Africa’s Data Engine & Refinery
The data that makes African AI work.
Ruach AI produces expert-labeled, African-validated training data for AI systems deployed across the continent. Built in Nairobi. Built for Africa.
2,000+
African languages in scope
6
Integrated platform modules
30+
Languages in active coverage
<1%
Africa’s share of global AI data
The mandate
AI deployed in Africa should understand the people, languages, and institutions it serves.
Capabilities
Expert data, end to end.
From raw datasets to production evaluation, Ruach combines local subject-matter expertise with rigorous data engineering.
Data Labeling & Annotation
Multi-modal annotation across text, image, audio, and video. Named entity recognition, sentiment analysis, object detection, and image segmentation — executed by African domain experts with the cultural and linguistic context your model needs.
02RLHF & Preference Data
African domain experts compare AI outputs and select the better response — teaching your model what good looks like in African contexts. High-value preference data for frontier AI teams, delivered with expert review.
03Model Evaluation
Benchmark your model against African language tasks, domain contexts, and specific failure modes that conventional benchmarks miss — before production deployment.
One pipeline
Built around the decisions that shape model quality.
Programmatic labeling
African domain experts encode their knowledge into labeling functions applied across your dataset. This increases throughput while preserving specialist judgment.
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.
Connected to the ecosystem
Start a project
Build AI that works in Africa.
Begin with a pilot dataset, documented quality scores, and an output ready for model training.