Podcast Production Automation

‍Challenge

‍A media company was limited to producing 2 podcast episodes weekly due to production constraints. Their manual editing process was time-intensive, requiring careful removal of verbal fillers, awkward pauses, and mistakes, along with balancing audio levels and adding intro/outro elements, creating a significant bottleneck in content production.

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Solution

An end-to-end AI audio solution for transcription, editing, and voice generation using Descript, Murf AI, and Make.com that:

• Automatically transcribed raw interview recordings with high accuracy
• Identified and removed filler words, long pauses, and repetitions
• Detected and corrected audio quality issues and level imbalances
• Generated consistent intros, outros, and segment transitions
• Created show notes and promotional clips for social media
• Enabled text-based editing of audio content for efficiency

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Implementation

‍The system was built over 5 weeks:

1. Audio processing workflow development for consistent quality
2. Creation of editing templates and rules for different podcast formats
3. Training of transcription models on domain-specific terminology
4. Implementation of promotional content generation workflows
5. Integration with podcast distribution platforms

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Results

• Production time per episode reduced from 14 hours to 4 hours
• 1.3 FTE saved in audio editing and post-production
• Output increased to 7 episodes weekly with the same staff
• 22% improvement in audio quality consistency
• 35% increase in listener engagement due to more regular publishing schedule

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Content Leverage

The system's ability to easily generate promotional clips and highlights significantly increased cross-platform promotion effectiveness.

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