RivetAI vs Clips AI

RivetAI is a film and television pre-production platform that keeps scriptwriting, breakdown, scheduling, and budgeting on one shared screenplay. You draft and tag scenes in an industry-standard editor, then downstream modules read the same draft without export handoffs. Writers and production crews buy a per-project license with no subscription, and your script is never used to train AI models.

Most pre-production stacks split script, breakdown, schedule, and budget across separate apps that need constant file exports. RivetAI runs all four on one connected draft, so a scene edit ripples into stripboards and top sheets automatically. Pay-once Writer and Studio licenses also skip recurring SaaS fees that tools like StudioBinder or Movie Magic charge for comparable workflows.

Screenwriters use Writer for tagging, AI breakdown, and coverage while sharing password-gated links with partners. Production teams upgrade to Studio for stripboards, DOOD calendars, budget topsheets, REST API keys, and MCP client integration. Enterprise customers get SAML/OIDC SSO, pooled AI credits, and procurement support for multi-show studios.

Clips AI is an open-source Python library that turns long-form videos into short clips and reframes them for vertical feeds. You install it with pip, transcribe a podcast or interview with WhisperX, then call ClipFinder to segment the transcript into clip start and end times. Developers wire those timestamps into their own pipelines instead of uploading files to a hosted clip editor.

Consumer clip tools hide the segmentation logic behind a web upload form. Clips AI exposes TextTiling with BERT embeddings on Whisper transcripts, plus a separate resize path that tracks the active speaker with Pyannote diarization, PySceneDetect, and face detection. That stack targets narrative audio like podcasts and sermons, not quick meme cuts from silent b-roll.

Engineers building podcast repurposing workflows, media startups, and research teams who need programmatic clip boundaries and 16:9 to 9:16 reframing on their own servers. It fits teams that already run Python and want Hugging Face Pyannote tokens in their resize step rather than a black-box SaaS export button.