Dropbox smart move: ML-assisted file organization with human-in-the-loop review
Dropbox users, especially team managers and company administrators, had to move files one at a time on the web, making large-scale folder organization tedious and daunting. File organization is highly personal and varies widely across users, making automation difficult to scope.
An initial prototype repurposed the existing 'suggested destinations' model, but internal testers found its results non-deterministic—changing based on recent user navigation rather than file/folder name relationships—and the model did not meet expectations for how suggestions should relate to filenames.
Smart move launched in November 2021.
In online alpha testing, 61% of heuristic suggestions were accepted overall and 94% of high-confidence heuristic suggestions were accepted. The trained model reached 73% offline accuracy versus 64% for the heuristic baseline, and the model was reused for additional Dropbox feature prototypes.
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Frequently asked questions
What did this team achieve with this AI workflow?
Smart move launched in November 2021.
What tools did this team use?
GloVe.
What results were reported?
Trained model offline accuracy: 73%; Similarity heuristic offline accuracy: 64%; Evaluation dataset size: 57,921 files; Heuristic suggestion acceptance rate (online alpha, overall): 61% (source-reported, not independently verified).
What failed first in this deployment?
An initial prototype repurposed the existing 'suggested destinations' model, but internal testers found its results non-deterministic—changing based on recent user navigation rather than file/folder name relationships…
How is this back office ops AI workflow structured?
User triggers smart move → Encode filenames and folders → Score and rank folder candidates → Confidence-tier filtering → Human reviews suggestions → Files moved in one click.