If you’ve ever spent hours cleaning datasets, fixing inconsistent annotations, or retraining models because your labels were off, you already know this: data labeling is where AI projects either take off or quietly fail. I talk to data scientists, ML engineers, and AI product teams all the time, and the frustration is consistent: labeling is slow, expensive, and painfully manual when it shouldn’t be. The bottleneck isn’t the model architecture anymore; it’s the ground truth. When I talk to
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