We sign NDA contracts with our specialists and generalists, and we keep remininding them about this serious policy

At Wakmate AI, we combat Annotator Drift using "Gold-Standard Insertion." We periodically inject "control" data (pre-verified samples) into our workers' queues without their knowledge. If an annotator's performance on these control samples dips below the threshold, they are automatically flagged for recalibration. This ensures that the "Helpfulness" and "Safety" standards we set on Day 1 are identical to those on Day 100.

Absolutely. We don't just act as a "passive" labeling force. Through our Active Discovery Workflow, our senior annotators are trained to tag "Edge-Case Ambiguity." If our team encounters a data point where the current guidelines are insufficient or the model's output is strangely inconsistent, we flag it as a "Boundary Case." We then provide you with a weekly Strategic Edge-Case Report, helping your research team identify where the model's reasoning is brittle, which often informs the next iteration of your architecture or training set.

We are always here and ready to respond to any issues that you may have pertaining to your project quality and solve it as soon as possible. Just reach us via any of the available channels that is convenient for you and we will sort the issue immediately

You can reach us via any of our contacts or chat and we will discuss your project.