Data annotation services for model training and evaluation
Annotation quality sets the ceiling on what your model can learn. Appen delivers labeled text, image, video, speech, LiDAR, and multimodal data with inter-annotator agreement measured and reported per project - not asserted in a capability deck. Agreement thresholds are set with your team before work begins, and the delivery includes the numbers.
Krippendorff's alpha or Cohen's kappa per project, with the threshold agreed in scoping.
Annotated by in-region native speakers, not translated after labeling.
Clinicians, attorneys, and engineers for domain work; calibrated generalists for volume tasks.
Consent, licensing, and chain of custody documented for EU AI Act and GDPR review.
What data annotation is
Data annotation is the process of labeling raw data so a machine learning model can learn from it. It applies structured labels - categories, bounding regions, transcripts, entity spans, preference rankings, or trajectory scores - to text, image, video, audio, sensor, and document inputs. Label accuracy and consistency across annotators determine how reliably a model generalizes to data it has not seen.
Two properties separate usable annotation from expensive noise: consistency, measured as agreement between independent annotators on the same item, and coverage, meaning the labeled set represents the conditions the model will meet in deployment. Both are measurable. Appen measures and reports both.
Annotation services by data type
Text and NLP annotation
Intent classification, named entity and relation extraction, sentiment and toxicity labeling, relevance rating, and instruction-response pair construction for language model training. Guidelines are drafted with your research team and revised against pilot-batch disagreement before full production.
Image and video annotation
Bounding boxes, instance and semantic segmentation, keypoint and pose annotation, object tracking across frames, and temporal action classification for consumer, industrial, medical, and autonomous driving footage.
Speech and audio annotation
Verbatim and orthographic transcription, speaker diarization, timestamp alignment, emotion and paralinguistic event labeling, and acoustic scene classification across 100+ languages and 500 locales.
LiDAR and 3D point cloud annotation
3D cuboid annotation, point-level semantic segmentation, and LiDAR-camera sensor fusion for perception stacks in autonomy and robotics. Annotation is performed in the sensor frame, with cross-sensor consistency checked rather than assumed.
Multimodal annotation
Cross-modal grounding annotated explicitly - region-to-phrase, timestamp-to-utterance, document-region-to-answer - for vision-language and any-to-any models, so alignment is supervised rather than inferred.
Preference and rubric-scored annotation
Human preference rankings, pairwise comparisons, and rubric-scored model output evaluation for RLHF and alignment work. Rubrics are written per task so partial correctness is scored as partial, not failed.
How annotation quality is measured
Annotation quality is not a property of an individual label. It is a property of the system that produces labels.
Inter-annotator agreement, reported per project.
Reliability is measured with Krippendorff's alpha or Cohen's kappa depending on task structure, label type, and annotator count. Thresholds are agreed before production starts and reported at delivery. Where annotators disagree, items are adjudicated by a senior reviewer - disagreement is resolved, not averaged away.
Gold-standard test questions running continuously.
Known-answer items are injected into live task queues to score contributor accuracy in real time. Contributors falling below the project threshold are routed out before their work enters the corpus.
Guidelines calibrated on a pilot batch, not written in advance and hoped for.
A pilot run surfaces the edge cases that break a spec. Guidelines are revised against observed disagreement, then locked for full-volume production.
Contributor networks matched to the task.
Domain work routes to credentialed specialists; locale work routes to in-region native speakers across 80+ languages and 500 locales. Cohorts are longitudinal, so annotator calibration compounds across a program instead of resetting with each batch.
Regulatory documentation as a deliverable.
Consent records, licensing terms, and data lineage are produced in a form that supports EU AI Act technical documentation, GDPR and CCPA obligations, and NIST AI Risk Management Framework mapping.
Annotation delivery and tooling
Annotation runs on the Appen platform, with model-assisted pre-labeling where it demonstrably reduces reviewer time and is disabled where it biases the label. Output formats include COCO, Pascal VOC, JSONL, JSON, and client-specified schemas (audio delivery also published as WAV/FLAC). Delivery is via secure transfer or into your environment.
Results from deployed annotation programs
Frequently asked questions
What is data annotation?
What is the difference between data annotation and data labeling?
How is data annotation quality measured?
What is inter-annotator agreement and why does it matter?
How much do data annotation services cost?
How long does a data annotation project take?
What data formats and annotation types are supported?
Can Appen annotate data that must stay inside our environment?
Ready to scope an annotation program?
Our team offers customized solutions to meet your specific data annotation needs, providing in-depth support throughout the project lifecycle.