Every annotation vendor advertises accuracy. Very few can explain how they measure it. At Zuvintel, 99.9% is not a slogan — it is the output of a pipeline engineered to catch errors before they reach a client dataset.
Gold sets before anything else
Every project starts with a calibrated gold set: a sample annotated independently by senior specialists and reconciled through adjudication. Annotators qualify against the gold set before touching production data, and it remains the yardstick for continuous scoring throughout the engagement.
Consensus where ambiguity lives
Not every label has one right answer. Occlusion boundaries, sentiment edge cases, and clinical findings all carry genuine ambiguity. For those classes we route items to multiple annotators and measure inter-annotator agreement. Low-agreement items are escalated to domain reviewers rather than averaged away.
Automated validation as a first filter
Geometry checks, class-distribution monitors, and temporal-consistency rules run on every batch. Automation never approves work — it only rejects the mechanically impossible, so human review time concentrates on judgement calls.
The feedback loop
Error taxonomies feed back into annotator training weekly. Accuracy is not a static audit result; it is a moving control system. That is the difference between promising a number and engineering one.
