every company we’ve spoken to rebuilds similar infrastructure – custom data spec, manual QA scripts, internal labeling pipelines, offline licensing workflows, etc. this is inefficient, prone to errors, and not built for the scale that the leading AI companies are collectively operating at now. we’ve replaced all of that with modular primitives on @psdnai: → sdks for structured collection → ml pipelines for deduplication, PII checks, and outlier detection → semi supervised labeling with active learning and uncertainty routing → IP-cleared via @StoryProtocol