FAQ
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Data Labeling
When Should a Company Outsource Data Annotation?
A company should outsource data annotation when data labelling volume becomes too large, too continuous or too quality-sensitive to manage comfortably with an in-house team. This usually happens when dataset volumes increase, model timelines tighten, or internal product and machine learning (ML) teams start spending too much time coordinating labellers instead of building the model itself.
Outsourcing also makes sense when a company needs trained annotators quickly, wants access to subject matter experts, or expects temporary volume spikes that don't justify building a permanent internal department.
At BUNCH, we're set up for both short-term and recurring data volumes, and can mobilize teams within days while keeping double-pass annotation and QA in place. This gives companies a way to scale training data operations without losing control of quality.
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