Build a Voice-Based Conversational AI With an SDK

The developer experience for building a Voice-Based Conversational AI has been fundamentally transformed. Thanks to a new generation of powerful and accessible Software Development Kits (SDKs) from platforms like OpenAI, ElevenLabs, and VideoSDK, creating a sophisticated voice agent is no longer a multi-year, research-intensive endeavor. 

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With just a few lines of code, developers can now install an SDK, initialize an agent, and integrate real-time Speech-to-Text (ASR), intelligent Large Language Models (LLMs), and lifelike Text-to-Speech (TTS) directly into their applications.

This SDK revolution has democratized access to cutting-edge voice technology. However, many teams embarking on this journey quickly run into a critical, yet often unforeseen, roadblock. The very SDKs that make it so easy to build a voice experience inside a web or mobile app are fundamentally not designed to handle the most common and critical channel for business communication: the telephone. This is the crucial gap between a clever proof-of-concept and a scalable, enterprise-grade voice solution.

What is Voice-Based Conversational AI?

Before diving into the challenge, let’s establish a clear definition. A Voice-Based Conversational AI is a system that enables natural, spoken interactions between a user and a software application. The core architectural pipeline involves several key stages:

1. Voice Input: Capturing the user’s speech in real-time.
2. Automatic Speech Recognition (ASR): Transcribing the live audio stream into text.
3. Conversational Logic: Using an LLM or NLP engine to process the text, understand intent, access memory, execute tools (like looking up data in a CRM), and formulate a response.
4. Text-to-Speech (TTS): Synthesizing the text response into natural, expressive audio output.

Modern SDKs expertly bundle these components, providing developers with pre-built modules for managing streaming, handling conversational turns, and even retaining context and memory across sessions.

The Hidden Limitation of Most Voice AI SDKs

The popular voice AI SDKs available today are, by design, application-centric. They excel at capturing audio from a device’s microphone, whether in a browser via WebRTC or within a native mobile app and managing the conversational flow within that digital environment.

The problem arises when your business needs this same intelligent agent to answer a phone call. A customer dialing your support or sales number is interacting with the Public Switched Telephone Network (PSTN). This is a completely separate ecosystem governed by complex telephony protocols like SIP, not the web-based protocols that most SDKs understand.

As a result, an SDK designed to be initialized inside your app (npm install @openai/agents) has no native capability to:

• Answer an incoming call from a real phone number.
• Connect to the complex global telephony infrastructure.
• Handle the nuances of call signaling, routing, and session management.
• Manage the packet loss and jitter common in phone networks.

Developers who try to solve this find themselves in a painful position. They must either build their own complex voice infrastructure from scratch, a massive distraction from their core mission, or abandon the goal of deploying their AI on the phone, severely limiting its reach and business impact.

FreJun SDKs: The Bridge Between Your AI and the Real World

This is where FreJun provides the critical, missing layer. We are not an alternative to AI logic SDKs like those from OpenAI or ElevenLabs. Instead, FreJun is the specialized voice infrastructure platform that connects your Voice-Based Conversational AI to the global telephone network.

We provide a suite of developer-first SDKs designed specifically for telephony. Our SDKs handle the messy, low-level complexity of voice transport, allowing you to use your preferred AI SDKs for the conversational logic.

Think of it as two sets of tools for two different jobs:

• AI Logic SDKs (OpenAI, ElevenLabs, etc.): You use these to build the brain of your conversational agent, its personality, its ability to understand, and its access to tools.
• FreJun Infrastructure SDKs: You use these to build the ears and mouth that connect your agent to the telephone network, allowing it to listen and speak on a live call.

By combining these, you get the best of both worlds: world-class AI intelligence and enterprise-grade telephony reach, all without having to become a telecom engineer.

App-Centric SDKs vs. FreJun’s Infrastructure SDKs: A Comparison

Aspect	App-Centric SDKs (e.g., OpenAI Agents SDK)	FreJun Infrastructure SDKs
Primary Use Case	Building voice interfaces inside a web or mobile app.	Connecting any application to the global phone network (PSTN).
Audio Source	Device microphone (via browser or native app).	Live audio stream from an inbound or outbound phone call.
Core Functionality	Manages conversational logic, memory, and tool use.	Manages call control, number provisioning, and real-time audio transport.
Telephony	No native capability to handle phone calls.	Purpose-built to manage SIP, call routing, and concurrent sessions at scale.
Integration Model	Embedded within your application’s client-side or server-side code.	Acts as the transport layer between the phone network and your application’s backend.
Result	A powerful in-app voice feature.	A universally accessible Voice-Based Conversational AI that works over the phone.

Pro Tip: Architect for a Hybrid Approach

The most powerful and flexible architecture for a Voice-Based Conversational AI uses a hybrid SDK approach. Use FreJun’s server-side SDK to receive and send call audio. In your application’s backend, pipe that audio to your chosen AI SDK (like the ElevenLabs Conversational AI platform) to handle the STT, logic, and TTS. This modular design allows you to leverage the best-in-class tool for each part of the problem: FreJun for transport, and another SDK for intelligence.

Architecting Your Telephony Voice AI: A 5-Step Guide

This high-level guide outlines how to use FreJun’s SDKs in tandem with an application-centric AI SDK to build a production-grade voice agent.

Step 1: Install and Configure Both Sets of SDKs

In your backend application (e.g., in Node.js or Python), you will have two primary dependencies:

1. FreJun’s Server-Side SDK: To handle incoming connections from our platform.
2. Your Chosen AI SDK: To power conversational intelligence.

Step 2: Provision a Number and Point it to Your App

Using the FreJun dashboard or API, provision a virtual phone number. Configure this number’s webhook to point to your backend server, where our SDK will be listening for events.

Step 3: Handle the Incoming Call and Audio Stream with FreJun

When a customer calls your number, the FreJun platform answers the call. Our SDK establishes a WebSocket connection to your server and begins streaming the caller’s raw audio to you in real-time. This abstracts away all the underlying telephony complexity.

Step 4: Process the Conversation with Your AI SDK

This is where you hand off to your AI logic. Your code will:

1. Receive the audio stream from the FreJun SDK.
2. Pipe this stream into the ASR module of your chosen AI SDK.
3. Take the transcribed text and pass it to the conversational agent you’ve configured with that SDK (complete with its memory and tools).
4. Receive the text response from the agent and send it to the TTS module.

Step 5: Stream the Response Back to the Caller via FreJun

As your TTS module generates the synthesized voice audio, your code streams it back to the FreJun SDK. Our platform then plays this audio to the caller over the phone call with minimal latency, creating a seamless and natural conversational loop.

Key Takeaway

Modern SDKs have made building the logic for a Voice-Based Conversational AI more accessible than ever. However, this logic is useless for telephony unless it can be connected to the phone network. FreJun’s developer-first SDKs provide this essential infrastructure layer, handling the complex voice transport and call management so you can deploy the intelligent agent you’ve built onto a real phone number, dramatically expanding its reach and value.

Best Practices for a Production-Grade Voice AI

Once the infrastructure is solved by FreJun, you can focus on perfecting the user experience.

• Optimize for Ultra-Low Latency: A natural conversation requires speed. While FreJun provides a low-latency transport, ensure your entire AI pipeline (ASR -> LLM -> TTS) is also fast. Choose providers and models optimized for real-time performance.
• Design for Interruption: Real conversations are not perfectly turn-based. Your system must handle “barge-in,” where a user speaks over the bot. FreJun’s bi-directional streaming enables this, allowing you to stop playback and process the new input instantly.
• Secure Your Sessions: Use the authentication and session management features within your SDKs to secure access and protect sensitive user data. FreJun builds security into every layer of our platform.
• Iterate with Real Data: Use call recordings and transcripts (managed through FreJun) to analyze where your agent is succeeding or failing. Use this feedback to continuously improve its conversational logic, tool usage, and fallback strategies.

Final Thoughts: Your SDK is for Logic, FreJun is for Reach

The proliferation of powerful, easy-to-use AI SDKs is a monumental leap forward for developers. Consequently, it allows any team to build a sophisticated Voice-Based Conversational AI. However, building the logic is only half the battle. Furthermore, for that AI to have a meaningful impact on your business, it needs to be accessible where your customers are, and very often, that is on the other end of a phone line.

Don’t let the limitations of an application-centric SDK constrain your vision. By combining the conversational intelligence of leading AI platforms with the robust telephony infrastructure of FreJun, you create an unbeatable solution. You leverage the best tools for the job, get to market faster, and build a voice agent that is not only smart but also universally reachable.

Let your AI SDK handle the conversation. Let FreJun’s SDKs handle the connection.

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