Rango: AI metadata for teams drowning in creative assets

I led Rango — an AI-powered digital asset management platform for marketing and ops teams drowning in creative files. Legacy DAM systems were either too expensive, too slow, or too manual for teams that needed assets found in seconds, not hours. My mandate was time-to-find: reduce the archaeology of filenames and folders, not build another repository with a search bar.

Enterprise teams stored thousands of images, videos, and documents with inconsistent naming, manual tagging, and search that required remembering exactly what someone called a file six months ago. Creative ops became archaeology. The trap was shipping folder hierarchy depth when users needed semantic retrieval.

I bet on AI-generated metadata as the wedge — automated tagging and semantic search that made assets findable without manual librarian work. Enterprise DAM workflows without the legacy bloat: upload, auto-tag, search, retrieve. Accuracy thresholds mattered. Fast wrong tags are worse than slow right ones — I scoped human review into the loop early.

[NEEDS YOUR INPUT: Add the specific trade-off — e.g., manual tagging vs. AI accuracy thresholds, or which enterprise workflow you shipped first.] Until then: the fight was balancing generative speed with catalog accuracy that enterprise buyers couldn't afford to get wrong. Marketing wanted auto-publish; legal wanted approval gates.

Rango delivered AI-accelerated search, tagging, and retrieval for enterprise marketing and ops teams. Time-to-find dropped from hours of Slack threads to seconds in search — the outcome that justified the platform. Built for teams drowning in creative assets — not another folder tree with a logo.

I'd ship the retrieval loop before the tagging perfection loop. Users forgive imperfect tags if search still saves them time. I'd measure "time to first correct asset" in onboarding, not feature adoption counts.