Ground Your AI in
African Reality
We Provide Large Scale LLM testing and High Quality Dataset that ensure your models reflect African realities while empowering local Communities.
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Why Kitala?
Standard crowdsourcing fails in Africa. We combine deep offline reach with academic rigor to provide ground truth data.
Offline & Online Communities
We don't just access internet users. We deploy physical surveyors to capture data from remote, offline communities, ensuring a truly representative dataset.
Experts & Precise Demographics
Beyond basic labeling. We recruit specific demographic profiles and deploy domain experts (doctors, bankers) to validate high-stakes data.
Focused on African Culture & Social Norms
Our methodology is designed to capture nuance. We align models with local traditions, social norms, and linguistic subtleties that outsiders miss.
Our Services
Tailored for Generative AI evaluation and high-stakes sectors. Our expert-led services ensure your AI speaks the local language—culturally and linguistically.
Stereotype & Bias Evaluation
Auditing LLM to detect locally grounded and harmful bias across African demographics and ethnic groups. We go beyond standard benchmarks.
Local Stereotype Detection Political Sensitivity Checks Cultural Norm Verification
Live LLM Testing (RLHF)
Real-time human feedback (Text or Voice) to rate model responses for safety, helpfulness, and cultural accuracy in live scenarios.
Culturally Grounded Q&A
High-quality, expert-curated Q&A datasets in specific domains (Health, Finance) reflecting local realities for authentic fine-tuning.
Domain-Specific Expertise Local Language Nuances
Voice Datasets for ASR
Diverse voice data capturing varied accents, dialects, and real-world background noise for robust speech recognition evaluation.
Mobile-First Collection Natural Environment Audio
Sectors Where
Nuance Matters
We specialize in high-stakes domains where "good enough" translation isn't safe. We ensure accuracy in critical fields like healthcare and finance.
Maternal & Public Health
Evaluating medical advice for local relevance, terminology, and safety guidelines. We verify that AI health assistants provide accurate, culturally safe information for expecting mothers and rural communities.
Inclusive Finance
Reviewing financial literacy content and support bots for unbanked populations. We adapt complex financial terms (interest rates, loans, mobile money) to local languages and mental models.
Cultural Alignment
Identifying western bias in history, social norms, and naming conventions. We align models with the lived realities, traditions, and sensitive historical contexts of African communities.
The Kitala Data Store
Download ethically sourced, expert-labeled datasets today.
Afristereo Benchmark
A culturally grounded dataset for evaluating stereotypical bias in Large Language Models across Senegal, Kenya, and Nigeria. This repo includes the raw and cleaned Datasets, the complete pipeline (manual + automated) to generate the dataset, and the code used to perform the various LLM evaluations.
Health Q&A (Senegal)
Wolof & French medical Q&A pairs grounded in local practices.
View Details ->Finance QA (Ghana)
Financial literacy content adapted for unbanked populations.
Coming soon ->Hausa ASR (Speech)
Diverse voice data capturing varied accents and backgrounds.
View Details ->Research & Benchmarks
Updated: Dec 2025 Evaluating Stereotypical Bias (BPR Score).
*BPR: Bias Probability Ratio. Higher scores indicate stronger stereotypical associations.
| Model | BPR Score | ||
|---|---|---|---|
| Modern (2023-2024) | |||
| Llama 3.2 3B | 0.78 | ||
| Mistral 7B | 0.75 | ||
| Qwen 2.5 7B | 0.71 | ||
| Gemma 2 2B | 0.71 | ||
| Phi-3 Mini | 0.70 | ||
| Baseline (2019-2022) | |||
| GPT-Neo | 0.71 | ||
| GPT-2 Large | 0.69 | ||
| FinBERT | 0.50 | ||
Alignment Debt: The Hidden Work of Making AI Usable
Cumi Oyemike et al. (arXiv 2025)
Investigating the invisible labor and structural challenges in aligning AI models for diverse global contexts.
AfriStereo: A Culturally Grounded Dataset
Yann Le Beux et al. (arXiv 2025)
A comprehensive benchmark analyzing how LLMs handle cultural nuances and biases.
Want to test your own models?
Deploy our expert surveyors to evaluate your model's alignment with African values.
Our research partners
Join as a Research Participant
Are you a linguist, health expert, or community leader? Join our network of experts to evaluate AI models and ensure they reflect African reality.
Earn Reliable Income
Get paid fairly for every mission you complete. Earn competitive rates for your local expertise, paid directly to your mobile wallet.
Flexible Contribution
Work from anywhere using our mobile app. Whether you have 15 minutes or 2 hours, there are tasks available for you.
Protect Your Culture
Help stop AI hallucinations. Ensure your language and cultural norms are accurately represented in global technology.
Priority Recruitment Regions
Kenya 
Ghana 
Nigeria 
Senegal 
Rwanda 
Evaluate Medical Advice
Check if this advice aligns with local health guidelines in Nairobi.
A Reflection of Culture,
Identity & Diversity.
In Lingala, "Kitala" means reflection. We chose this name because we believe Artificial Intelligence in Africa must be a true reflection of the people it serves—grounded in our diverse cultures, languages, and realities.
We are building the infrastructure to ensure no community is left behind in the AI revolution.
Powered by YUX Design
The team behind Kitala is YUX Design, a leading social research and technology design company in Africa.
