AI voice agents are intelligent virtual assistants that use advanced AI technologies to understand, interpret, and respond to human conversations. Compared with traditional systems, AI voice agents greatly reduce wait times and operational costs. Want to learn more details about the AI voice agent? Let’s dive into it together with Solvea.
What Is an AI Voice Agent
An AI voice agent is a software system powered by artificial intelligence technologies to understand and respond to human speech. It works like a human assistant that can answer questions, engage in conversations, provide relevant info, and complete actions by using technologies, like natural language processing (NLP), speech recognition, and machine learning.
To be specific, when a call comes in, the AI voice agent can quickly handle customer speech, understand what they are talking about, and give them an informative response. The entire process doesn’t require any human intervention.
AI Voice Agents Development History
The emergence of smartphones brought AI voice agents directly into the users’ hands. In 2011, Apple’s Siri offered users the ability to interact with the voice assistant using natural language. Later, Amazon introduced Alexa in 2014 and there comes Google Assistant in 2016, further expanding the capabilities of voice AI agents.
2024 was a year of major breakthroughs in AI voice. Various advanced models in voice agents have emerged one after another, like GPT-4o voice from OpenAI, Sonic from Cartesia, and Conversational AI from ElevenLabs. These models are also becoming more affordable over time.
As technologies upgrade, problems with conversational quality, like latency, interruptions, and emotional expression, have been solved mostly. AI voice agents now perform even better than BPOs/call centers.
The global voice agents market also exploded in 2024. According to the report from Cartesia, an ultra-realistic voice AI platform, companies building their businesses with AI voice have accounted for 22% of the most recent YC class.
How Does an AI Voice Agent Work?
AI voice agents work through a combination of multiple technologies to understand spoken language and respond appropriately. They can provide a seamless and interactive experience for users. Let’s start exploring its working process.
Note: Some advanced voice AI agents have more complex working processes and adopt newer models. The following content only displays general models.
Capture Voice Commands
When you raise a request or question, the AI voice agent will capture it with your device’s microphone immediately. Then it turns your spoken words into a raw audio signal.
For example, if you are using a smartphone in your living room, you say, “Hey, please sing a pop song”. The microphone first captures the voice command, next clears other background voices in the room, and then passes the clear audio of your request to the voice AI for further processing.
Automatic Speech Recognition
Then, AI voice agents will convert the raw audio signal into digital signals and then transcribe them into text with Automatic Speech Recognition (ASR). It is a technology that can turn spoken words into text.
For instance, when an Australian accent says, “G’day, can you help me?”, a well-trained ASR model won’t mix up “G’day” with “Good day”. It can transcribe the phrase accurately based on Australian English speech patterns.
Natural Language Understanding
Next, the transcribed text is analyzed by a Natural Language Understanding (NLU) system, a part of Natural Language Processing (NLP). This step is when the AI voice agent understands what you’re saying, including your true intent, the context of your request, and other details.
The NLU system works as a bridge between the text (from ASR) and informative responses. AI voice agents can’t understand the meaning of your words without this step.
Determine the Appropriate Action
After understanding your intent, the agent will give appropriate action or information that fulfills your request. It not only understands your goals but also maps out the steps required to achieve them and even lists different tools or data sources you may need.
For example, when you say, “I have a headache”, it can detect if you need corresponding medicines and mitigation methods. This process may involve utilizing external knowledge bases, using retrieval-augmented generation (RAG), or performing a task via an API.
Output Voice
At the final step, the synthesized speech is played back to you through your device's speaker, completing the interaction. It not only turns text to voice but also refines the voice to feel natural and clear. It uses the Text-to-Speech (TTS) technology to ensure the AI’s response feels like a real conversation instead of robotic output.
What Are the Use Cases of an AI Voice Agent
After investigating extensive references and user reports, especially from Reddit, we summarize some practical use cases of AI voice agents, ranging from different areas.
E-commerce
AI voice agents are now widely seen in e-commerce areas. They greatly enhance the customer journey by offering personalized shopping experiences and recommendations. They can track customers’ purchasing history and browsing behavior, enabling online stores to offer personalized product recommendations.
These agents also assist customers with purchase decisions by giving detailed product descriptions, comparisons, and even guiding them through the purchasing process. This can improve customer satisfaction with higher conversion rates.
Healthcare and Telemedicine
In the world of healthcare and telemedicine, AI voice agents are enhancing health-relevant services by assisting patients with consultations and basic medical advice when necessary. The use of voice agents is mainly reflected in patient triage and appointment scheduling.
In patient triage, they can handle initial patient requests, such as asking questions related to your symptoms, and determine the urgency of the medical problem. In the appointment scheduling, they automate this process by allowing patients to book, reschedule, or cancel appointments with ease, which increases operational efficiency in healthcare.
Financial Institutions
The AI-powered voice assistants in the financial sector help these institutions improve service efficiency while keeping high levels of security. They can monitor suspicious account activities for fraud detection, such as unusual patterns or transactions. Once detected, they will provide a secure and real-time response to prevent fraud.
Furthermore, they also help customers manage accounts, such as offering information about balance and recent transactions. Customers can resolve financial issues and perform routine transactions through simple voice prompts.
How to Build an AI Voice Agent
Now, the most important thing is to build an effective AI voice agent. This part will show you how to create an AI voice agent using three mainstream products on the market.
Method 1. Use Synthflow
Synthflow is a no-code platform that can help you build a human-like AI voice agent easily. It allows you to configure the agent’s identity, define the knowledge base for conversational ability, and so on. It offers you a 7-day free trial for Pro and Growth plans to test the platform. Let’s have a try.
Step 1. Determine Your AI Voice Model
Register an account for the Synthflow workspace and log in to it.
Go to the “Assistance” sector and create a new agent. Here you can choose inbound calls, outbound calls, or a website widget.
Determine the AI model you want, such as Synthflow's LLM.
Choose a voice for the agent.
Step 2. Customize Agent Capabilities
Implement knowledge bases that match your business.
Set up custom greetings for the agent that align with the tone of your industry.
Generate accurate voice prompts for the agent.
Step 3. Deploy the Voice Agent and Test It
Assign a dedicated phone number to the agent for receiving calls.
Perform a test call to let the agent listen and respond
Collect the agent data for further optimization.
Method 2. Use Vapi
Vapi is a powerful tool for creating voice-enabled agents that can handle phone calls with minimal human intervention. With Vapi, you can create and deploy an AI phone assistant to automate inbound and outbound calls.
Every new Vapi account can get $10 in free credits to start building without the need for a credit card. To do that:
Create a Vapi account: In the dashboard of Vapi, click “Sign up” and use your email to create a Vapi account.
Create a new AI agent: Click the “Create Assistant” button and select one from the pre-made templates or start with a blank template.
Configure the agent details: Select a voice for your agent offered by Vapi or services, like Cartesia. Then choose the large language model (LLM) to let the gent understand and respond.
Setting up a knowledge base: To ensure the agent provides informed responses, you need to set up a relevant knowledge base by adding support documents, FAQs, and even notes from your team.
Attach a phone number: Assign a phone number for the agent to receive calls.
Test the voice agent: Once configured, let the agent perform some tasks to test its performance.
Method 3. Use Bland
Bland allows you to create a natural-sounding agent for businesses to automate phone calls and perform tasks, like customer service, appointment booking, etc. Its Conversational Pathways feature enables you to build custom conversations. It offers voice cloning, multilingual support, and integrations with other apps.
To create an AI voice agent using Bland, you can refer to the following guide:
Get API keys: Sign up for an account using your email to get the API credentials.
Purchase a phone number: You need to buy a dedicated phone number for the agent.
Choose the voice model: There are two models: No-code or API. For no-code, go to “Conversational Pathways” and use the visual editor to customize the voice model. For API, go to the “Send phone call” page or use the API directly.
Set up the call flow and prompt: Set up the greetings that match your business and provide background information, like product, customer profiles, and common questions.
Test and optimize: Review the live transcript of calls to see if the agent performs well and adjust the voice prompts for optimization.
What Is the Best Practice of AI Voice Agents?
Here are the critical factors that you need to consider when designing and developing an AI voice agent.
Understand your user needs and pain points: Create detailed user profiles based on your actual customers and provide solutions that solve customers’ pain points.
Ensure natural interactions: Does your AI voice sound like an actual human? If not, you can use a good NLP model to understand and interpret what customers are really saying.
Ensure data security and privacy: It’s vitally important to protect your customers’ data security and privacy using strong encryption, secure storage, and complying with regulations, like GDPR.
Provide clear error recovery: We all make mistakes—even AI! Make sure your agents can recognize when they’re confused and recover with helpful prompts like” Could you rephrase your question?”.
AI Voice Agents FAQ
How to tell if someone is using an AI voice?
If the voice is AI-generated, it usually lacks natural variabilities, such as robotic tones or rhythms, overly smooth delivery, and inconsistent emotional expression. Besides, AI voices may have unnatural pauses, exaggerated pronunciation, or strange background noise.
What are the benefits of AI voice agents?
One of the biggest advantages of AI voice agents is that they provide instant responses when customers are in urgent need. They can handle high call volumes and automate routine inquiries, making live agents focus on complex customer issues.
What are the best AI voice agent tools?
There are a lot of AI voice agent tools that align with different businesses, such as Synthflow, ElevenLabs, Vapi, Deepgram, Bland, Retell AI, OpenAI’s Whisper, Lindy, Cognigy, and Murf.ai.
What are the biggest challenges in building AI voice agents?
After viewing extensive actual comments from Reddit, we found the hardest parts of building a real-time voice agent are as follows:
Latency: If the agent requires complex logic, most LLM call systems and voice pipelines find it hard to achieve natural sound.
Flexibility: Many platforms lack certain workflows, making deeper customization difficult.
Reliability – It’s hard to build and test agents to ensure they work consistently for your use case.