virtual AI Agents

If you want to provide a better customer experience, you might consider using a virtual agent. This is a software application that can talk to your customers on the phone, using natural language. It can understand what they want, give them the right answer, or do something for them. And if it can't handle the situation, it can transfer the call to a human agent. A virtual agent can help you save money, make your customers happier, and boost your reputation. But how do you create one?

In this article, we will show you use Azure services to build a virtual AI agent for your business. You will also see how, a leading conversational AI platform, used this solution to create amazing virtual AI agents for their clients.

Architecture of the solution

The key components of a Virtual AI agent solution typically include:

  • Telephony integration enables communication between the customer and the virtual AI agent over a phone call. 
  • Azure Communication Services is an offering from Microsoft that can help connect telephony infrastructure with the Azure AI services. It provides easy APIs and SDKs for voice and video calling, chat, and SMS. It can also serve as the orchestrator to handle key operations such as hand-off between the Virtual agent and Human agent. It is available in these countries.
  • Azure AI services: This is the component that powers the core functionalities of the virtual AI agent, such as Speech, Azure OpenAI, Language, Document Intelligence, and others.
  • Human agent: Handles the customer queries that the virtual AI agent cannot resolve, or if the customer so chooses. In such a case, the human agent receives the call from the virtual AI agent and a summary of the conversation so far.

The following diagram illustrates the architecture of the solution:

VA diagram.png

Creating a virtual AI agent using Azure AI services

To create a virtual AI agent using Azure AI services, you will need to follow these steps:

  1. Choose your telephony infrastructure.
  2. Create your speech recognition: You can use the Azure Speech service to convert speech to text, enabling your virtual AI agent to understand what the customer is saying. You can perform speech recognition in real-time or batch mode and in various languages and dialects. You can also customize the speech recognition model to suit your specific domain and vocabulary.
  3. Create your natural language generation model: You can use the Azure OpenAI service to generate natural language responses or actions based on the customer's intent and context. You can leverage the power of generative AI models, such as GPT-4 Turbo, to create realistic and relevant dialogues for your virtual AI agent. You can also fine-tune the models to match your business goals and scenarios.
  4. Create your text-to-speech model: You can use the Azure Text-to-Speech service to convert text to speech, enabling your virtual AI agent to speak to the customer. You can synthesize natural and expressive voices in various languages and styles. You can also customize the voice and emotion of your virtual AI agent to suit your brand identity and customer preferences. Additionally Azure Speech now also offers a suite of new super-realistic voices which are designed for conversations. The voice used in the demo above is one such voice. There are already multiple such voices available for use, and more are on the way.
  5. Create your other AI functionalities: Depending on your business needs, you may also need to create other AI functionalities, such as Azure Cognitive Search, Document Intelligence, CLU, Question Answering, etc., to enhance the capabilities and functionalities of your virtual AI agent.
  6. Test and deploy your virtual AI agent: You can use the Azure Bot Service to manage and monitor your virtual AI agent. Once you are satisfied with the performance and quality of your virtual AI agent, you can deploy it to your telephony infrastructure and start serving your customers.

Measuring impact

To evaluate the effectiveness and impact of your virtual AI agent, you can measure and analyze various metrics, such as customer satisfaction, operational efficiency, and cost savings. You can use Azure OpenAI to generate surveys and feedback forms, Azure Text Analytics to analyze sentiment and emotion, Azure Speech to measure the accuracy and latency of speech recognition, and Azure OpenAI to measure the relevance and quality of natural language generation.

Yellow.AI Case Study is an online B2B platform that helps businesses create powerful AI-driven bots for customer support. The platform allows businesses to connect bot users to human agents, run campaigns, manage bots on multiple channels, integrate with third-party applications, and measure bot performance. With's Dynamic Platform (DAP), businesses can use conversational AI tools to create no-code solutions for automating repetitive tasks and processes. The platform leverages generative AI to provide voice and text for customer and employee interactions, resulting in lower operational costs. is using Azure OpenAI and Azure Speech and Translation to power these intelligent bots and provide greater user value.

The accuracy provided by Azure AI models for Indian languages is far better than other products that we have tried. The ease of integration, enterprise-grade security, and extensive language support make it the perfect choice for our global audience.” – Abhimanyu Singh, Associate Director of Product – Voice AI,


This article was originally published by Microsoft's Azure AI Services Blog. You can find the original article here.