Programmatic labeling
African domain experts encode their knowledge into labeling functions applied across your dataset. This increases throughput while preserving specialist judgment.
Methodology
Quality comes from judgment, not volume.
We combine programmatic labeling, active learning, and measured human agreement with African domain expertise that global pipelines cannot reproduce.
Active learning
We identify the samples your model is most uncertain about and route only those to human annotators. Expert time is focused where it creates the greatest information gain.
Quality control
Inter-annotator agreement is calculated automatically. Cohen’s Kappa is tracked by entity type and annotator, with low-agreement tasks sent to expert arbitration.
Continuous validation
Data drift monitoring compares production data with the training distribution, detecting changing language, seasonal patterns, and emerging conditions before failures occur.
0.75
IAA arbitration threshold
15.9×
Targeted annotation ROI
100%
Versioned outputs
24/7
Drift monitoring
Start a project
Build AI that works in Africa.
Begin with a pilot dataset, documented quality scores, and an output ready for model training.