In development · first release planned

Fasili Africa Index

How well do frontier AI models handle African work? The Index tests them on tasks written by practising African professionals and language specialists, and publishes the scores.

SAMPLE TASK · TAX & ACCOUNTINGILLUSTRATIVE

An invoice totals 11,600, including VAT at 16%. How much VAT is due, and what is the net amount?

  • States VAT of 1,600Required
  • States a net amount of 10,000Required
  • Divides by 1.16 rather than taking 16% of the grossRequired
  • Shows the workingScored

Rubric written and validated by registered accountants.

Method

Built the way professional benchmarks are built.

Global benchmarks rarely test African law, tax rules, clinical guidance or the way people in African cities actually write. The Index fills that gap.

01

Written by professionals

African accountants, clinicians, lawyers, teachers and language specialists write each task from real work.

02

Graded by rubric

Each task has pass/fail criteria and a reference answer, checked by a second expert.

03

Published openly

Scores appear on a public leaderboard with the method, and a sample of tasks is released.

04

Kept private

The full test set stays private so models cannot be trained on the answers.

Domains

Five areas of African work.

The first release covers 200 tasks in three domains, run on at least six frontier models. The second grows to 600 tasks in five domains.

DomainExample taskWritten byRelease
Swahili & Sheng customer supportResolve a failed mobile-money transfer reported in ShengLanguage specialistsFirst
Tax & accountingCompute VAT and payroll tax for a small business’s invoicesRegistered accountantsFirst
HealthGive triage advice consistent with national health guidelinesRegistered cliniciansFirst
LawAdvise on notice periods under national employment lawPractising lawyersSecond
EducationMark a learner’s answer against a national curriculum rubricRegistered teachersSecond
Leaderboard. Scores will appear here with the first release, with error bars and the date each model was tested.
Independence

We sell data to labs we score. Here is how we keep the Index fair.

  • Published methodologyHow tasks are written, validated and scored, released with every version.
  • Test items are never soldNo Index test task is licensed or used in any client’s training data.
  • Same conditions for every modelIdentical prompts, settings and grading for each model tested.
  • Independent reviewAn outside advisor reviews each release before publication.
Datasets

African-language and African-context data, ready to license.

Built by our employed team between client projects, documented with a datasheet, and licensed non-exclusively to labs and model developers.

In preparation

Swahili & Sheng customer-support intents

Customer messages from banking and telco support, labelled for intent, sentiment and urgency by native speakers.

Languages
Swahili, Sheng, English code-switching
Use
Training and evaluating support assistants
In preparation

African-context LLM evaluation set

Questions on finance, health and education with rubrics and reference answers grounded in African practice.

Domains
Finance, health, education
Use
Model evaluation and LLM-judge calibration
In preparation

Expert-graded finance Q&A

Accounting and tax questions answered and graded by registered African accountants.

Experts
Registered accountants
Use
Fine-tuning and preference data
Planned

Swahili & Sheng speech

Transcribed conversational speech for speech recognition and voice-quality testing.

Format
Audio with verified transcripts
Use
ASR training and TTS evaluation