Clean labels. Production-ready datasets.
High-quality labeled datasets for NLP, computer vision, and classification. SpaceDrift uses Label Studio and custom validation pipelines to annotate text and images at scale, with full quality reporting and export in your preferred format.
Model quality is a direct function of data quality. Noisy labels, inconsistent annotation guidelines, and poorly structured datasets are among the most common reasons ML systems underperform — in both academic and production settings.
SpaceDrift provides structured, validated data annotation services for NLP tasks (NER, intent classification, sentiment labeling, relation extraction) and computer vision tasks (bounding boxes, polygon segmentation, keypoint detection, image classification).
Every annotation engagement begins with a clearly defined schema and annotation guidelines. Every delivered dataset is quality-validated — a representative sample is reviewed post-annotation to identify and correct systematic labeling errors before they propagate into model training.
What you receive
Labeled data exported in JSON, CSV, COCO, Pascal VOC, or your preferred format.
Inter-annotator agreement metrics and error analysis on a sampled subset.
Full documentation of label taxonomy, annotation rules, and edge case handling.
Exported project so you can review annotations and run future annotation rounds.
1 week of free corrections for any annotation errors identified after delivery.
Step by step
We define the label taxonomy, annotation guidelines, and edge case resolution rules in collaboration with your team.
We annotate a pilot batch of 50–100 samples for your review to validate quality before full-scale work begins.
Complete dataset annotated following the validated schema, with regular progress updates.
Random sample reviewed. Inter-annotator agreement calculated and included in the final report.
Final dataset exported in your required format, accompanied by full schema documentation.
Real numbers
Is this for you?
This service is designed for a specific type of client. If you see yourself in this list, we`re a good fit.
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