Aymen Furter introduces a robust blueprint for a voice-powered AI Sales Coach using Azure’s new Voice Live API, explaining the technical architecture and how developers can implement a real-time, avatar-driven training solution.

Building a Real-Time Voice-Powered AI Sales Coach Using Azure Voice Live API

By Aymen Furter

Developers seeking to build sophisticated, real-time voice and avatar AI solutions have historically faced hurdles with audio management and latency. With the general availability of the Azure Voice Live API, creating such experiences has become far more accessible.

Project Overview: AI Sales Coach

This project demonstrates a reference implementation for a practical business use case: sales training. The AI Sales Coach app lets users select training scenarios and have realistic voice conversations with an AI-powered virtual customer, represented by a lifelike avatar. After each simulation, users receive an automated performance analysis.

Key Features

  • Real-Time Speech: True voice-to-voice interaction with AI
  • Avatar Integration: Customizable characters and presence
  • Session Configuration: Backend scripts define text/audio modalities, voice, avatar details, and advanced audio processing (noise reduction, echo cancellation)
  • Dynamic Agent Behavior: Swap models (e.g. GPT-4o, GPT-5), tune creativity (temperature), and provide prompt-based role instructions
  • Conversational Realism: Instructions ensure the AI acts genuinely and stays in character
  • Post-Session Feedback: A large language model analyzes the full transcript and generates a scorecard for user reflection

Sample Implementation Details

Session Initialization Example:

def _build_session_config(self) -> Dict[str, Any]:
    return {
        "type": "session.update",
        "session": {
            "modalities": ["text", "audio"],
            "turn_detection": {"type": "azure_semantic_vad"},
            "input_audio_noise_reduction": {"type": "azure_deep_noise_suppression"},
            "input_audio_echo_cancellation": {"type": "server_echo_cancellation"},
            "avatar": {
                "character": "lisa",
                "style": "casual-sitting",
            },
            "voice": {
                "name": config["azure_voice_name"],
                "type": config["azure_voice_type"],
            },
        },
    }

Agent Behavior Configuration:

def _add_local_agent_config(self, config_message: Dict[str, Any], agent_config: Dict[str, Any]) -> None:
    session = config_message["session"]
    session["model"] = agent_config.get("model", config["model_deployment_name"])
    session["instructions"] = agent_config["instructions"]
    session["temperature"] = agent_config["temperature"]
    session["max_response_output_tokens"] = agent_config["max_tokens"]

Role-Playing Instruction Template:

CRITICAL INTERACTION GUIDELINES:
- Keep responses SHORT and conversational (3 sentences max, as if speaking on phone)
- ALWAYS stay in character, never break role
- Simulate natural human speech patterns with pauses, um, well, occasional hesitation
- Respond as a real person would in this business context
- Use natural phone conversation style, direct but personable
- Show genuine emotions and reactions appropriate to the situation
- Ask follow-up questions to keep the conversation flowing naturally
- Avoid overly formal or robotic language

Architecture Highlights

  • Azure Voice Live API serves as the real-time audio and avatar abstraction layer.
  • Modular backend allows model/version switching without changing frontend logic.
  • LLM-generated feedback (“LLM-as-judge” pattern) offers a detailed scorecard after each session.
  • Designed for easy deployment using Azure Developer CLI (azd up).

Design Considerations

  • Avatar Manifestation: Embedding a face makes role-play more effective in sales contexts. In other scenarios, avatars may not be suitable.
  • Feedback System: Transcripts are processed by GPT-4o to evaluate user performance and offer actionable advice.

Resources

Conclusion

The new Azure Voice Live API removes key technical barriers, letting developers create robust, voice-first AI experiences that are ready for production. This project by Aymen Furter offers a clear template (with source code) for real-time AI coaching applications and beyond.

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