For developers using Relevance AI, the power to build and chain custom AI tools and agents is a game-changer. You can visually construct complex workflows, connect them to your own data, and deploy a powerful AI “brain” in a fraction of the time it would normally take.
But once you have built this brilliant agent, it often lives in a world of silence, limited to text-based interactions. The next logical step to truly unlock its potential is to give it a voice. This is where a VoIP Calling API Integration for Relevance AI comes in.
Imagine the sophisticated agent you built, one that can access your company’s knowledge base & and use external tools that could answer a customer’s phone call, have a natural conversation, and solve their problem in real-time.
This guide is for developers who have harnessed the power of Relevance AI’s low-code platform and are ready to transform their silent agents into active, voice-enabled assistants that can operate in the real world.
Table of contents
What is Relevance AI?
Relevance AI is a developer-centric platform designed to help you build and deploy custom AI agents and workflows with ease. Its core value lies in abstracting away the complexity of chaining together different AI components.
Features of Relevance AI
- Chain & Tool Building: It provides an intuitive interface to chain together LLMs, your own data sources, and any external API into a single, cohesive workflow or “tool.”
- Data-Aware Agents: You can easily ground your agents in your own data, allowing them to provide accurate, context-aware responses based on your company’s unique knowledge.
- API-First Deployment: Every agent and tool you build can be instantly deployed and accessed via a simple, secure API endpoint. This last point is critical for our integration.
Relevance AI gives you the power to build the brain. Now, let’s give it a voice.
The Problem: The “Silent Agent” Limitation
No matter how intelligent your Relevance AI workflow is, if it can only be triggered by an API call or a text input, its ability to automate real-world processes is limited. Many of the most urgent and valuable business interactions are still voice-based. Without a voice, your agent cannot:
- Handle an inbound customer support call.
- Automate an outbound appointment reminder.
- Provide an interactive, conversational experience for users who prefer talking over typing.
This is the exact barrier a VoIP Calling API Integration for Relevance AI is designed to eliminate.
Also Read: How To Test Voice Agents For Latency And Quality?
An Integration Guide for Developers
Connecting a voice to your Relevance AI agent is a matter of orchestrating a few specialized services in a high-speed loop. Thanks to modern APIs, this is more straightforward than it sounds.
The Four Core Components
A complete voice-enabled agent built on Relevance AI consists of four key parts:
- Your Relevance AI Agent (The Brain): The workflow or chain you have built on the Relevance AI platform, accessible via its unique API endpoint.
- A Voice Infrastructure Platform (The Voice): Your VoIP API provider. This is the foundational layer that manages the phone call, the phone number, and the real-time streaming of audio data.
- A Speech-to-Text (STT) Service (The Ears): An AI service that instantly converts the human’s spoken words into text.
- A Text-to-Speech (TTS) Service (The Mouth): An AI service that converts your agent’s text response back into a natural-sounding voice.
Also Read: How To Lower Latency In Voice AI Conversations?
The Conversational Data Flow (Step-by-Step)
Let’s walk through a live phone call. This is the core of the VoIP Calling API Integration for Relevance AI.
- The Call is Answered: A user dials a phone number. Your VoIP platform answers the call and establishes a live, two-way audio stream.
- The User Speaks: The user says, “Hi, I have a question about my recent order.”
- Audio is Transcribed (STT): The VoIP platform streams this audio in real-time to your chosen STT service. The STT API returns a JSON object with the transcribed text: “Hi, I have a question about my recent order.”
- The Brain is Queried (Relevance AI): This is the key step. Your application takes the transcribed text and makes a POST request to your Relevance AI agent’s API endpoint. The text is passed as an input in the request body.
- Relevance AI Executes its Workflow: Your agent on the Relevance AI platform receives the input. It runs through its entire chain—it might first call an LLM to understand the intent, then use a tool you created to look up the order status in your company’s database via an API call.
- A Response is Generated: After completing its workflow, your Relevance AI agent returns a final JSON response with the output text, for example: {“output”: “I can help with that. Could you please provide your order number?”}
- Text is Synthesized (TTS): Your application extracts the output text and sends it to your chosen TTS service. The TTS API returns an audio file or stream of the sentence spoken in a natural voice.
- The Agent Speaks: The VoIP platform plays this audio back to the user over the phone line.
Also Read: How To Add Voice To Chatbots With TTS?
Use Cases Unlocked by Voice-Enabled Relevance AI Agents
Once you complete your VoIP Calling API Integration for Relevance AI, you can deploy your powerful, data-aware agents in a variety of high-impact roles:
- Intelligent IVRs: Build a customer service IVR that can do more than just route calls. Your Relevance AI agent can access your entire knowledge base to answer complex questions directly.
- Proactive Support Agents: Trigger an agent when an event occurs (e.g., a shipping delay) so it automatically calls the customer, explains the situation, and answers their follow-up questions.
- Data-Driven Sales Assistants: Deploy an outbound agent that calls a list of leads, asks qualifying questions, and uses tools to check if they match your ideal customer profile in real-time.
Conclusion
Relevance AI provides an incredibly powerful, low-code platform for developers to build sophisticated AI brains. But a brain without a voice has limited ability to interact with the world. A VoIP Calling API Integration for Relevance AI is the essential next step, transforming your well-designed workflows into autonomous agents that can participate in the most natural form of human communication.
By connecting your agent’s API endpoint to a real-time voice infrastructure, you unlock a new dimension of automation and create truly interactive experiences for your users.
Also Read: Cloud Phone System: Everything You Need to Know
Frequently Asked Questions (FAQs)
Your Relevance AI agent, when deployed, has a unique API endpoint. The voice platform uses a Speech-to-Text service to convert the user’s speech to text, and then your application makes a standard API call to your agent’s endpoint with that text as the input.
Yes. The voice system waits for your Relevance AI agent to complete its entire workflow, including looking up information in your vectorized database or calling an external API via a tool, before returning the final text response to be spoken.
Latency. The total delay from when the user stops talking to when the agent starts responding must be extremely low (ideally under a second). It requires a voice infrastructure platform that is optimize for real-time streaming and fast processing.
No. The entire purpose of a VoIP Calling API platform like FreJun AI is to handle all the complex telephony for you. As a developer, you only need to interact with a simple, modern API.