How to Setup Azure OpenAI Service with Google Meet (2026 Guide)

Why Use Google Meet with Azure OpenAI Service

Google Meet is a widely used video conferencing platform integrated into Google Workspace, serving millions of organizations for team meetings, client calls, webinars, and remote collaboration. While Google Meet handles the communication itself effectively, the valuable information exchanged during meetings — decisions, action items, insights, and commitments — often gets lost or requires significant manual effort to capture and organize.

Azure OpenAI Service provides enterprise-grade access to GPT-4 and Whisper (speech-to-text) models through Microsoft Azure. By connecting Azure OpenAI to your Google Meet workflow, you can automatically transcribe meeting recordings, generate structured summaries with action items, extract key decisions, and create follow-up documentation — turning every meeting into an organized, searchable knowledge asset.

This combination is valuable for any organization that runs frequent meetings and struggles with note-taking, follow-up accountability, and information retrieval from past discussions. Instead of relying on manual notes or memory, you get AI-processed meeting intelligence delivered automatically after every call.

What You Can Do

• Meeting Transcription: Use Azure OpenAI's Whisper model to transcribe Google Meet recordings with high accuracy, including speaker identification.
• Structured Summaries: Generate organized meeting summaries with sections for key decisions, action items, discussion topics, and follow-up deadlines.
• Action Item Extraction: Automatically identify and list all commitments, tasks, and next steps mentioned during a meeting, attributed to specific participants.
• Meeting Q&A: After processing a meeting transcript, query it with natural language questions like "What did the team decide about the timeline?" and get specific answers.
• Follow-Up Email Drafting: Generate professional follow-up emails summarizing meeting outcomes and assigned responsibilities, ready to send to attendees.
• Cross-Meeting Analysis: Analyze transcripts from multiple meetings to track project progress, identify recurring blockers, and surface trends across a series of discussions.

Prerequisites

• Google Workspace account with Google Meet (meeting recording requires Business Standard plan or higher)
• Google Meet recording enabled by your Workspace admin
• Access to Google Drive where Meet recordings are stored
• An active Microsoft Azure subscription
• Azure OpenAI Service resource with Whisper (for transcription) and GPT-4 (for analysis) models deployed
• API key and endpoint URL from your Azure OpenAI resource
• Google Drive API access for programmatic retrieval of recordings (or manual download)

Step-by-Step Setup Guide

Step 1: Set Up Azure OpenAI Service with Required Models

In the Azure Portal, create an Azure OpenAI resource. Complete the access approval process and provision the resource. In Azure OpenAI Studio, create two model deployments: deploy Whisper for audio transcription (needed to convert meeting recordings to text) and deploy GPT-4 for transcript analysis and summary generation. Note your endpoint URL, API key, and both deployment names.

Step 2: Enable and Access Google Meet Recordings

In the Google Workspace Admin Console, ensure meeting recording is enabled for your organization. When a meeting is recorded in Google Meet, the recording (MP4 file) is saved to the organizer's Google Drive in a "Meet Recordings" folder. You can access recordings manually through Google Drive or programmatically using the Google Drive API. Enable the Drive API in Google Cloud Console and set up a service account with access to the relevant Drive folders.

Step 3: Transcribe the Recording with Whisper

Download or stream the meeting recording file from Google Drive. Send the audio to Azure OpenAI's Whisper endpoint at https://{resource}.openai.azure.com/openai/deployments/{whisper-deployment}/audio/transcriptions?api-version=2024-02-01. Submit the file as a multipart form upload with the api-key header. Whisper returns a text transcription of the entire recording. For long meetings, you may need to split the audio into segments under 25 MB (Whisper's file size limit) and transcribe each segment separately, then concatenate the results.

Step 4: Process the Transcript with GPT-4

Send the full transcript to your GPT-4 deployment's chat completions endpoint. In the system message, instruct GPT-4 to act as a professional meeting analyst and specify the output format you want: meeting title, date, attendees (extracted from the conversation), executive summary (2-3 sentences), key discussion points (bulleted list), decisions made, action items (with owner and deadline if mentioned), and open questions. Include the transcript in the user message. For very long transcripts that exceed the context window, split the transcript into sections and process each separately, then send a final request to consolidate the section summaries.

Step 5: Automate the Pipeline

Create an automated workflow using Google Apps Script, Python, or an automation platform. Set up a script that monitors the Google Drive "Meet Recordings" folder for new files (using the Drive API's changes endpoint or a periodic check). When a new recording appears, the automation downloads the file, sends it to Whisper for transcription, sends the transcript to GPT-4 for analysis, and saves the resulting summary. Store summaries in a Google Doc, a shared Drive folder, or a database for easy retrieval.

Step 6: Distribute Summaries and Follow-Ups

After generating the meeting summary, automatically distribute it to attendees. Use the Gmail API or Google Apps Script's MailApp to send an email with the summary, action items, and a link to the full transcript. Alternatively, post the summary to a Google Chat space or Slack channel where the team communicates. You can also have GPT-4 generate a follow-up email draft that the meeting organizer can review and send, ensuring no action items are missed.

Practical Examples

• Weekly Team Standup: Every team standup recorded in Google Meet is automatically transcribed and summarized. The summary includes what each team member reported, any blockers mentioned, and follow-up items — posted to the team's project channel within an hour of the meeting ending.
• Client Discovery Calls: Sales teams record client calls in Google Meet. Azure OpenAI processes the transcript to extract client requirements, objections, budget indicators, and timeline expectations, producing a structured brief that the sales rep can immediately add to the CRM.
• All-Hands Meeting Digest: Company-wide meetings are recorded and processed into a concise digest with key announcements, policy changes, and Q&A highlights. The digest is distributed to all employees, including those who could not attend live, ensuring consistent information sharing.
• Project Retrospective Analysis: After a project wraps up, feed all related meeting transcripts from the past quarter to Azure OpenAI. GPT-4 analyzes the full arc of discussions to identify when key decisions were made, how requirements evolved, and what recurring challenges the team faced — generating a comprehensive retrospective report.

Tips and Troubleshooting

• Google Meet recordings are in MP4 format — Whisper handles this format natively, but for best transcription quality, extract the audio track (using ffmpeg or a similar tool) to reduce file size and processing time.
• For meetings longer than 60 minutes, split the audio into 20-minute segments before sending to Whisper to stay within file size limits and improve transcription accuracy.
• If Whisper struggles with speaker identification, add a post-processing step where GPT-4 attempts to assign speakers based on conversation context, name mentions, and speaking patterns.
• Always send meeting context (agenda, attendee list, project name) in the GPT-4 system prompt alongside the transcript to improve the quality of summaries and action item extraction.
• Store transcripts and summaries with consistent naming conventions and metadata so they become a searchable meeting knowledge base over time.
• Be mindful of privacy — ensure all meeting participants are aware that recordings will be AI-processed, and comply with your organization's data handling policies and any applicable regulations regarding recorded conversations.