Scaling an annotation team is not a hiring problem — it is a curriculum problem. Anyone can add seats; keeping accuracy flat while volume triples is the hard part.
Pods, not pools
Our specialists work in per-modality pods — medical imaging, LiDAR, NLP, retail imagery — each led by a reviewer who owns the pod's quality metrics. Generalist pools look flexible on paper and underperform on every domain-specific task we have measured.
A four-week onboarding curriculum
New specialists spend their first month on gold-set replicas with daily calibration reviews. Nobody annotates production data until their agreement scores stabilise above threshold for five consecutive sessions.
Career ladders keep quality in-house
Annotation is skilled work, and treating it as such is a retention strategy. Reviewers and adjudicators are promoted from the floor, which means our QA layer is staffed by people who know exactly where errors hide.
The result: throughput has tripled since 2023 while our client-audited error rate has stayed flat. That is what scaling on purpose looks like.
