We support annotation across multiple data modalities including images, videos, text, audio, multimodal datasets, and source code.

Annotation tasks vary depending on the dataset and AI use case. Common tasks include object detection, segmentation, entity labeling, transcription, preference ranking, code review, and instruction-response evaluation.

Quality is maintained through structured guidelines and multi-layer QA processes including multi-pass review, gold-standard tasks, consensus validation, and sampling-based audits. Flexible quality assurance method is applied following clients' needs and nature of the project.

Yes. Our operations are designed to scale depending on project requirements. We maintain trained annotator pools and standardized onboarding pipelines to ramp up new teams quickly.

We typically work with industry-standard annotation platforms such as CVAT, Label Studio, and Prodigy depending on the project and data modality. We also prefer working on clients' platform to ensure alignment and data security.

Datasets can be delivered in formats commonly used by machine learning teams such as COCO, YOLO, Pascal VOC, JSON, JSONL, or CSV.

Yes. We encourage clients begin with a small pilot dataset to validate guidelines and evaluate annotation quality before scaling to larger datasets.