StemPort vs Scribe

StemPort uses machine learning to identify, label, and organize audio stems before they reach your DAW. You point it at a folder of stem files and it sorts them by instrument, applies consistent naming, and groups like stems together. It is aimed at anyone juggling multitrack exports before a session starts.

Most DAWs leave you to rename and sort stems by hand, which is tedious when a client sends dozens of mislabeled tracks. StemPort handles that sorting step outside the session with patent-pending stem detection, so you drag organized files straight into Pro Tools, Logic, or similar hosts. The trade-off is that it focuses on stem housekeeping, not mixing, editing, or arrangement inside the DAW itself.

The site positions StemPort for mixing engineers, producers, artists, and music labels who receive multitrack deliveries or archive large stem libraries. A public beta is available for download, with the roadmap listing BPM/key detection, color coding, and broader instrument recognition as upcoming features.

Scribe captures how work actually gets done and turns it into step-by-step guides your team and AI agents can follow. It records workflows as people perform them in the browser, desktop, or mobile apps, then generates documentation without manual screenshots or write-ups. Teams use it for SOPs, training, onboarding, and feeding workflow context to AI tools.

Where most documentation tools expect you to write guides from memory or schedule walkthroughs, Scribe watches real workflows and documents them automatically. Capture surfaces the processes teams run most often and turns them into shareable guides, while Optimize maps inefficiencies across approved apps and recommends where to automate or invest in AI.

Operations, IT, HR, and enablement teams rely on Scribe to share tribal knowledge before someone goes on PTO. Companies rolling out new software or building AI agent roadmaps use it to give bots accurate workflow context through MCP and enterprise search APIs.