01
African language NLP
Low-resource language modeling, code-switching, dialect variation, and orthography — from Sheng to Nyanja. We study how annotation schemas must change when a language has little written history.
Research
The science behind African training data.
Ruach AI's research practice advances the methods that make expert-labeled data measurable, reproducible, and African by design.
02
Data-centric evaluation
Benchmarks built from African institutional realities — agriculture, healthcare, finance, public services — so evaluation measures what deployment actually demands.
03
Annotation quality science
Inter-annotator agreement, arbitration design, and the economics of expert routing. We publish our internal thresholds and hold every dataset to them.
04
Human feedback & alignment
Preference data from African domain experts, and what alignment looks like when the rater's cultural context is the signal, not the noise.
0.75
Published IAA arbitration threshold
30+
Languages under active study
100%
Agreement scores shipped with data
24/7
Drift & quality monitoring
Research hub
Papers
Research papers
Working papers and peer-reviewed findings on African language data, model evaluation, and annotation quality.
First publications in preparation
Labs
Ruach Labs
Experimental benchmarks, shared datasets, and reproducible methods developed with African research partners.
Partnership programme opening soon
Blogs
Research notes
Field notes from our researchers on low-resource NLP, expert feedback, and data-centric evaluation.
Opening series in development
Collaborate
Research partnerships.
We work with universities, labs, and independent researchers across the continent on low-resource language datasets, evaluation design, and annotation methodology.
Joint studies, shared benchmarks, and co-authored publications are welcome. Send us your area of interest and the dataset problem you are trying to solve.
Propose a collaborationStart a project
Build AI that works in Africa.
Begin with a pilot dataset, documented quality scores, and an output ready for model training.