Data annotation is the process of labeling raw data such as images, text, or audio so that machine learning models can learn from it. The quality of your annotated training data directly determines the performance of your AI model.

Services include text classification, named entity recognition, sentiment labeling, image bounding box annotation, segmentation, audio transcription and labeling, and video annotation for object detection and tracking.

Use a clear annotation guideline document, run inter-annotator agreement checks on a sample of the data, and review annotated batches before they go into training. Experienced annotators on BotPool can flag ambiguous cases rather than guessing, which improves overall data quality.