Building an AI-Powered Meeting Summary System (Complete Code + Architecture)

Meeting summaries were eating 30+ minutes per meeting. I automated the entire process — from recording to action item creation. Architecture Zoom/Meet Recording ↓ Whisper (transcription) ↓ Claude API (analysis) ↓ ┌─────┼─────┐ ↓ ↓ ↓ Email Slack Asana (summary) (alert) (tasks) Step 1: Transcription import whisper import os def transcribe_meeting ( audio_path ): model = whisper . load_model ( " base " ) result = model . transcribe ( audio_path ) # Save transcript transcript_path = audio_path . replace ( ' .mp3 ' , ' _transcript.txt ' ) with open ( transcript_path , ' w ' ) as f : f . write ( result [ ' text ' ]) return result [ ' text ' ] For production, I use Whisper API or Otter.ai — faster and handles longer recordings. Step 2: AI Analysis import anthropic def analyze_meeting ( transcript , meeting_context = None ): client = anthropic . Anthropic () context = f " Meeting context: { meeting_context } " if meeting_context else "" response = client . messages . create ( model = " claude-