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Customer support is vital but often bogged down by repetitive queries like password resets, billing issues, and basic troubleshooting. Automating these common responses frees up valuable time, allowing your team to focus on more complex tasks.

In this tutorial, we’ll build an AI-Powered Email Responder that leverages OpenAI’s GPT models to craft intelligent replies and Azure Communication Services to handle email delivery seamlessly.

How It Works

Here’s how our AI-powered responder works:

1. Input Query: Enter the recipient’s email, subject, and customer query.


2. Generate Response: GPT quickly drafts a detailed reply.


3. Review & Edit: You can adjust the response as needed.


4. Send Email: Azure Communication Services dispatches the message instantly.

Prefer a walkthrough? Watch the full video demo to see the app in action with live code explanations.

Tools We’re Using

This project is ideal for developers, solution architects, or anyone exploring practical AI integrations with minimal setup.

We will utilize the following services:

• Azure OpenAI Service: Provides state-of-the-art GPT models capable of intelligent text generation and conversational responses.


• Azure Communication Services: Delivers reliable, scalable, and secure email capabilities suitable for enterprise-level applications.

Azure Environment Setup

Follow these steps to configure your Azure environment:

1. Azure OpenAI Service

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  Go to Azure Portal and create an “Azure OpenAI” resource (see how: docs).

  
  


• 
  

  In Azure AI Studio, deploy your selected GPT model (such as GPT-4o).

  
  


• Retrieve your API Key, Endpoint URL, and Deployment ID.

2. Azure Communication Services

• In Azure Portal, create “Communication Services” and “Email Communication Services” resources (see how: docs or video).


• Provision a free Azure subdomain and obtain your verified sender email address.


• Copy your Connection String from your Communication Services resource for later use.

Coding the Application

You can view the full project on GitHub to access the complete, ready-to-use code, and skip the manual steps — or follow the highlights below to understand how everything works. 

Create two files; an .env file and app.py file in your preferred IDE.

3. Environment Variables (.env file):

Replace OpenAI and Azure Communication Services configurations with your credentials obtained in the above steps. Store sensitive credentials securely:

# OpenAI Configuration
OPENAI_API_KEY=your_azure_openai_key
OPENAI_ENDPOINT=https://your-openai-resource.openai.azure.com/

# Azure Communication Services Configuration
ACS_CONNECTION_STRING=endpoint=https://your-resource.communication.azure.com/;accesskey=your_key
ACS_SENDER_EMAIL=DoNotReply@your-resource.communication.azure.com
RECIPIENT_EMAIL=someone@example.com

4. Python Application (app.py):

The app.py file provided uses Streamlit to create an intuitive and interactive user interface. Users input recipient details and customer queries directly through the web-based form. Behind the scenes, the app makes API calls to Azure OpenAI, sending the customer’s query to generate a contextually relevant reply using the GPT model. It also leverages Azure Communication Services API to dispatch the finalized email instantly to the specified recipient. You can access the full detailed code and in-depth explanations in the GitHub repository.

Below is the breakdown of the app’s core functionality, highlighting how each user action maps to backend logic for generating, editing, and sending AI-crafted responses.

🧠 Generate Response

We send the user’s query to Azure OpenAI with a prompt like:

“You are a support agent. Respond only with the email body. Be concise, helpful, and use numbered steps if needed.”

💬 Preview and Edit

The AI-generated response is displayed in a text area, allowing human review and manual editing.

✉️ Send via Azure Communication Services Email

We use the EmailClient from the azure.communication.email package to send the final message.

Ideas for Extension

The app provides a strong foundation for exploring AI automation using large language models and email APIs, and it can easily be extended with additional capabilities such as:

• RAG (Retrieval-Augmented Generation): Integrate with internal knowledge bases for more tailored answers.


• Ticketing Integration: Connect directly with your existing support or ticketing system to fetch queries and update responses automatically.


• Role-based Prompts: Fine-tune the AI’s tone based on use case (e.g., sales vs. tech support).


• Analytics & Logs: Track usage and monitor AI-generated replies for compliance and quality.

Final Thoughts

This project shows how easy it is to bring real AI capabilities into customer workflows using Azure. You get powerful language understanding via OpenAI and production-grade email delivery through Azure Communication Services—all in a minimal Python app.

Whether you’re prototyping a support bot or building internal tools, this project offers a practical entry point into real-world AI automation. Get started by exploring the GitHub repository.