Annotation is the act of marking up raw data with structured detail an AI system can learn from: drawing boxes around objects in an image, highlighting names and dates in a document, or tagging the parts of speech in a sentence. It is closely related to data labeling, with annotation usually referring to the richer, in-context markup.

It matters because models need examples that point precisely at what to learn. The shape and consistency of annotations directly determine what the model can do and how reliably it does it, which is why clear schemas and quality checks are essential.

At arosplatforms we design annotation schemas to match the real decision a system has to make, then build review steps so the markup stays consistent across people and over time. Good annotation is quiet, unglamorous work that pays off in every downstream prediction.