Human intelligence from Africa

AI that understands Africa,
shaped by the people who live it.

We collect, label and evaluate the data AI needs to work for Africa, from speech, text, images, video and documents to model outputs, with native speakers and domain experts who know the context. Run your own collection on AfriEval, or let us manage the entire collection for you.

We work withSpeechTextImagesVideoDocumentsModel outputs
See what we do
In the languages people actually speak
Kiswahili
Swahili
Yorùbá
Yoruba
Hausa
Hausa
አማርኛ
Amharic
Igbo
Igbo
isiZulu
Zulu
العربية
Arabic
Français
French
and more
What we do

AI data and evaluation, grounded in African languages, places and expertise.

Tell us what your model needs. We bring in the right contributors, run the work with quality checks built in, and hand back clean, documented data you can train or test on.

Speech data collection

Contributors record prompts in their own voice, accent and language, straight from the browser.

ExamplesRead-aloud sentences, everyday speech, domain vocabulary

Transcription

Audio turned into accurate text in the language spoken, with dialect and code-switching handled by native speakers.

ExamplesVerbatim transcripts, speaker labels, entity tagging

Translation and language data

Translations, glosses and word pairs that keep close meanings distinct, checked by speakers of both languages.

ExamplesGikuyu to English, Swahili to English, word pairs

Image and video annotation

Classification, tagging and document checks, with the local context a generic workforce misses.

ExamplesImage classification, OCR verification, content tagging

Model evaluation and RLHF

People compare, rate and red-team model answers in African languages, so you can see where a model fails.

ExamplesPreference pairs, hallucination and toxicity checks

Expert review

Doctors, lawyers, teachers and agronomists judge answers in their own field.

ExamplesClinical, legal and agricultural review

Contributors certified for each task type and language
Quality checks on every project
Usable data, with metadata and provenance
Two ways to work with us

We collect it for you, or you collect it yourself.

Some teams want a finished dataset. Others already have a community, contributors or a data source and need the infrastructure to collect, review and manage it properly. AfriEval supports both.

We collect for you

Tell us what you need. We recruit and verify our community of contributors, manage collection and quality review, and deliver a ready-to-use dataset. Best when you want results without managing the workforce or workflow yourself.

  • Contributors matched by language, region, expertise and task requirements
  • Multi-stage quality checks and senior review
  • Raw and reviewed data delivered in the format you need
  • Consent, contributor and collection metadata included
Run your own collection

For research teams, curators and language communities that want to manage their own collection. Bring your contributors, invite certified AfriEval contributors, or combine both while managing collection, review and quality control in one place.

  • Start from a template or build your own speech, text, translation or evaluation task
  • Bring your own contributors or invite from AfriEval's certified contributor network
  • Set your own quality workflow, from simple review to consensus and senior escalation
  • Track progress and quality as submissions arrive
  • Export raw data, reviewed data and associated metadata
Create a curator account →New accounts are reviewed before access.
Inside the platform

Everything a collection run needs, in one workspace.

Start from a template

Prebuilt starting points for speech, text, images, documents and model answers. Copy one into your library and adapt it to your project.

AfriEval
The AfriEval template library, with filters for data type and job and cards for ready-made templates.
Who it is for

Made for the people who build, test and shape AI.

Researchers

Run reproducible evaluations, share benchmark results, and test how models handle African languages and contexts.

Teams building AI

Get the data you need, or evaluate and improve products for African users, from a first prototype to a production system.

Curators and language communities

Run your own collection with your contributors, with review, quality control and export in one place.

Benchmarks

See how models handle African languages.

Run an existing benchmark or bring your own questions. Every search, document read and answer is recorded, so results can be checked and reproduced.

Featured benchmark
Multilingual agentic search

Questions asked in African languages are answered by an agent that searches an English corpus. Every search round is recorded, and results export as JSONL.

Any model, any query set
Bring your own evaluation

Upload your own questions and corpus, choose a model, and get a full trace of what it searched, read and answered.

The AI lifecycle

One journey. End to end.

From the first recording to the published benchmark, in one place.

Collect

Multilingual speech, text and images from vetted contributors.

Annotate

Label, transcribe and structure data with templates you can adapt.

Validate

Consensus and senior review check every judgment.

Evaluate

Preference, safety and reasoning evaluations of model answers.

Benchmark

Compare model versions and track quality trends over time.

Share

Export datasets and results as Excel, JSON or JSONL, ready for training or publication.

The workflow builder

Compose human intelligence,
stage by stage.

From contributor to export, every stage is set up in one place. This is an illustrative workflow.

workflows / sample pipeline
Sample workflow
Pipeline
  1. 01
    Contributor
    Source
    Vetted contributors pick up tasks in their own language and field.
  2. 02
    Contributor
    Label
    Contributors label, transcribe or record, guided by instructions written for the project.
  3. 03
    Consensus
    Validate
    Several contributors answer each task independently, never the same person twice.
  4. 04
    Senior review
    Approve
    Senior contributors review work and approve, correct or reject it.
  5. 05
    Export
    Ship
    Download your data, or send it to your own systems, with provenance for every record.
5 stages Contributors and senior reviewers Webhook delivery
Human intelligence network

The people behind the data

Not an anonymous crowd. Native speakers and subject-matter experts, matched to the work they know best.

Speech Contributors
Medical Experts
Lawyers
Teachers
Native Linguists
Agricultural Specialists
Software Engineers
Financial Analysts
Researchers
Trust and quality

Every judgment, traceable.

Quality checks, permissions and lineage are built in, whether you are a solo researcher or a large team.

Consent and licensing
Every dataset comes with its provenance, consent basis and licence terms written down.
Quality assurance
Several contributors answer each task, and senior review decides what is approved.
Permissions
Separate roles for clients, contributors, senior reviewers and admins.
Traceability
Every submission and review decision is recorded against the task it belongs to.
Exports and webhooks
Excel, JSON and JSONL exports, raw audio, and webhooks to your own systems.
Contributor privacy
Contributors appear as pseudonymous codes in your data, never by name.
Built with African intelligence

Why Africa matters

Africa is home to thousands of languages and a generation shaping how technology is used. Models built without them misread names, proverbs, medicine, law and everyday speech. AfriEval puts those people at the centre of building and judging AI, not at the end of the line.

AI cannot be globally representative without African intelligence.

AI is better when Africa helps build it.

Tell us the languages, the kind of data and the format you need. We will collect, label and evaluate it with people who know the language and the context.

Or email info@afrieval.com

Speak an African language or have expert knowledge?

Join as a contributor, earn certifications for the work you are good at, and help shape the data AI learns from.