Enables AI assistants to watch YouTube videos by extracting frames at scene changes and visual references, pairing each frame with the exact words spoken at that timestamp. Provides dense frame-transcript interleaving for any model.
Transcribes meeting recordings with speaker diarization and automatic speaker identification, leveraging project context to generate minutes. Supports various ASR engines and offers privacy-preserving local processing.
Enables coding agents to watch and analyze videos by extracting scene-aware frames, transcribing speech, and generating shot timelines, all constrained by a token budget to fit LLM context limits.
Turns a YouTube video or allowlisted local video into a timestamped transcript, chronological timeline, and retrievable image resources for transparent media preprocessing.
Extracts ffprobe metadata, subtitles, scenes, and timelines from video files without frame-by-frame LLM vision, providing evidence-first reading for AI agents.
