Podcast Monitoring Tool
An automated system for monitoring podcasts, generating editorial pitches, and eliminating manual transcription work.
Problem
Editorial teams were spending full working days manually listening to podcasts, transcribing episodes, and identifying potential story angles. This process was slow, expensive, and fundamentally unscalable. Content output was thereby limited and delaying time-to-publish.
Solution
Build an end-to-end automated podcast monitoring system that replaces manual listening and transcription, transforming new episodes into editor-ready pitches and headlines via an n8n-orchestrated workflow.
How it works
New podcast episodes are automatically detected via RSS feeds or webhooks as soon as they're published.
An n8n-orchestrated workflow schedules processing, routes tasks, and handles retries without manual oversight.
Audio is transcribed via an external transcription API and passed to an LLM for summarization and pitch extraction.
The system generates structured editorial outputs, including story angles, headlines, and notes.
Final outputs are written directly into Airtable, where editors can immediately review the content.
Outcome
The system eliminated the need for manual podcast review, saving 50+ hours per week and $5000 per month in contracting costs. By dramatically increasing editorial throughput, this automation unlocked additional revenue through faster turnaround times and higher content volume.