Ground truth your models can stand on.
Your agent — or your data team — commissions real-world data collection, human labels and evaluation data from KYC-verified Operators in Nigeria. Funds escrow first, every submission is verified by AI and people, and the dataset comes back as one signed record.
KYC-verified collectors · Gold-task quality gates · Consent recorded · NDPA-aligned
- Quidax
- VFD
- Paystack
- Smile ID
- Dojah
- WhatsApp Business Platform
- Quidax
- VFD
- Paystack
- Smile ID
- Dojah
- WhatsApp Business Platform
- Quidax
- VFD
- Paystack
- Smile ID
- Dojah
- WhatsApp Business Platform
The problem, plainly.
Synthetic wells run dry
Models trained on scraped and synthetic data plateau. Real-world, consented, provenance-clean data is the scarce input — and the hardest to collect at arm’s length.
Anonymous crowds, unverifiable labels
Crowd platforms can’t tell you who labeled your data or whether the same person labeled it twice under different accounts. Every Hannu Operator is KYC-verified with a portable reputation.
The worker-trust debt is real
AI-work platforms burned Nigerian workers with opaque rejections and stranded payouts. Operators who trust the platform produce better data — our payout and appeal policies are published, not promised.
Stop training on data you can’t defend.
The data debt you carry
- No way to tell who labeled your data — or whether one person labeled it twice under different accounts.
- Opaque rejections and stranded payouts burn workers; quality decays as the good ones leave.
- Provenance that will not survive a model audit or a licensing negotiation.
- Consent status unknown, jurisdiction by jurisdiction.
Provenance-clean, worker-fair data
- Every row attributable to a KYC-verified Operator with a portable reputation.
- Hidden gold tasks with known answers measure quality inside every batch.
- Published payout and appeal policies — Operators who trust the platform produce better data.
- Consent lineage recorded with the data, erasable on request.
Field data collection
Structured on-the-ground collection against your schema — locations, prices, signage, conditions — with geotagged proof.
See the full use case →Data labeling
Human labels for training and evaluation sets, quality-gated by hidden gold tasks with known answers.
Translation & transcription
Nigerian-language pairs and audio transcripts — evaluation data that models can’t fake.
Surveys & studies
Real respondents in the field, consented and verified. Multi-site structured studies unlock as a workflow product.
You order it. We run it. One signed record comes back.
Define the schema
Post the task from the console or the API — the shape of the data, the proof required, the price. Escrow locks first.
Operators collect, verification screens
Matched, verified Operators do the work; GPS, EXIF and content checks plus human review score every submission before you see it.
One signed dataset back
Approved data arrives as a single verified deliverable with a recomputable audit trail — pay only for what passes.
“The scarcest training data is the kind someone actually walked outside to collect.”