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

Widespread AI adoption coupled with renewed demand for HITL training has created a lucrative opportunity for countries like India to leverage Human Capital.

The Current State of Internal Tooling Internal tools are critical to the optimal productivity of a modern business. From CRUD…

From modest beginnings around the turn of the millennium, global retail eCommerce sales had reached USD 4.28 trillion by…