LabelLatch

LabelLatch is a web app (with optional desktop agent) for managing human-in-the-loop data labeling specifically for model training teams that keep getting burned by inconsistent labels, unclear guidelines, and low auditability. It centralizes labeling instructions, version-controls them, and automatically measures annotator agreement, confusion hotspots, and label drift across dataset versions. Instead of trying to be a full training platform, it focuses on the unglamorous bottleneck: making training data reliable and repeatable. You can run small internal labeling teams or plug in external vendors, then review work with targeted sampling rather than endless manual QA. It exports clean, traceable datasets to your existing stack (S3/GCS, Hugging Face Datasets, or your data lake) with provenance metadata so you can reproduce training runs later. This is an AI app + traditional app combo: analytics are AI-assisted, but the core value is workflow and governance.