AI clipping

The pipeline is usually four stages. Speech is transcribed with timestamps, a language model scores passages for whether they work out of context, boundaries are snapped to natural pauses so clips do not start mid sentence, and a reframing pass tracks the speaker so a horizontal source fills a vertical frame. Captions are generated from the same transcript.

It matters because sourcing, not editing, is the bottleneck for most short form output. A single hour long podcast, stream, or webinar contains a handful of self contained moments, and finding them manually means watching the whole thing. Automating selection turns one recording into a week of posts, which is what makes daily posting realistic for solo creators and small teams.

The nuance is that scoring is prediction, not virality. Models identify segments with the shape of a good clip: a question and an answer, a strong opening line, an emotional peak, a complete thought. They cannot know what a specific audience will share, and they miss visual moments that carry no speech at all. Human review of the top candidates still improves results measurably.

In practice the workflow is generate many candidates, keep the ones with a genuine hook, adjust the start point by a second or two, then publish across platforms. Crayo produces the clips as editable projects so those adjustments do not require a re render from scratch.