Data Flywheel
Label, augment, and curate training data. Feedback loops that improve over time.
/ Overview
Data Flywheel turns production feedback into better training data. Label, augment, and curate with human-in-the-loop and model-assisted workflows. Version your datasets and close the loop from inference back to training.
/ The_Problem
The best models improve with feedback—but getting production data labeled, curated, and back into training is messy. Data Flywheel closes the loop: collect, label, augment, version, and feed the next training run without building pipelines from scratch.
/ Close_the_loop
Production logs and user feedback flow into labeling and curation. New dataset versions trigger retraining; we track which model was trained on which data so the flywheel is traceable end to end.
/ Use_Cases
Human-in-the-loop
Send uncertain predictions to labeling queues, get corrections, and merge back into versioned datasets. Quality controls and SLA tracking built in.
Synthetic and augmentation
Generate synthetic examples and augment existing data. Version everything and track which data trained which model.
“We went from ad-hoc labeling in spreadsheets to a full flywheel in a few weeks. Our model quality improved faster than we expected.”
/ Roadmap
Labeling UI
Tasks, queues, quality
Augmentation
Synthetic, filters
Feedback ingestion
Production → dataset