Model Evaluation & Alignment Infrastructure for Africa
AfroEval Scorecard™ measures how an AI system performs for African users and in African contexts — language, culture, hallucination, bias, and safety — before it reaches production rather than after.
The Deployment Reality
An AI system scores 92%, clears procurement, and ships.
Then it reaches Lagos, Nairobi, Addis Ababa, Accra.
For users writing in African-language-inflected English or French — or code-switching mid-sentence — the same model scores 63%.
A 29-point gap. No aggregate score reported it.
Aggregate accuracy is an average, and averages hide exactly the populations an institution deploying in African markets most needs to see.
Major benchmark suites rest on corpora that underrepresent African languages, and assess cultural fit against contexts they were never built to represent.
A confident, reproducible number that is silent on the question that matters reads as reassurance. Nothing was found because nothing was looked for.
AI systems are entering credit decisions, citizen services, customer operations and clinical support — where an error is a denied application or a missed diagnosis.
Continental and national governance frameworks now expect these systems to be fair, accountable and evidenced. The instrument to demonstrate it largely does not exist.
Afterwards the same finding arrives as an incident, a regulatory enquiry or a remediation programme — by which time the system is load-bearing.
What It Is
AfroEval Scorecard™ is a structured evaluation of how an AI system performs for African users and in African contexts.
It returns a scorecard across five dimensions — broken out by user group, not averaged into one number — as evidence a board, a regulator or an auditor can read.
The system is assessed through the same interface a real user reaches, so the result describes deployed behaviour rather than laboratory behaviour.
Independent experts review separately, and the agreement between them is quantified and reported alongside the findings rather than assumed.
Results are reported by the populations that will use the system, not collapsed into a single average that conceals the difference.
Evaluation sits beside the deployment rather than inside the request path. No added latency, no dependency introduced, no change to a live system.
Model weights, training data and internal systems stay where they belong. Evaluation needs none of them.
We conduct the evaluation and present the findings to the institution’s technical and governance leads.
Five Dimensions
Each dimension is assessed separately and reported separately, so an institution can see not just whether a system performs, but for whom it performs and where it stops.
What Changes
Questions Institutions Ask
Standard suites answer whether a model performs well on tasks built largely in English and in Western contexts. That is a real question, and it is not the question an institution deploying in African markets needs answered. AfroEval Scorecard™ measures performance for the users and contexts of the actual deployment, and reports results disaggregated by user group rather than as a single average.
No. The system is evaluated as it is served — through the same interface a user reaches. That keeps the evaluation aligned with production reality and keeps proprietary assets where they belong.
By treating agreement as something to be measured rather than assumed. Reviews are conducted by independent experts working separately, and the level of agreement between them is quantified and reported alongside the findings. Where experts disagree, that is itself a finding, and it is disclosed rather than smoothed over.
No. A test set that has been shared is no longer a measurement — it becomes a target, and a system tuned toward it stops telling anyone the truth about how it behaves in the wild. Withholding the items is what makes the score meaningful. The methodology behind the score is explained in full in the briefing.
No. It runs out-of-band, in batch, alongside the deployment rather than inside it. There is no added latency, no dependency introduced into the request path, and no change required to a live system in order to be evaluated.
That is the outcome the evaluation exists to surface, and it is more useful early than late. The scorecard identifies where performance breaks down and for whom, which turns an abstract concern into a specific, addressable list — scope changes, guardrail work, review layers, or a vendor conversation held with evidence in hand.
A briefing walks through what the evaluation covers, how the five dimensions are assessed, and what the resulting scorecard and analysis contain. Thirty minutes, no obligation.
Governance-grade. African-context. Verifiable.
