Whisper + LLM Task Extraction: My Meeting Intelligence Architecture

Whisper + LLM Task Extraction: My Meeting Intelligence Architecture Last quarter, our team was drowning in meeting notes. We had 40+ meetings per week across 12 people, and action items were scattered across Slack, email, and Google Docs. Someone would inevitably miss a deadline because a task got buried in a 2000-word transcript. So I built a system that listens to meetings, extracts structured tasks, and routes them to the right people. It's been running in production for 6 months, processing ~200 meetings monthly. Here's exactly how it works. The Problem With Naive Transcription You might think: "Just use Whisper to transcribe, then ask an LLM to extract tasks." That's a starting point, but it fails in practice. The issues: Whisper produces 3000-5000 word transcripts. LLMs struggle to extract precise tasks from walls of text. A 45-minute meeting transcript costs $0.15-0.30 to process with GPT-4. At scale, this adds up. You lose context about who said what and when they committed to