// cognitive_dataops
Annotation built for model performance, not checkbox completion.
Datafy Lab provides human-in-the-loop annotation workflows for physical AI datasets across images, video, text, speech, robotics trajectories, and sensor data. Every workflow includes QA, consistency checks, and dataset-level reporting.
// supported_data
Every modality your model touches.
ImagesVideoTextSpeechSensor dataRobot trajectoriesTask demonstrationsIndustrial inspection footageField dataSynthetic data
// annotation_types
Labels that hold up in training.
Bounding boxesSegmentation masksKeypointsObject trackingClassificationTemporal segmentationAction labelsDefect labelsScene metadataSpeech/text labelingFailure categorization
// qa_loop
Every workflow is QA’d, not just labeled.
// 01
Calibration
Annotator guidelines, gold sets, and edge-case examples before production.
// 02
Consistency checks
Inter-annotator agreement, spot reviews, and disagreement resolution.
// 03
Expert review
Domain reviewers verify ambiguous, rare, or safety-critical labels.
// 04
Dataset reporting
Coverage, balance, and quality metrics delivered with every batch.
// faq
Common questions
Bounding boxes, segmentation, keypoints, object tracking, classification, temporal segmentation, action labels, defect labels, scene metadata, speech/text labeling, and failure categorization.
We diagnose model failures, design data strategy, capture real-world data, annotate with QA, filter, and certify. Annotation is one step inside a closed loop.
Yes. We can re-annotate, QA, and extend datasets you already have, with clear consistency reporting.
We validate and annotate synthetic data and measure synthetic-to-real transfer gaps; we focus on making data model-ready rather than generating volume for its own sake.
Each qualified dataset ships with a Model-Ready Data Certificate covering rights, privacy, coverage, balance, annotation quality, limitations, and readiness.
Book a Data Failure Audit, or share a sample so we can scope an annotation workflow with QA built in.