An overview of AI voice agents
The way we interact with technology is undergoing a seismic shift. Voice is one of the fastest growing interfaces, transforming how we engage with devices, applications, and each other. As the founder and CEO of Deepgram, I've witnessed first-hand the acceleration of voice technology and its profound impact on the tech industry.
From the second you make capable AI agents, you want to talk to them. This is a new scaled digital interface. Before we only had tapping and typing, and now there is talking. And in an era where efficiency and accessibility are paramount, AI voice agents are not just a convenience—they're a necessity. They bridge the gap between humans and machines, enabling seamless, natural interactions.
For tech professionals navigating this dynamic landscape, understanding the capabilities and offerings of different AI voice agents is crucial.
Unlocking New Possibilities: Use Cases for AI agents
AI Teammates
Going from co-pilot to full on AI Teammates that are part of your teams. These teammates can listen, understand and speak just like humans do. They attend meetings, ask questions, sign up for action items, making sure that they are asking for what they need from others to get their jobs done.
Enhanced Customer Service
AI Voice agents can handle customer inquiries efficiently, reducing wait times and improving satisfaction. By leveraging LLMs and high-fidelity TTS, they provide personalized and natural conversational experiences.
Front-desk Automation
For small businesses, doctor’s clinics, and quick-serve restaurants, being able to offer human-like voice agents can help keep quality of service high while managing costs in the face of rising operational expenses.
Accessible Technology
Voice interfaces, powered by advanced TTS and LLMs, make technology more accessible to those with disabilities or those who prefer hands-free interaction.
Coaching & tutoring
Whether you’re learning a new language, need help studying for a test, or preparing for a public speaking engagement, AI will soon become one of the best options for coaching & tutoring.
The Value Proposition
Integrating AI voice agents offers several benefits:
24/7, Personalized Availability
Efficiency: Streamlines operations by automating routine tasks.
Worker Productivity: Augment existing workforce by taking over repetitive, mindless tasks, freeing employees to focus on more strategic work.
User Engagement: Provides a more natural and engaging user experience through advanced LLMs and TTS.
Scalability: Handles high volumes of interactions including seasonal or event-driven spikes in demand without compromising quality.
Cost Savings: Reduces the need for large customer support teams.
The Evolution of AI Voice Agents
AI voice technology has matured from simple voice recognition tools to sophisticated agents powered by low-latency transcription, high-fidelity Text-to-Speech (TTS), and advanced Large Language Models (LLMs). The advancements in TTS have led to more natural and expressive voice outputs, while the productization of lower-latency LLMs has enabled real-time understanding and generation of human-like responses.
Key Considerations for building an AI Voice Agent
When choosing a voice agent API, consider the following:
Listening Skills: For applications where precision is critical, choosing the highest accuracy transcription model is advantageous. This is particularly important for enterprise applications that involve transcribing alphanumerics like phone numbers and addresses, PHI, and medical terminology.
Human Speed Responsiveness: Natural human interactions need to be sub-second and general consensus is that responses that take longer than that don’t feel natural.
Reasoning and Intelligence: For advanced understanding and generation, choose providers with robust LLM integration.
Conversation Flow Handling: For most providers, VAD-based endpointing is used behind real-time APIs to predict when someone is done talking and when to respond. Deepgram’s Voice Agent API uses a modern neural network based approach to contextually predict when someone is done speaking with higher accuracy and lower latency.
Natural Expressive Voice: No one likes a bot voice on the other end of the conversation. A natural-sounding speech is essential. The degree of expressiveness depends upon the use case Historically, there has been a tradeoff between voice quality and latency. Few providers off both, but this is becoming a technical reality.
Customization: If your application requires specialized vocabulary or industry-specific terms, choose a provider that offers custom model training or keyword boosting.
Scalability: Ensure the provider can handle your expected volume of interactions.
Support and Compliance: Enterprise-level support and compliance certifications may be necessary depending on your industry.
Hosting flexibility : Some customers consider it paramount to be able to host the models in their own cloud infrastructure or data center for various security, privacy and data residency reasons.
Key Components of Modern AI Voice Agents
Whether delivered as a unified Speech to Speech API or be-spoke API’s that are stitched together by vendors, the following are the essential components that make up a modern voice agent API.
Automatic Speech Recognition (ASR): Transforms spoken language into text with high accuracy.
Cognitive Architecture: Helps power the brain behind the listening and talking helping the Voice AI Agent understand and respond intelligently. This architecture is a combination of Large Language Models (LLMs), Retrieval Augmented Generation (RAGs), Knowledge Graphs and helps us experience human-like text, enabling contextual and coherent interactions.
Text-to-Speech (TTS): Converts text back into natural-sounding speech with high fidelity.
Contextual Awareness: Remembers previous interactions to provide relevant responses.
Multilingual Support: Breaks language barriers by supporting multiple languages and dialects.
Noise and Interruption Handling: The real world is messy and the Voice AI systems must be robust enough to handle it.
(Optional) Telephony: Connecting to the scaled voice network we are all familiar with (telephones) allows anyone to access Voice Agents without needing apps or browsers.
Comparing Leading AI Voice Agent Providers
This is a subjective and a point in time perspective. However, understanding the strengths of each provider helps in selecting the right partner for your needs.
Vendor Overview
| Vendor | Specialization | Key Strengths | Ideal For |
| Deepgram | Foundational Voice first Models | High accuracy, low latency, scalable with flexible hosting | Building AI agents for B2B use cases from AI teammates to front desk automation across all verticals. |
| OpenAI | Foundational Language Models | Powerful LLMs for language tasks | Conversational AI applications and real-time voice agents built for consumers. |
| Vapi | Platform Provider | Industry-specific customization | Rapid development of voice agents. |
| Bland AI | Platform Provider | Easy integration | Building an AI phone calling agent that can make phone calls. |
| Retell AI | Platform Provider | Engaging voice experiences | Building, testing, deploying, and monitoring AI voice agents at scale. |
| Sierra AI | Platform Provider | Agent Management Platform | End to End platform for building and managing your AI Agents |
The Road Ahead
Voice technology is no longer a futuristic concept—it's here, and it's transforming industries. The fusion of high-fidelity TTS and low-latency LLMs has opened new horizons for voice applications. As tech professionals, staying ahead means embracing these advancements and integrating them thoughtfully into our applications.
At Deepgram, we're committed to pushing the boundaries of what's possible with voice. By harnessing the power of advanced LLMs and cutting-edge TTS technology, we believe in a future where voice interfaces are seamless, intuitive, and ubiquitous.
Voice is the next frontier in user interaction. Let's navigate it together.