AI Training Data

Data Annotation Services

Enterprise data annotation services for AI and machine learning , image, text, video, and audio annotation with expert human annotators across 80+ languages.

Data annotation is the process of labeling raw data to make it usable for machine learning. It is the step that transforms images, audio, text, and video into the structured training signal that AI models learn from. Appen has been providing data annotation services for 30 years, across every data type and annotation task that AI development requires.

From image classification and object detection through text sentiment and intent labeling to speech transcription and video action recognition, our annotation programmes are built on the quality infrastructure that enterprise AI development demands: calibrated contributors, rigorous review processes, and measurement systems that verify label consistency before data enters your training pipeline.

Annotation Services by Data Type

Text and NLP Annotation

Intent classification, entity recognition, sentiment labeling, relevance rating, and LLM evaluation across instruction-following, question-answering, and preference ranking tasks. Text annotation underpins every language model training and evaluation programme.

Image and Video Annotation

Bounding box labeling, instance segmentation, keypoint annotation, action classification, and video action recognition across consumer, industrial, medical, and autonomous driving imagery.

Speech and Audio Annotation

Verbatim transcription, speaker diarisation, emotion labeling, acoustic scene classification, and paralinguistic event annotation across 100+ languages and 500 locales.

Multimodal Annotation

Co-annotation across audio-visual content, LiDAR and camera fusion, and paired image-text for multimodal AI training and evaluation.

What Makes Annotation Quality Reliable

Annotation quality is not a property of individual labels. It is a property of the system that produces them. Appen's quality management includes contributor calibration against gold standard examples, inter-annotator agreement measurement, multiple independent review rounds, and statistical sampling of final datasets. This infrastructure is what ensures that AI data quality meets the standard your training pipeline requires, consistently, at scale.