Data Labeling

Enterprises have reached a point of diminishing returns with Model-centric AI. The way forward is to create Data-centric production AI with high-quality, bias-free labeled data.

Tool sprawl in enterprise teams results in loss of productivity, siloed data, threats to data security, and data sprawl. No code workflows and tool centralization are key to countering its effects.

The elimination of bias in AI hinges on the quality of labeled data, expert-in-the-loop solutions, and the geographic diversity of labeling teams