How to Build AI Voice Agents Using InternLM?

The world of conversational AI is undergoing a radical transformation, and at the heart of this revolution is the power of open-source models. For developers, the ability to build AI voice agents using InternLM, the powerful open-weight model series, represents a new era of freedom and control. No longer are you locked into a single proprietary ecosystem. You can now handpick your entire AI stack, creating a truly custom “brain” that is perfectly tailored to your business needs, particularly for advanced reasoning and instruction-following tasks.

This freedom is intoxicating. You can pair the advanced capabilities of InternLM with best-in-class speech recognition and synthesis engines to create a unique and powerful conversational experience. However, after the initial success of building this intelligent core, many teams run into a formidable and often project-killing roadblock. Their brilliant, custom-built creation is trapped, unable to connect to the most critical channel for any real-world business application: the telephone network.

The New Era of AI: The Freedom of Open-Source Models

The rise of powerful, accessible models like InternLM has fundamentally changed the game. When you build AI voice agents using InternLM, you gain a unique set of advantages:

• Ultimate Customization: You can fine-tune InternLM models on your own data, giving them deep, domain-specific knowledge that a generic, off-the-shelf model could never achieve.
• Full Control and Privacy: By hosting the model yourself, you maintain complete control over your data, a critical consideration for businesses in regulated industries.
• Instruction-Following Prowess: Models like InternLM3-8B-Instruct are specifically designed for instruction-following, making them ideal for building agents that can reliably execute multi-step tasks.
• A Best-in-Class Stack: You have the freedom to assemble a “dream team” of components, pairing InternLM’s powerful NLU with other best-of-breed open-source or commercial tools for Automatic Speech Recognition (ASR) and Text-to-Speech (TTS).

This flexibility allows developers to build a truly differentiated AI brain, tailored to their specific needs.

The Hidden Challenge: A Brilliant Bot Trapped in Your Data Center

You have successfully built your custom AI stack. You’ve cloned the GitHub repo, set up your Python environment, and your InternLM-powered agent is intelligent, context-aware, and works perfectly in your development environment. Now, it’s time to put it to work. Your business needs it to handle the customer support hotline, qualify sales leads, or automate appointment booking over the phone.

This is where the project grinds to a halt. The problem is that the entire ecosystem of tools used to build your bot, InternLM, Whisper for ASR, a custom TTS engine, is designed to process data, not to manage live phone calls. To connect your custom-built agent to the Public Switched Telephone Network (PSTN), you would have to build a highly specialized and complex voice infrastructure from scratch. This involves solving a host of non-trivial engineering challenges:

• Telephony Protocols: Managing SIP (Session Initiation Protocol) trunks and carrier relationships.
• Real-Time Media Servers: Building and maintaining dedicated servers to handle raw audio streams from thousands of concurrent calls.
• Call Control and State Management: Architecting a system to manage the entire lifecycle of every call, from ringing and connecting to holding and terminating.
• Network Resilience: Engineering solutions to mitigate the jitter, packet loss, and latency inherent in voice networks that can destroy the quality of a real-time conversation.

Suddenly, your AI project has become a grueling telecom engineering project, pulling your team away from its core mission of building an intelligent and effective bot. The freedom you gained by using an open-source model is lost in the rigid, complex world of telephony.

FreJun: The Voice Infrastructure Layer for Your Custom AI Agent

This is the exact problem FreJun was built to solve. We are not another AI model or a closed ecosystem. We are the specialized voice infrastructure platform that provides the missing layer, allowing you to connect your custom agent to the telephone network with a simple, powerful API. FreJun is the key to letting you build AI voice agents using InternLM that are both powerful and reachable.

We handle all the complexities of telephony, so you can focus on perfecting your unique AI stack.

• We are AI-Agnostic: You bring your own “brain.” FreJun integrates seamlessly with any backend, allowing you to use your custom InternLM, ASR, and TTS stack.
• We Manage the Voice Transport: We handle the phone numbers, the SIP trunks, the global media servers, and the low-latency audio streaming.
• We are Developer-First: Our platform makes a live phone call look like just another WebSocket connection to your application, abstracting away all the underlying telecom complexity.

With FreJun, you can maintain the full freedom and control of a custom AI stack while leveraging the reliability and scalability of an enterprise-grade voice network.

DIY Telephony vs. A FreJun-Powered Agent: A Strategic Comparison

Feature	The Full DIY Approach (Including Telephony)	Your Custom InternLM Stack + FreJun
Infrastructure Management	You build, maintain, and scale your own voice servers, SIP trunks, and network protocols.	Fully managed. FreJun handles all telephony, streaming, and server infrastructure.
Scalability	Extremely difficult and costly to build a globally distributed, high-concurrency system.	Built-in. Our platform elastically scales to handle any number of concurrent calls on demand.
Development Time	Months, or even years, to build a stable, production-ready telephony system.	Weeks. Launch your globally scalable voice bot in a fraction of the time.
Developer Focus	Divided 50/50 between building the AI and wrestling with low-level network engineering.	100% focused on building the best possible conversational experience.
Maintenance & Cost	Massive capital expenditure and ongoing operational costs for servers, bandwidth, and a specialized DevOps team.	Predictable, usage-based pricing with no upfront capital expenditure and zero infrastructure maintenance.

How to Build AI Voice Agents That Can Answer the Phone?

This step-by-step guide outlines the modern, efficient process for taking your custom-built InternLM-powered agent from your local machine to a production-ready telephony deployment.

Step 1: Build Your AI Core

First, assemble your custom AI stack.

• Set up your InternLM Model: Clone the official InternLM repository, set up your Python environment, and download the pretrained model or fine-tune it on your own data.
• Integrate ASR and TTS: Install and configure your chosen speech recognition engine (like Whisper) and text-to-speech engine (like ElevenLabs or Google Cloud Text-to-Speech).
• Orchestrate with a Backend: Write a backend application (e.g., in Python using PyTorch and Hugging Face Transformers) that orchestrates these components. This is where you will manage the conversational logic and context.

Step 2: Provision a Phone Number with FreJun

Instead of negotiating with telecom carriers, simply sign up for FreJun and instantly provision a virtual phone number. This number will be the public-facing identity for your AI agent.

Step 3: Connect Your Backend to the FreJun API

In the FreJun dashboard, configure your new number’s webhook to point to your backend’s API endpoint. This tells our platform where to send live call audio and events. Our server-side SDKs make handling this connection simple.

Step 4: Handle the Real-Time Audio Flow

When a customer dials your FreJun number, our platform answers the call and establishes a real-time audio stream to your backend. Your code will then:

1. Receive the raw audio stream from FreJun.
2. Pipe this audio to your ASR engine to be transcribed.
3. Send the transcribed text to your InternLM model for processing.
4. Take the AI’s text response and send it to your TTS engine for synthesis.
5. Stream the synthesized audio back to the FreJun API, which plays it to the caller with ultra-low latency.

Step 5: Deploy and Monitor Your Solution

Deploy your backend application to a scalable cloud provider, using a GPU-accelerated environment to optimize inference speed. Once live, use monitoring tools to track your bot’s performance, analyze user interactions, and continuously improve its accuracy and effectiveness.

Best Practices for a Flawless Implementation

• Fine-Tune Your Model: For domain-specific applications, fine-tune your InternLM model on your own conversational data. This will dramatically improve its accuracy and relevance for your specific use case.
• Leverage RAG for Accuracy: For use cases that require factual, up-to-date information, integrate a Retrieval-Augmented Generation (RAG) system. This connects your InternLM model to your own knowledge base, reducing hallucinations and ensuring your bot provides trustworthy responses.
• Design for Human Handoff: No AI is perfect. For complex issues, design a clear path to escalate the conversation to a human agent. FreJun’s API can facilitate a seamless live call transfer.
• Secure Your Data: When you build AI voice agents using InternLM on your own infrastructure, you have full control over data privacy. Ensure secure handling of user data and API credentials in compliance with all relevant standards.

Final Thoughts

The freedom to build AI voice agents using InternLM and other powerful open-source models is a revolutionary advantage. It allows you to create a truly unique and differentiated conversational AI experience. But that advantage is lost if your team gets bogged down in the complex, undifferentiated heavy lifting of building and maintaining a global voice infrastructure.

The strategic path forward is to focus your resources where they can create the most value: in the intelligence of your AI, the quality of your conversation design, and the seamless integration with your business logic. Let a specialized platform handle the phone lines.

By partnering with FreJun, you can maintain the full freedom of a custom AI stack while leveraging the reliability, scalability, and speed of an enterprise-grade voice network. You get to build the bot of your dreams, and we make sure it can answer the call.

Try FreJun AI Today!

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