Labeling isn't generic data entry, even though it sometimes gets treated that way. Good labelers understand edge cases, follow annotation schemas precisely, and flag ambiguous data instead of guessing. A single inconsistent labeler can quietly degrade an entire training dataset, which is a much bigger problem than a late invoice or a missed deadline. https://gigmint.ai./