What are AI Voice Agents?

Voice agents are software designed to automate tasks and solve problems faster and more conveniently for both you and your clients. The next step is clear and unsurprising: multi-modal models and agents built on top of them are reallocating human resources to more valuable tasks. The AI in Voice Assistants Market is projected to reach USD 31.9 billion by 2033, with a compound annual growth rate (CAGR) of 28.5% from 2024 to 2033.

Currently, voice agents serve roles like therapists, coaches, and support agents, and in the future, they will likely expand to cover an even broader range. AI is the new electricity, set to integrate into every part of business operations. With the right strategy, this shift presents a powerful opportunity for growth and efficiency. Let’s explore how you can leverage AI and voice agents to stay ahead and succeed.

Use cases in B2B SaaS

These are standalone B2B SaaS solutions where AI voice agents form the core offering, providing specialized services to businesses across an industry.

Real Estate Assistants

AI voice agents act as virtual assistants for real estate companies, handling tasks such as scheduling property viewings, managing client inquiries, and providing detailed information about listings. These SaaS solutions enable multiple real estate businesses to enhance customer service and streamline operations.Example: A SaaS provider offers a voice agent platform that real estate agencies can subscribe to. This platform allows any agency to automate client communications, schedule showings, and provide 24/7 access to property information, improving efficiency industry-wide.

Healthcare Scheduling

Medical practices utilize AI voice agent SaaS products to manage patient appointments, answer common health questions, and provide pre-visit instructions. This service is available to various healthcare providers, reducing administrative workload and improving patient access.Example: A company provides a voice-enabled scheduling system that clinics and hospitals can integrate into their operations. Patients can interact with the AI agent to book appointments and receive information, benefiting numerous healthcare institutions.

Legal Assistants

Law firms use AI voice agent SaaS products to automate client interactions, schedule consultations, and provide preliminary legal information. This solution helps various legal practices streamline client intake and communication.

Example: A legal tech provider offers a voice agent service that law firms can implement to handle initial client inquiries, gather case details, and schedule appointments, enhancing efficiency for multiple firms in the legal industry.

From IVR to AI Voice Agents

We are transitioning from traditional phone Interactive Voice Response (IVR) systems and phone trees to a new wave of AI voice agents powered by Large Language Models (LLMs), Text-to-Speech (TTS), and Speech-to-Text (STT) technologies. This evolution is further enhanced by emotional intelligence and multimodal capabilities. While traditional IVR systems offer proven reliability and familiarity, the scalability and potential of LLM-based agents are significantly greater.

This shift is transforming the landscape of phone support teams. Current phone support professionals are finding opportunities to retrain and evolve their roles, moving from routine call handling to more complex, value-added responsibilities. Meanwhile, businesses across B2B and B2C sectors are discovering new possibilities for automated, intelligent customer interaction that was impossible with traditional IVR systems.

Traditional IVR Systems Explained

Interactive Voice Response (IVR) is the familiar phone system that greets you with "Press 1 for billing, press 2 for support..." These systems were revolutionary when introduced, offering the first widespread automated phone interactions.

A typical IVR interaction flows like this:

• IVR: "Welcome to Acme Bank. Please say or press 1 for account balance..."
• Customer: *presses 1*
• IVR: "Please enter your account number..."
• IVR systems:
  • Use pre-recorded messages and menu options
  • Recognize basic voice commands ("yes," "no") or keypad inputs
  • Follow pre-defined conversation paths
  • Route calls to appropriate departments
  • Provide basic automated information

Modern AI Voice Agents

Today's AI voice agents represent a technological leap, powered by three core technologies:

1. Speech-to-Text (STT)
   • Converts natural speech to text in real-time
   • Handles various accents and speaking styles
2. Large Language Models (LLMs)
   • Process and understand natural language
   • Generate contextual responses
   • Handle complex conversations
3. Text-to-Speech (TTS)
   • Creates natural-sounding speech
   • Supports multiple voices and languages

Here's how an AI voice agent handles the same scenario:

• Customer: "I need to reschedule my appointment from next Tuesday to Thursday afternoon if possible."
• AI Voice Agent: "I see your appointment for next Tuesday at 2 PM. I can check Thursday's availability for you. Would you prefer early or late afternoon?"

Capabilities and Limitations

As AI voice agents revolutionize customer interactions, it's crucial to understand both their strengths and current constraints. While these systems offer remarkable improvements over traditional IVR, they come with their own set of challenges that businesses need to consider.

Key Capabilities of AI Voice Agents

1. Advanced Conversational Abilities
   • Natural Language Understanding: Comprehension of complex sentences, idioms, and colloquialisms
   • Context Awareness: Maintaining context over multiple exchanges
   • Fluid dialogue management with natural transitions
2. Emotional Intelligence
   • Sentiment Analysis: Detecting user emotions
   • Empathetic Responses: Providing appropriate emotional support
   • Tone adaptation based on conversation context
3. Scalability and Efficiency
   • 24/7 Availability
   • Handling high volumes of simultaneous interactions
   • Consistent performance under load
4. Continuous Learning and Improvement
   • Machine Learning Adaptability: Learning from interactions
   • Personalization: Tailoring responses based on user history
   • Ongoing optimization of responses
5. System Integration
   • API Connectivity with CRMs, scheduling tools, and databases
   • Real-time data processing and updates
   • Seamless workflow integration
6. Advanced Features
   • Multi-language support
   • Voice biometrics
   • Real-time analytics
   • Function calling capabilities during conversations

Current Limitations of AI Voice Agents

1. Customer Adoption and Trust
   • Uncanny Valley Effect: Discomfort with highly human-like voices
   • Lack of awareness about natural AI interaction
   • Initial hesitation and skepticism
2. Technical Complexity
   • System Compatibility challenges
   • Resource-intensive processing
   • Response latency (700-2500ms) contrasts with typical human turn-taking, often under 300 ms.
3. Data Privacy and Security
   • Sensitive information handling risks
   • Regulatory compliance (GDPR, HIPAA)
   • Data protection requirements
4. Ethical and Legal Considerations
   • Transparency requirements about AI interaction
   • Liability issues for misinformation
   • Ethical use guidelines
5. Language and Cultural
   • Accent and dialect recognition challenges
   • Limited proficiency in less common languages
   • Cultural nuance understanding
6. Contextual Understanding
   • Nuance Detection: Difficulty with sarcasm and humor
   • Emotional Misreading: Potential misinterpretation
   • Complex situation handling
7. Data Quality Dependencies
   • Training data bias issues
   • Data scarcity in specific domains
   • Quality consistency challenges
8. Performance Variability
   • Novel Scenario Handling
   • Error Recovery Challenges
   • Unpredictable situation management
9. User Experience Consistency
   • Response variability
   • Over-reliance on scripted interactions
   • Balance between natural and efficient communication

As you see, the real challenge here is not even the technical complexity—first and foremost, it's customer adoption. Today, no one is surprised by the robotic voice of an IVR and everyone is used to it, but hearing a voice that is indistinguishable from a human voice and communicating with it as you would with a human on the other end of the line is still not typical. In the next section, "Common challenges and how to overcome them" we'll explore practical strategies for addressing these adoption barriers and other key limitations.

High-Level Architecture

The architecture of an AI voice agent mirrors a standard client-server application with third-party integrations and consists of:

1. Client (phone, web browser, desktop app, etc.)
2. Streaming part
3. Multimodal Agent - Speech-to-Text (STT) model, Language Models (LLMs), and Text-to-Speech (TTS) model

An AI voice agent typically follows this workflow:

• A user initiates a call through a smartphone or desktop device
• The user's speech is transmitted to the server hosting the agent
• The agent processes the speech and generates an audio response
• The response is sent back to the user

Communication Channels

Businesses can implement AI voice agents through two main channels – web or phone.

Web-Based Voice Agents

These agents communicate with users through internet protocols:

• Utilize Voice over IP (VoIP) technologies for audio streaming
• Can be accessed through web browsers and mobile apps
• Support WebRTC (Web Real-Time Communication) for direct browser-based communication

Popular services include Livekit, RingCentral, Agora, Nextiva, CallHippo, Dialpad, 8x8, and Vonage. For a detailed comparison and guidance on choosing the right tool for your needs, check our article "Choosing the Right Web Communication Tool for Your AI Voice Agent."

Phone Voice Agents

These agents operate through traditional phone networks:

• Use Public Switched Telephone Network (PSTN)
• Allow users to interact through standard phone calls
• Integrate with services like Twilio for phone connectivity

By employing VoIP for web agents and services like Twilio Media Streams for phone voice agents, a wider range of users can be reached. This approach allows people to interact with the agent either through the internet or by making a regular phone call, depending on what works best for your business use-case.

Multimodal Agent vs Multimodal Model

There are two main approaches to implementing AI voice capabilities.

Multimodal Agent

The multimodal agent is the core component of an AI voice agent system, responsible for understanding user input and generating appropriate responses across multiple modes of communication—primarily speech and text. It integrates several advanced technologies to process spoken language, interpret intent, and deliver natural-sounding replies.

A multimodal agent is the entire system that allows an AI voice assistant to understand and respond to both speech and text. It uses several specialized tools working together.

• Speech-to-Text (STT): Converts what you say into text.
• Language Models (LLM): Understands the meaning of that text and decides what the response should be.
• Text-to-Speech (TTS): Converts the response back into speech, so the AI can talk to you.

These three parts work together to allow the voice assistant to have a conversation. Each component is responsible for a specific task.

Multimodal Model

A multimodal model with real-time audio support like GPT4o-realtime, on the other hand, is a more advanced, all-in-one solution. Instead of using separate components for speech and text, a multimodal model can handle everything—understanding what you say, processing it, and responding to you—within a single model. It combines all the capabilities in one package. 

For a detailed comparison and guidance on choosing between these approaches, read our article Multimodal Agents vs Models: Making the Right Choice for Your Business.

Current market landscape

In recent years, we've seen a fundamental shift in voice AI technology. Agent-based voice systems now combine natural language understanding with proactive, task-oriented capabilities. These modern systems not only respond to requests but can anticipate needs, execute complex tasks, and work seamlessly across different interfaces.

Big Tech and Enterprise Solutions

In 2024, major tech players - AWS, Microsoft, Google, and OpenAI - continue to dominate with powerful Speech-to-Text (STT), Text-to-Speech (TTS), and multimodal models. Their solutions, like Amazon Transcribe, Microsoft's Azure Cognitive Services, Google's Speech APIs, and OpenAI's voice-enabled GPT-4, offer enterprises scalable, high-quality voice solutions tailored to various demands—from customer engagement to workflow automation.

Innovation in Specialized Sectors

While big tech provides the foundation, startups are finding success in niche sectors where specialized Voice Agents are essential. They often choose to bypass big-tech infrastructure, prioritizing unique features like highly realistic voices, quicker response times, and on-device processing for enhanced data privacy. However, they face significant challenges in infrastructure, scalability, and availability while pursuing these innovative features.

"While it's easy to envision and prototype AI products, the path to sustained success and profitability can take decades." – Andrey Karpathy [YouTube link]

Companies like Suki (voice assistant for medical professionals), Speak (AI-powered language learning), and Ada (AI-driven customer support) demonstrate both the potential and challenges in the industry. Their success highlights the demand for specialized solutions and the competitive edge gained by deep industry focus, while also illustrating the extensive work needed for long-term growth and profitability.

Tools and Infrastructure

The modern voice AI tech stack is complex, featuring core components like Large Language Models (LLMs), STT, TTS, and streaming capabilities, with robust application layers supporting various uses. Middleware providers and specialized APIs like Vapi, Bland.AI, and LiveKit offer modular solutions, helping developers incorporate sophisticated voice technology features without the overhead of building foundational infrastructure. For a detailed comparison of available tools and their use cases, check our article "Complete Guide to Voice AI Development Tools."

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Market Growth and Trends

The AI in Voice Assistants Market is projected to reach USD 31.9 billion by 2033, growing at 28.5% annually from 2024. This growth is driven by increasing demand for automation in business processes and enhanced customer service capabilities.

We're seeing exciting developments in:

• Real-time conversational AI with reduced latency
• On-device processing for better privacy
• More accessible voice synthesis tools
• Improved emotional intelligence in responses

Emerging trends in the voice AI space include real-time conversational AI that supports highly dynamic interactions, on-device processing that provides better privacy and reduced latency, and the democratization of voice synthesis tools, making advanced voice technology more accessible. 

With these trends, voice AI is becoming more customizable and practical across a broader range of applications, enabling developers to experiment and innovate in areas previously limited by resource constraints or technical barriers.

Pricing Models & Considerations 

Cost per Minute

For production-level AI voice agents, costs generally start around $0.07 per minute and can extend up to $1.10 per minute, particularly if using OpenAI’s Realtime model, which has exponential pricing that increases as conversations grow longer. Reducing the cost of AI voice agents to under $0.05 per minute is nearly impossible without sacrificing quality, making such pricing only viable for non-production, fun projects, or basic demos. 

At launch, OpenAI’s Realtime API was priced at $0.06 per minute for audio input and $0.24 per minute for audio output—a high rate, though optimized models have now brought costs down by 30% from the initial release. In practice, despite OpenAI’s projected cost reductions with prompt caching, the actual cost per minute can still accumulate significantly due to how prompts are processed, resulting in expenses between $0.20 and $1 per minute. Here’s a preliminary cost breakdown based on real test cases:

• 1-minute call: $0.56 per minute
• 3.5 minute call: $2.80, or $0.73 per minute
• 5-minute call: $4.02, or $0.80 per minute
• 10-minute call: $9.40, or $0.94 per minute
• 20-minute call: $21.20, or $1.06 per minute

These pricing levels are still feasible only for cases where users are willing to pay a premium for high-quality, responsive interactions. 

Pricing Models

• Usage-Based Pricing – this model charges businesses based on the number of interactions or minutes of audio processed. It is particularly advantageous for companies with variable demand, allowing them to manage costs effectively according to actual usage.
• Subscription Models – many providers offer fixed monthly fees that cover a set number of interactions or features. This approach is beneficial for businesses with consistent usage patterns, as it provides predictable budgeting.

Tiered Pricing – сompanies can select from various service tiers, each with distinct features and limits. This flexibility allows businesses to align their choice with specific needs while balancing cost and capability.

Factors Influencing Pricing

• Feature Set: The complexity of the voice agent's capabilities, such as multi-language support or taking actions during the call (Functions Calling), can significantly affect costs.
• Data Security and Compliance: Solutions that offer enhanced security measures or comply with regulations like GDPR or HIPAA may incur additional costs. Businesses must evaluate their security needs against these potential expenses.
• Support and Maintenance: Ongoing technical support can vary in pricing; some providers include it in their packages, while others charge separately for premium services.
• Customization Needs: Tailored solutions that meet specific business requirements generally involve higher costs than off-the-shelf options due to additional development work.

We’ve conducted a broad pricing review, but it’s not detailed enough for precise competitor analysis. If needed, we can perform an in-depth competitor analysis, including pricing, during the shaping phase. This phase refines your idea into a robust business and product concept ready for early sales, moving through the Design and Build stages. Read more here.

Average Subscription Costs

• Local Businesses: ~$254/month
• Healthcare: ~$350-$5,000/month 
• Recruiting: ~$120/month
• Restaurants: ~$266/month
• Training: ~$66/month
• Finance/Insurance:  ~$350/month

Usage-Based Pricing Insights

Flexible, usage-based models are common, benefiting businesses with variable demand. Examples include:

• Hyro: $0.11 to $0.14 per minute
• Syllable: ~$0.10 per interaction

Trends Overview

• Shift to Subscription Models
• Customizable Solutions: Tailored pricing based on specific needs.
• Competitive Market Dynamics: Innovation and varied price points maintain affordability.
• Scalability Focus: Prioritizing scalable solutions, is crucial in sectors like healthcare and recruiting where demand can fluctuate.

Build vs. Buy Framework

As a founder aiming to develop a B2B product around AI voice agents, one of the pivotal decisions you'll face is whether to build your own technology stack or leverage existing solutions. This choice significantly influences your product's time to market, scalability, cost structure, and competitive differentiation. This framework is designed to guide you through the critical considerations in making an informed build vs. buy decision tailored to your entrepreneurial journey.

Factors to Consider

Below is a table summarizing key factors to consider when deciding whether to build or buy your AI voice agent solution:

Note: While the option to use self-hosted models on platforms like AWS Bedrock or Azure OpenAI exists when building your own solution, integrating these models can be complex and may not always be compatible with your existing architecture. This limitation can present challenges in terms of integration efforts, technical expertise required, and potential delays in deployment.

Pros and Cons of Building

Pros:

• Unique Value Proposition:
  • Ability to develop proprietary features that differentiate your product. This is common because you often need more than just the voice agent component; you may require integration with your CRM, third-party services, function calling, and actions after the call finishes (e.g., sentiment analysis, transcription, summaries, and other post-processing).
  • Developing unique capabilities can set your product apart in the market.
• Customisation:
  • Tailor every aspect of the product to meet specific market needs, such as voice customization options accessible via the product dashboard.
  • Ability to create industry-specific solutions, like specialized vocabulary for healthcare or finance, or integrating with niche third-party services.
  • Design the user interface and interaction flow to match your vision without limitations imposed by third-party providers.
• Flexibility:
  • reater ability to pivot or add features in response to market feedback, such as switching providers, adopting new technologies, or adjusting functionalities.
  • Quickly implement changes based on customer needs or technological advancements without waiting for vendor updates.
• Data Control:
  • Enhanced ability to manage data security and compliance, especially when using self-hosted models. Full control over how data is stored, processed, and protected.
  • Ability to implement strict data privacy measures to meet regulatory requirements and build customer trust.

Cons:

• High Costs:
  • Significant initial costs for development, infrastructure, and hiring the team
  • Continuous expenses for maintenance, updates, scaling, and adapting to market changes.
• Longer Development Time:
  • Potential delays in entering the market due to longer development cycles.
  • Risk of missing out on market opportunities and first-mover advantages.
• Integration Challenges:
  • AI voice agents and their associated challenges are not trivial; you need an experienced team to handle the intricacies of AI, NLP, and voice technologies.
  • Developing the system is not enough, designing a system that can handle growth without performance degradation demands careful planning and technical proficiency.
• Resource Intensive:
  • Difficulty in finding and retaining skilled professionals in AI and voice technologies.
  • Building the technology may divert focus from other critical areas like marketing, sales, and customer engagement.

Pros and Cons of Buying

Pros:

• Speed to Market:
  • Accelerate development timelines by integrating existing technologies, allowing for faster entry into the market.
  • Capitalize on current market demands and trends before competitors.
• Lower Initial Investment:
  • Reduced need for capital to build foundational technology
  • Lower upfront costs compared to building from scratch.
  • Most industry lead providers like VAPI or Bland.ai provide with Pay-as-you-go model
• Focus on Core Business:
  • Allocate more resources to areas where you have the most expertise, such as customer acquisition, market research, and business development.
  • Simplify the development process by offloading complex technical aspects to the vendor.

Cons:

• Limited Differentiation:
  • Competitors may use the same technology, making it harder to stand out in the market.
  • Inability to offer unique features that aren't supported by the vendor's platform.
• Dependency:
  • Dependence on third-party providers for critical components and updates.
  • Vulnerability to vendor-related issues such as price increases, policy changes, or discontinuation of services.
• Customization Constraints:
  • Restrictions on tailoring the technology to specific needs beyond what the vendor offers.
  • Difficulty in responding to unique customer requirements or market shifts.
• Hidden Costs:
  • Costs can escalate with scaling, additional features, or premium support services.
  • Possible extra charges for exceeding usage limits or accessing advanced functionalities.
• Data Control:
  • Less flexibility in using self-hosted models, which can impact data security and compliance efforts.
  • Reliance on the vendor's compliance with regulations, which may not fully align with your requirements. 

Common Challenges & Solutions

AI voice agents are transforming industries like customer service, healthcare, and smart devices. Despite their potential, they face significant challenges that limit performance, scalability, and user experience. Below are the key challenges and actionable solutions for building effective, scalable AI voice systems.

1. Interruption and Pause Handling

Challenge:AI voice agents often fail to manage user interruptions and pauses effectively. Without a robust Voice Activity Detection (VAD) system, they struggle to pause or adjust their responses dynamically when users interject or correct them mid-conversation. Similarly, they may misinterpret intentional pauses in speech, leading to premature or inappropriate responses.

Solution:Integrate a VAD system capable of detecting user speech patterns, interruptions, and intentional pauses. This enables agents to pause and resume conversations naturally, improving the overall interaction flow. Sophisticated speech-processing algorithms should be designed to handle these scenarios, creating smoother and more engaging conversations.

2. Agent Behavior, Dataset Complexity, and Adaptability

Challenge:AI voice agents may still fail to perform as expected in real-world scenarios. Inconsistent responses, difficulty adapting to complex prompts, and misunderstandings during interactions highlight limitations in fine-tuning. Additionally, creating datasets that capture diverse scenarios and evolving user needs is resource-intensive, and static datasets often fail to keep pace with changing requirements.

Solution:Refine models using high-quality, adaptable datasets tailored to real-world complexities. Continuously monitor live performance and iteratively update datasets to ensure agents remain relevant and responsive. Modular approaches to dataset creation, coupled with targeted fine-tuning, enable agents to handle nuanced interactions effectively and adapt to evolving demands.

3. Scalability, Provider Integration, and Adaptability to Market Changes

Challenge:Scaling AI voice agents to handle high interaction volumes while managing operational costs and ensuring compatibility with diverse technologies is complex. Additionally, the fast pace of AI innovation demands systems that can quickly adapt to market changes and technological advancements.

Solution:Adopt a modular system architecture that supports seamless scaling, integration with providers, and rapid adaptation to new technologies. Evaluate Speech-to-Text (STT), Text-to-Speech (TTS), and language processing providers based on performance benchmarks and compatibility with existing systems. This approach ensures flexibility to switch providers or scale infrastructure as needed. Building a robust and adaptable architecture also positions the system to efficiently incorporate emerging innovations and meet evolving user expectations.

4. Realistic Voice Generation

Challenge:Many AI voice agents sound robotic or unnatural, reducing user trust and engagement. Achieving human-like voices with appropriate tone, emotion, and intonation remains a challenge.

Solution:Leverage advanced Text-to-Speech (TTS) providers focused on realism, such as Cartesia, Play.ht, and ElevenLabs, which specialize in neural TTS technology. Custom voice models tailored to specific use cases can further improve vocal quality and relevance, creating more natural, engaging interactions.

5. Background Sounds and Backchanneling to Reduce Perceived Latency

Challenge:Noticeable silences between user input and agent response create a perception of latency, disrupting engagement. Additionally, agents often lack conversational cues like "Mm-hmm" or "I see," which make interactions feel more natural and engaging.

Solution:Incorporate ambient background sounds to mask response latency and create seamless interactions. Simultaneously, implement backchanneling features using advanced conversational models to provide real-time affirmations and conversational cues. Together, these enhancements improve interaction flow and user engagement.

6. Background Noise and Voice Filtering

Background Noise Filtering

Challenge:External noise such as music, car horns, or environmental sounds can interfere with the performance of audio-based models like transcription or speech recognition, leading to degraded system accuracy.

Solution:Employ a real-time noise filtering model to clean audio before it reaches downstream processing models. This ensures effective noise cancellation while maintaining low latency, improving audio quality for subsequent processing.

7. Background Voice Filtering

Challenge:Speech transcription models are designed to capture everything resembling speech, which can be problematic in real-world environments where background voices, television, or echo interfere with understanding. This can severely impact critical functions like interruption detection, endpointing, and backchanneling.

Solution:Deploy proprietary audio filtering models to isolate the primary speaker and block out other voices. These models focus on identifying the user’s speech, ensuring that only relevant input is passed to the transcription and language models. This approach significantly enhances the agent’s ability to maintain coherent conversations in noisy environments.

8. Latency in Response Times

Challenge:High latency between user input and the agent’s response undermines the real-time conversational experience expected by users.

Solution:Optimize system pipelines by streamlining the integration of Speech-to-Text (STT), Text-to-Speech (TTS), and language processing components. Employ low-latency processing techniques and reduce computational overhead to ensure quicker response times without sacrificing quality.