Segment Suggester

Production teams waste hours watching down long videos to identify segment points, manually create cuts, and ensuring consistent naming conventions. This bottleneck delays delivery to clients and ties up editors in repetitive work.

Built AI-powered web app simple enough to be used by producers without editing experience that analyzes video content through transcription of the audio, identifying natural segments, and exports H.264 web-ready files for delivery. It uses AI analysis of transcripts to identify natural break points based on content, speaker changes, and topics, and is able to incorporate an agenda or run of show for proper naming conventions. The tool runs locally to avoid time-consuming upload of full-quality source files.

In real-world use, reduced segmentation time for a 4 hour video from 40 minutes to 3 minutes.

Integrated transcript API for accurate and speaker-aware transcription, Gemini AI for content understanding, and FFmpeg for video processing. Most crucially, created an intuitive interface where users can review AI suggestions, make adjustments, and batch export segments with proper naming. This elevates the tool from a simple copy-paste chat to an LLM into a flexible editing workspace that allows users to quickly adjust and refine the AI output into something client-ready.

Accelerated post-production workflows and freed editors to focus on creative work rather than mechanical segmentation, while enabling faster client delivery.