Kili measures quality through four distinct mechanisms: Honeypot testing (ground-truth assets embedded in the queue to measure individual accuracy), Consensus scoring (inter-annotator agreement), Review scores (original label vs. reviewed version), and Human-model IoU (annotations vs. model predictions). All four are computed per annotator, per asset, and per project.

When reviewers find a problem, they flag the specific annotation — not just the whole asset — with context on what went wrong. Labelers see open issues when they return to the asset and can correct in place. For edge cases that need discussion, the conversation happens inside the platform, attached to the object in question. No email chains, no spreadsheet trackers, no ambiguity about which annotation in which project someone means. When your team is distributed across time zones, contextual communication like this is what keeps the operation running.