Detect bias, drift, and inconsistencies in your labeled datasets. LabelCheck AI uses consensus scoring and automated QA to maintain the integrity of your training data.
Comprehensive quality assurance for your annotation pipeline
Multi-annotator agreement analysis. Identify outliers and measure inter-rater reliability with Cohen's Kappa, Fleiss' Kappa, and custom consensus metrics.
Automatically discover systematic biases in your dataset. Detect label imbalance, demographic skew, and annotator-specific tendencies before they impact model performance.
Track temporal changes in annotation patterns. Detect concept drift, label drift, and quality degradation over time with real-time alerts and trend analysis.
Find conflicting labels and ambiguous samples. Use similarity clustering and embedding analysis to identify contradictory annotations within your dataset.
Clean training data leads to better model performance. Reduce false positives and improve generalization.
Catch quality issues early. Automated QA reduces manual review time by 70%.
Minimize re-annotation costs. Fix problems before they propagate through your pipeline.
Full audit trail and explainability. Know exactly where and why quality issues occur.
Four-step quality assurance pipeline
Import annotations from any format (CSV, JSON, COCO, YOLO). Supports all major annotation platforms.
Run automated checks for bias, drift, and inconsistencies. Generate consensus scores and quality metrics.
Identify problematic samples with low consensus, high variance, or suspicious patterns.
Prioritize samples for re-annotation. Export cleaned datasets with confidence scores.
Explore consensus scoring visualization
Click on samples to see annotation details
Start detecting annotation issues before they impact your models.