Treza

Treza reframes AI video production as a workflow rather than a prompt. Each project is a real node graph on a drag-and-drop canvas, where generation, audio, captions, compositing and publishing are separate steps you can wire together, branch and fan out. Because the pipeline is explicit, the same brief can drive several modalities in one run: one node refines a rough idea into a proper shot prompt, the next renders the video, another drafts the caption or burns in subtitles.

Model choice stays open. You pick a model per node from a large library that spans Google Veo, ByteDance Seedance, Kling, Alibaba Wan, Gemini image models, FLUX, Recraft, ElevenLabs speech, Google Lyria music and Whisper transcription, among many others, and you can swap a provider at any step without rebuilding. Anything you do repeatedly can be saved back as a reusable pipeline and put on a schedule, so recurring content runs unattended.

The platform also handles quality and delivery. A vision model samples frames on every render and fails a run when the output does not actually show what was asked for, and failed steps get automatic retries with fallback models. Every pipeline gets a versioned endpoint callable either as a typed invoke or through an OpenAI-compatible chat completions route, so an existing SDK works without changes. Editing happens in a built-in timeline that the assistant can adjust from a plain-language description, and footage can be pulled from any URL, transcribed with word-level timings, reframed to vertical and captioned to turn long recordings into short clips.