Frame-accurate highlight extraction and multi-platform export engine for podcast and live-stream creators
Runtime that detects scene changes, speaker transitions, and topic shifts in raw podcast/stream video, extracts frame-accurate clips with preserved audio sync, and auto-exports to platform-native formats (YouTube Shorts, TikTok, Instagram Reels) with correct aspect ratios, captions, and branding.
The problem
Podcast and live-stream creators rely on generic 'AI clipping' tools that fail at basic tasks: missing actual highlights, breaking audio-video sync, producing clips too short or too long, generating unusable aspect ratios for each platform, and requiring manual re-editing. Existing tools are marketed as AI-powered but deliver poor scene detection and no understanding of podcast structure (guest intros, story peaks, jokes, call-to-action moments).
Who has it: Podcast studios and independent podcast creators producing 2+ episodes per week and distributing to YouTube, TikTok, Instagram, and Twitter; studios with 10k-1M monthly listeners and clip-distribution as part of go-to-market.
Why now: Creator adoption of short-form distribution is now mandatory for reach, but clip production remains manual or broken. Multimodal models (vision + audio + speech transcription) are now accurate enough to detect scene intent, not just cuts. Creators are burning out on manual clipping and willing to pay for reliability.
Where this came from
2 public sources behind this idea.
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