Building a Smart Teams Bot that Creates and Emails AI-Generated Meeting Documents

Introduction

In today's fast-paced business environment, meetings are essential but often leave teams scrambling to document discussions and action items. Many organizations struggle with inconsistent note taking, delayed follow-ups and important details falling through the cracks. This challenge becomes even more pronounced in remote and hybrid work environments where clear communication is paramount. 

In this tutorial, we will build a Microsoft Teams bot that transforms meetings into polished summary documents automatically. The bot captures meeting transcripts, uses Azure OpenAI to generate professionally formatted Word documents with executive summaries, key points, and action items, and emails these documents to participants using Azure Communication Services. While Microsoft does offer a Copilot integration into Teams, this custom built solution would be most effective for custom flows or grounding of specific industry and department use cases. This automation eliminates not just the manual effort of note-taking, but also the time-consuming task of formatting and distributing meeting documentation, ensuring all participants receive consistent, professionally formatted summaries directly in their inboxes. 

We will walk through the entire development process using JavaScript and the Teams Toolkit in Visual Studio Code. You'll learn how to handle meeting transcripts, process them with AI to create structured Word documents, and automatically distribute these professional summaries via email. By the end of this blog, you'll have a production-ready solution that generates and distributes meeting documentation that matches your organization's professional standards.

Architecture

Our solution leverages four key Microsoft Azure services working in harmony to transform meeting content into actionable documentation. The process begins in Microsoft Teams, where our bot listens for meeting end events and captures the transcript through the Microsoft Graph API. This transcript is then passed to Azure OpenAI, specifically the GPT-4o model, which analyzes the meeting content and generates a professionally formatted Word document. The document includes key sections like executive summary, decisions made, and action items. Finally, Azure Communication Services handles the email distribution, delivering the generated document to all meeting participants' inboxes.

This architecture is designed for both efficiency and scalability. By using managed Azure services, we avoid the complexity of handling infrastructure while benefiting from enterprise-grade security and compliance features. The Teams bot serves as the orchestrator, coordinating between services and managing the flow of data. The use of Azure OpenAI ensures reliable document generation with consistent formatting, while Azure Communication Services provides robust email delivery with built-in monitoring and delivery tracking. This combination of services creates a seamless, automated workflow that transforms verbal discussions into professional documentation with minimal latency and maximum reliability.

Here's a flowchart showing the journey from meeting to inbox:

Prerequisites

To follow along this blog, you'll need the following:

• Microsoft 365 Developer account: This provides access to Teams features and APIs and will be used by the Teams Toolkit in VS Code. It gives a sandbox environment with sample users and data to test your Teams bot. If you don't have one, you can sign up here: https://developer.microsoft.com/en-us/microsoft-365/dev-program
• Azure subcription: You will need an active subscription to host the services we will use. Make sure you have appropriate permissions to create and manage resources. If you don't have one, you can sign up here: https://azure.microsoft.com/en-us/pricing/purchase-options/azure-account
• Visual Studio Code: For the development environment, install Visual Studio Code. Other IDEs are available, though we will be benefiting from the Teams Toolkit extension within this IDE. Download VS Code here: https://code.visualstudio.com/
• VS Code Teams Toolkit extension: The Teams Toolkit streamlines the bot development process by handling much of the configuration and development scaffolding. To install on VS Code, head to Extensions > Search Teams Toolkit > Click Install.

Building the solution

Cloning the Teams bot sample

The foundation of our solution starts with the Teams meeting-transcription sample from Microsoft's official repository: https://github.com/officedev/microsoft-teams-samples/tree/main/samples/meetings-transcription/nodejs. This is the basic scaffolding for capturing meeting transcripts, but we'll enhance it with AI summarization and email capabilities.

From a new, empty folder in VS Code, let's clone the Microsoft-Teams-Samples repository. Head to Terminal > New Terminal and run the following command:

git clone https://github.com/OfficeDev/Microsoft-Teams-Samples

This may take a few minutes to complete. Once done, you will have that whole repository from GitHub locally. While we are only after the meetings-transcription sample in this blog, there are many other interesting ones to choose from for future projects.

Now, head to the correct sample by navigating down the folder tree: samples > meeting-transcription > nodejs. For the best experience with the Teams Toolkit, I recommend to open this folder in a new window of VS Code and operate from there.

Let's explore the codebase. It's a standard nodejs project with some files specifically for Teams and some helpers for powering the transcription retrieval:

• There are teamsapp.yml and teamsapp.local.yml files which are configuration files for the Teams Toolkit that defines required Azure resources, environment variables, deployment settings, bot registration details and more.
• There is an appManifest folder which contains essential Teams app configuratrion files, such as the manifest.json which defines details like name, description and permissions, and the .png files which are required app icons for display in the Teams interface.
• The meat of this is found inside the activityBot.js file: it contains the core functionality of our Teams bot. It handles three key events that occur during Teams interactions. The first is a message handler that processes incoming messages from users, currently configured to simply echo back any text it receives. The task module handler manages how meeting transcripts are displayed to users, as when triggered it opens a dialog window to show the transcript content. But the most substantial part is the meeting end handler, which activates when a Teams meeting concludes. It uses the Microsoft Graph API to retrieve the meeting's transcript, stores this data and then presents users with an Adaptive Card - Teams' rich card format - containing a View Transcript button. And if no transcript is found, it gracefully handles this by display a Transcript not found message. 

 

Adding Azure OpenAI integration

For the AI element, we'll use Azure OpenAI, which brings powerful large language models to your Azure environment. It provides enterprise-ready capabilities with the security, regional availability and compliance features you expect from Azure services.

In building this AI-powered meeting summarizer, choosing the right model is crucial. Azure OpenAI offers many models, each with distrinct characteristics. The GPT-4 family represents the most advanced models available (at the time of writing), which would excel at processing extremely long meetings or, for example, all-day workshops. In the GPT-3.5 family, there are smaller context windows and are more limited in their analytical capabilities, but handle linear discussions well, and are often preferred because of their cost.

For advanced comparison, head to Azure AI Foundry > Model catalog > Model benchmarks:

For this bot, I will use GPT-4o, OpenAI's newest and most advanced model. What makes this model particularly exciting for our use case is its native multimodal capabilities - it can handle audio, images and text simulatenously through a single neural network, meaning that there is great expansion capabilities here (for example, processing recordings, or live summaries).

Before we add Azure OpenAI code to our project, we first need to deploy the model and copy the endpoint and key. To deploy a model within Azure AI Foundry, head to Deployments > Deploy model > Deploy base model > Select gpt-4o > Confirm > Create resource and deploy.

After a small wait, the model should be deployed and you will now be exposed to an Endpoint section; copy the values for the Target URI and Key field as we'll need them soon.

Now, head to the codebase and let us setup our environment to work with our deployment. Firstly, we must install the required node packages by running this command:

npm install openai azure/identity

Next, head to the .env file and then add the following variables, changing the placeholders to the values you copied from your deployment:

AZURE_OPENAI_ENDPOINT=your-endpoint
AZURE_OPENAI_KEY=your-api-key
AZURE_OPENAI_DEPLOYMENT=your-gpt4o-deployment-name

Now begins the fun part: adding the code. To power the Azure OpenAI content, it would be best to make a separate class so that we can initialize the client and add some helper functions for sending requests to the API. Based on the project structure that we cloned from the samples, it would make most sense that this class sit in the helpers folder alongside the graphHelper.js.

Create a class called aoaiHelper.js with the following code:

const {
    OpenAIClient,
    AzureKeyCredential
} = require("@azure/openai");
const fs = require('fs');

class OpenAIHelper {
    constructor() {
        this.client = new OpenAIClient(process.env.AZURE_OPENAI_ENDPOINT, new AzureKeyCredential(process.env.AZURE_OPENAI_KEY));
        this.deploymentName = process.env.AZURE_OPENAI_DEPLOYMENT;
    }
}

module.exports = OpenAIHelper;

When the class instance is created, this constructor immediately sets up the essential components we need to communicate with the Azure OpenAI API. First, it initializes a client connection using the OpenAIClient from the SDK, which uses the two pieces of information we provided through environment variables earlier. The constructor also stores the name of our specific model deployment which will be used in the upcoming function.

Now, let us add the following function to that same class:

async generateMeetingSummaryDoc(transcript, meetingDetails) {
    // First, create an Assistant with Code Interpreter
    const assistant = await this.client.beta.assistants.create({
        name: "Meeting Summary Assistant",
        instructions: `You are a professional meeting analyzer that creates Word documents. When given a meeting transcript, create a detailed summary document with:
            1. Executive Summary
            2. Key Discussion Points
            3. Decisions Made
            4. Action Items (with owners and deadlines)
            5. Next Steps
            Format this as a professional Word document with proper styling, headers, and company branding.`,
        model: this.deploymentName,
        tools: [{ type: "code_interpreter" }]
    });

    // Create a Thread
    const thread = await this.client.beta.threads.create();

    // Add the meeting transcript and details as a message
    await this.client.beta.threads.messages.create(thread.id, {
        role: "user",
        content: `Meeting Details:
            Duration: ${meetingDetails.duration} minutes
            Date: ${meetingDetails.date}
            Participants: ${meetingDetails.participants.join(', ')}
            Transcript: ${transcript}`
    });

    // Run the assistant
    const run = await this.client.beta.threads.runs.create(thread.id, {
        assistant_id: assistant.id,
        instructions: `Create a professional Word document summarizing this meeting. Use the python-docx library to create proper formatting and structure.`
    });

    // Wait for completion
    let runStatus = await this.client.beta.threads.runs.retrieve(thread.id, run.id);
    while (runStatus.status !== 'completed') {
        await new Promise(resolve => setTimeout(resolve, 1000));
        runStatus = await this.client.beta.threads.runs.retrieve(thread.id, run.id);
    }

    // Get the messages (including the generated file)
    const messages = await this.client.beta.threads.messages.list(thread.id);

    // Find the message with the generated file
    const fileMessage = messages.data.find(msg => 
        msg.role === 'assistant' && msg.content[0]?.type === 'file'
    );

    if (fileMessage) {
        // Download the file
        const file = await this.client.files.retrieve(fileMessage.content[0].file.id);
        return file;
    }

    throw new Error('No summary document was generated');
}

This function is where the real work happens. This process begins by creating a custom AI assistant equipped with Code Interpreter capabilities, essentially giving it the ability to write and manipulate documents programmatically. We provide this assistant with specific instructions on how to analyze meeting content and structure the output, including sections for executive summary, key points, decisions, and action items.

The process then unfolds through a series of structured steps. First, we create what's called a Thread - think of this as a dedicated conversation space where we can interact with our assistant. Into this thread, we input all the relevant meeting details: duration, date, participant list, and the full transcript. The assistant processes this information using its code interpreter capabilities and the python-docx library to generate a properly formatted Word document. We actively monitor the processing status, waiting for completion, and once finished, we retrieve the generated document file from the assistant's response. This approach ensures we get a consistently formatted, professional document that can be easily distributed to all meeting participants. The entire process happens asynchronously, allowing our bot to handle other tasks while the document is being generated.

But this Azure OpenAI code is useless unless we integrate it into our meeting end event. Inside the onTeamsMeetingEndEvent of activityBot.js, insert the following code after the transcript is retrieved but before the cardJson is created:

...
}

const summaryDoc = await this.openAIHelper.generateMeetingSummaryDoc(
    result,
    {
        duration: meetingDetails.details.scheduledDuration,
        date: new Date().toLocaleDateString(),
        participants: meetingDetails.details.participants || []
    }
);

var cardJson = {
...

Now, when a Teams meeting ends, our bot does more than just fetch the transcript - it sends that transcript text to Azure OpenAI's GPT-4o model for analysis. The OpenAI helper class processes this text and returns a Word document containing a concise, structured summary of the meeting's key points. 

And, as an added bonus for the user experience, let's improve the adapative card that came with the sample to mention that our bot has sent out email summaries:

var cardJson = {
    "$schema": "http://adaptivecards.io/schemas/adaptive-card.json",
    "version": "1.5",
    "type": "AdaptiveCard",
    "body": [
        {
            "type": "TextBlock",
            "text": "Meeting summary document has been generated and will be emailed to all participants.",
            "weight": "Bolder",
            "size": "Large"
        }
    ],
    "actions": [
        {
            "type": "Action.Submit",
            "title": "View Transcript",
            "data": {
                "msteams": {
                    "type": "task/fetch"
                },
                "meetingId": meetingDetails.details.msGraphResourceId
            }
        }
    ]
};

This card serves as a user-friendly confirmation message, displaying a bold text announcement that confirms the summary has been generated and will be emailed out. The card also includes an interactive "View Transcript" button which, when clicked, allows users to access the complete meeting transcript if they need to reference specific details. This adaptive card is rendered directly in the Teams chat, providing immediate feedback to users while maintaining the option to access the full meeting content.

 

Adding Azure Communication Services integration

In our Teams meeting summarization workflow, Azure Communication Services (ACS) provides the crucial final step: delivering the AI-generated summary document to all participants. This service is Microsoft's cloud-based communication platform that enables us to embed enterprise-grade communication features directly into our applications. While ACS supports various communication channels like voice, video, and chat, we're specifically utilizing its email capabilities to distribute our meeting summaries.

After GPT-4o generates our professionally formatted Word document, Azure Communication Services takes over the delivery process. Using an authenticated email sender domain and the ACS SDK, we can programmatically send the summary document to all meeting participants' email addresses. This integration ensures that even team members who couldn't attend the meeting receive a comprehensive summary in their inbox. What makes this particularly powerful is that ACS handles all the complexity of email delivery - from managing attachments and handling different email clients to ensuring deliverability and providing delivery status tracking.

Before we can send emails programmatically, we need to complete several configuration steps in the Azure portal. First, you'll need to create a Communication Services resource. 

Then, the first crucial step is domain authentication. You have two options here: either verify your own custom domain or use an Azure-managed domain. For production environments, using your own domain builds trust with recipients and maintains brand consistency. Choose one option, then navigate to your Communication Services resource and head to Email > Domains > Connect domains and then configure the information. Once you have set a resource group, it will be expecting two things: 

1. An email service: Attach an existing one or create one through the available wizard. This will create an Email Communication Services resource which acts as a bolt-on to this Communication Service resource. It is needed to send emails.
2. Verified domain: This is where you can select a Microsoft managed subdomain you have created or a custom domain that you have attached through DNS.

Once this has been created and hooked to the main communication resource, head into the Email Communication Services Domain resource. From the left pane, head to Email services > MailFrom addresses. Here, you'll create the sender address that will be used for your meeting summaries. Click Add and configure your new MailFrom address - this is what recipients will see in their inbox when they receive meeting summaries. Note that DoNotReply exists by default.

With the sender configured, the next step is to note down two crucial pieces of information from your Azure portal: the connection string from your main Communication Services resource and the sender address you just created. These will be used in our application's environment variables. It's worth mentioning that Azure automatically handles vital email infrastructure concerns like SPF, DKIM, and DMARC records, ensuring high deliverability rates for your meeting summaries.

Like with Azure OpenAI, let's firstly install the ACS SDK node package:

npm install azure/communication-email

Then, let's add the new environment variables to our .env file:

COMMUNICATION_SERVICES_CONNECTION_STRING=your-connection-string
EMAIL_SENDER_ADDRESS=your-verified-sender@domain.com

Now, let us create an email helper class. Similar to before, we will create a JS file in the helpers folder called emailHelper.js with the following code to start with:

const { EmailClient } = require('@azure/communication-email');

class EmailHelper {
   constructor() {
       this.emailClient = new EmailClient(
           process.env.COMMUNICATION_SERVICES_CONNECTION_STRING
       );
       this.senderAddress = process.env.EMAIL_SENDER_ADDRESS;
   }
}

module.exports = EmailHelper;

When this class is instantiated, the constructor sets up two essential elements for email communication. First, it creates an EmailClient using the connection string we obtained from our Azure Communication Services resource. This client handles all the underlying complexity of connecting to Azure's email service, managing authentication, and handling the actual email sending process. Second, it stores the sender address we configured earlier in Azure - this is the email address that participants will see in the "From" field of their meeting summaries. This initialization creates a reusable email client that we'll use throughout our application to send meeting summaries.

Now, we need to add the function which is responsible for creating and sending off the emails:

async sendMeetingSummary(recipients, summaryDoc, meetingDetails) {
   const message = {
       senderAddress: this.senderAddress,
       content: {
           subject: `Meeting Summary: ${meetingDetails.title || 'Team Meeting'}`,
           html: `
               <html>
                   <body style="font-family: Arial, sans-serif; padding: 20px;">
                       <h2>Meeting Summary</h2>
                       <p><strong>Date:</strong> ${meetingDetails.date}</p>
                       <p><strong>Duration:</strong> ${meetingDetails.duration} minutes</p>
                       <p><strong>Participants:</strong> ${meetingDetails.participants.join(', ')}</p>
                       <p>Please find attached the AI-generated summary of today's meeting.</p>
                       <p>You can also view the full transcript in Teams by clicking the "View Transcript" button in the meeting chat.</p>
                   </body>
               </html>
           `,
           plainText: `Meeting Summary\n\nDate: ${meetingDetails.date}\nDuration: ${meetingDetails.duration} minutes\nParticipants: ${meetingDetails.participants.join(', ')}\n\nPlease find attached the AI-generated summary of today's meeting.`
       },
       recipients: {
           to: recipients.map(email => ({ address: email }))
       },
       attachments: [
           {
               name: 'MeetingSummary.docx',
               contentType: 'application/vnd.openxmlformats-officedocument.wordprocessingml.document',
               contentInBase64: summaryDoc.toString('base64')
           }
       ]
   };

   try {
       const poller = await this.emailClient.beginSend(message);
       const result = await poller.pollUntilDone();
       return result;
   } catch (error) {
       console.error('Error sending email:', error);
       throw new Error('Failed to send meeting summary email');
   }
}

This function takes three parameters: the list of recipients who should receive the summary, the Word document we generated with GPT-4o, and the meeting details for context. Let's break down how it constructs and sends the email:

The function first creates a message object that defines every aspect of our email. It starts with the sender address we initialized earlier and builds a professional-looking email structure. The subject line automatically includes the meeting's title (or defaults to 'Team Meeting' if no title is available), helping recipients quickly identify the email's content.

The email body is crafted in two formats: HTML and plain text. The HTML version provides a clean, formatted layout with clear headings, bold text for labels, and proper spacing. It includes key meeting information like the date, duration, and a list of participants, followed by a note about the attached summary. We also include a helpful reminder that recipients can view the full transcript in Teams - this creates a nice connection between the email and the Teams interface. The plain text version contains the same information but formatted for email clients that don't support HTML, ensuring all recipients can read the content regardless of their email client.

For the recipients, the function takes the array of email addresses and maps them into the format required by Azure Communication Services. The Word document generated by our OpenAI helper is attached to the email, properly labeled as a .docx file with the correct content type.

The actual sending process uses Azure Communication Services' polling mechanism to ensure reliable delivery. We initiate the send operation with beginSend and then use pollUntilDone to wait for confirmation that the email has been processed. This approach gives us confidence that our emails are being handled properly by the service. Error handling is implemented to catch and log any issues during the sending process, helping us maintain robust operation and troubleshoot any problems that arise.

Now the final step is to make our bot trigger this. Insert the following code just after where you placed the code in the main activityBot.js flow earlier which created the summary:

try {
   // Get email addresses for participants
   const participantEmails = meetingDetails.details.participants.map(p => p.email);

   // Send the summary document via email
   await this.emailHelper.sendMeetingSummary(
       participantEmails,
       file.content, // The Word document from our OpenAI assistant
       {
           title: meetingDetails.details.title || "Team Meeting",
           duration: meetingDetails.details.scheduledDuration,
           date: new Date().toLocaleDateString(),
           participants: meetingDetails.details.participants.map(p => p.displayName)
       }
   );

   // Now show the card confirming the email was sent
   await context.sendActivity({
       attachments: [CardFactory.adaptiveCard(cardJson)]
   });
} catch (error) {
   console.error('Error sending meeting summary email:', error);
   await context.sendActivity('An error occurred while sending the meeting summary.');
}

Now, once GPT-4o has generated our Word document, we extract the participant email addresses from the meeting details and pass them, along with the document and meeting metadata, to our email helper. The email helper then ensures each participant receives a professionally formatted email containing the Word document summary. We've wrapped this process in error handling to ensure our bot gracefully handles any email delivery issues, maintaining a smooth user experience even if problems arise. This completes our end-to-end workflow: from meeting end, to AI document generation, to email distribution - all happening automatically without any user intervention.

 

Testing the solution

Now that the solution is built and all the parts are hooked up, it's time to test. Before we run the project, I advise logging into your Microsoft 365 Developer account in Teams in your browser first. The following debugging steps will launch your browser and will use your default Teams account, which may not be the developer one. Then, I recommend creating a meeting. The details of this meeting do not matter much, but this is where we will add the bot momentarily.

Now we can run the bot we have made. The Teams Toolkit in Visual Studio Code makes this process straightforward. Press F5 in Visual Studio Code to start the debugging process. This triggers several automated actions:

1. Starts your local Node.js server
2. Creates a tunnel to make your local bot accessible to Teams
3. Registers a temporary bot with Microsoft Teams
4. Sideloads the Teams app into your development Teams client

The tunneling process is particularly interesting. When you're developing locally, your bot needs to be accessible via HTTPS for Teams to communicate with it. The Teams Toolkit automatically creates this tunnel, providing a secure URL that forwards Teams' requests to your local development server.

Once the browser loads, you will be presented with a modal showing the temporary Teams app which pulled data from the manifest.json that can be configured. From there, follow the prompt to add the bot to a meeting, and be sure to select the meeting we just created.

Now we're ready to test the complete workflow. Join the meeting and immediately you should enable meeting transcription. You'll find this option in the meeting's three-dot menu. Without transcription enabled, our bot won't have any content to process. With transcription running, conduct a test conversation. It's helpful to have a structured test script ready - something that includes clear discussion points, decisions, and action items. For example, you might discuss a project timeline, assign tasks to specific team members, and set some deadlines. This gives the AI model meaningful content to work with when generating the summary document.

I used GPT-4o from the playground with our deployment to generate a sample script:

Sarah: Welcome everyone. Today we need to finalize our Q4 digital marketing strategy and assign key responsibilities. We've got a budget of $250,000 to work with, and we need to make sure we're allocating it in the most effective way possible.

James: Thanks Sarah. I've been analyzing our Q3 performance, and what's really interesting is that Google Ads has been delivering a 3.2 ROI, while we're seeing about 2.8 on social media. Based on these numbers, I think we should put about 60% of our budget into search marketing and keep 40% for social.

Emma: That aligns with what I'm seeing in the analytics. Looking at last quarter's data, our peak engagement is consistently between 2 and 5 PM. Our click-through rate is averaging 2.1% on weekdays, but it drops pretty significantly to 0.8% on weekends. We should really think about adjusting our campaign scheduling to match these patterns.

Sarah: Those are good insights. So if we're following James's suggestion, that would put us at $150,000 for search marketing and $100,000 for social. James, how would you recommend splitting that across the different social platforms?

James: Well, looking at where our B2B audience is most active and where we're seeing the strongest conversion rates, I'd say we should put $50,000 into LinkedIn. Then I'd suggest $30,000 for Instagram and $20,000 for Twitter. LinkedIn is really where we're seeing the best business conversion rates.

Emma: If we want to hit our Q4 targets, we need to have these campaigns live by October 15th. That means we need to work backward - we'll need creative briefs by September 30th, then we can get ad copy done by October 5th, and landing pages need to be ready by October 10th.

Sarah: Agreed on those timelines. James, can you take the lead on the search marketing strategy? I'll need a detailed proposal by next Friday. Emma, I want you to own the analytics setup - can you make sure all our tracking is in place by October 1st? I'll review and approve all creative briefs by September 25th.

Emma: Sure thing. For our campaign KPIs, I think we should be aiming for a click-through rate of 2.5%. We need to keep our cost per acquisition under $75, and I'd like to see our return on ad spend at 3.5 minimum.

Sarah: Perfect, this has been really productive. Let's plan our next team check-in for September 23rd. I'll need to present the final strategy to leadership on October 1st, and then we're launching on October 15th. Thanks everyone for your input today.

After generating some good test conversation, end the meeting. This triggers our bot's meeting end handler, and you should see several things happen in sequence. First, the bot will retrieve the meeting transcript through the Graph API. Then it passes this transcript to Azure OpenAI to generate our summary document. Finally, it uses Azure Communication Services to email this document to all participants. Meanwhile, in the Teams chat, you should see our adaptive card appear, confirming that the summary has been generated and emailed.

To verify everything worked correctly, check several key points. First, look in the Teams chat to ensure the adaptive card appeared and contains the correct message. Then check your 365 Developer account email inbox - it should receive a professionally formatted email with the Word document attached. Open the document to verify it contains all the key sections we specified and that the content accurately reflects the meeting discussion.

When developing this solution, keep your debug console open in Visual Studio Code. This helps you track the flow of data through your application and catch any issues that arise. You might see common issues like transcript retrieval delays, or occasional timeout errors from the OpenAI API for longer meetings. These are normal during development and testing, and understanding them helps build a more robust production solution.

Summary

That's it! You should now have a locally available Teams bot solution which can transform the way your organization handles meeting documentation and follow-up. By automating transcript capture, generating AI-powered summary documents and distributing them via email, we have eliminated manual note taking and streamlined post-meeting communication.

We built a Teams bot which will leverage multiple key Azure services: Azure OpenAI for intelligent summarisation and Azure Communication Services for email distribution, as well as the Graph API for transcript retrieval.

If you are still motivated on the topic, future enhancements could include:

• Custom summary formats for different meeting types
• Integration with task management systems
• Real-time meeting insights
• Multi-language support
• Analytics on meeting patterns

Thanks for following along with this blog. I am happy to help if you have questions, and am interested in hearing how you customise this solution for your specific needs. Good luck!