Where valuable data gets stuck
AI projects stall when research datasets, field notes, documents, and local-language records stay fragmented — or get cleaned in ways that strip the context models actually need.
Layer for Scholars, Researchers & Enterprise AI Teams
DataYetu turns research and real-world data into high-quality, rights-cleared, context-rich datasets that AI teams can train, evaluate and build against.
01The context gap
AI projects fail when valuable research and real-world data remain fragmented, poorly structured, weakly documented or stripped of context.
research datasets
field data
documents
transcripts
audio
chat histories
enterprise records
local-language data
How weak structure breaks AI work
Poorly structured files, missing consent, mixed languages, and lost collection context mean models cannot reliably train, evaluate, or retrieve what researchers and organizations already have.
What the data layer restores
We ingest, structure, de-identify, annotate, review, and version that research and real-world data — with provenance and permissions attached — so it can be evaluated, exported, or licensed where rights allow.
Where research and real-world data stall AI work
fragmented sources
weak documentation
stripped context
unclear rights
no provenance
02Product
The data layer between real-world information and AI
We ingest, structure, de-identify, annotate, review, validate, evaluate and version research and real-world data so it can be reliably consumed by AI systems. Provenance, permissions and dataset history travel with the data.
Researchers keep control of their data. Datasets become licensing-ready where ownership, consent and usage permissions allow — not because a file was uploaded.
01
Research and real-world data ingestion
Bring research datasets, field data, documents, transcripts, audio, chat histories, enterprise records, and local-language data into a structured workspace.
02
Annotation and human review
Schema-driven annotation with researcher teams or DataYetu-managed annotators, followed by configurable review and adjudication.
03
De-identification
Detect, classify, and redact or mask personal information — with human verification where required — without destroying the original source.
04
Provenance and permissions
Source, consent, context, and permitted uses travel with every record so released data stays auditable.
05
Dataset versioning
Immutable releases that point to exact asset hashes. Material additions, label corrections, schema changes, or quarantines become a new version or living increment — never a silent rewrite. Keep annotation and review in progress so living subscribers receive APPROVED updates when their subscription covers them.
06
ML evaluation and benchmarking
Run standard or custom benchmarks against a dataset version and compare baseline versus DataYetu-enhanced performance from measured runs only.
07
AI/RAG-ready export
Export JSONL, manifests, and versioned API access with retrieval metadata, provenance references, and source citations.
08
Commercial licensing readiness
Licensing-ready where ownership, consent and usage permissions allow — never assumed from the fact that data was uploaded.
// sample evaluation record
Illustrative schema only — field structure for a pilot evaluation pack. No invented source text, translations, or model results.
| Field | Contents |
|---|---|
| source_text | Consent-based Swahili / Sheng / code-switched example |
| language_variety | sw | sheng | code-switched (+ context notes) |
| meaning_annotation | Human-reviewed translation and intended meaning |
| labels | code-switching · slang · negation · urgency · tone · intent |
| model_output | Customer model response under test |
| failure_mode | Where meaning, tone, or intent breaks (when observed) |
| provenance | Consent, source, permitted uses, release version — what makes a record licensable or reusable |
03Pipeline
From research and real-world files to AI-ready data
A repeatable, consent-based pipeline — designed to be auditable at each step, with batches moving independently.
- 1L1
Upload
Ingest research and real-world files — text, documents, audio, images, or mixed batches — into a workspace.
- 2L2
Provenance & rights
Record source, collection context, consent status, and permitted uses before the data moves downstream.
- 3L3
De-identify
Detect PII, apply redaction or masking, and keep raw source separate from the ML-consumable derivative.
- 4L4
Annotate & review
Apply a schema, then run human review and adjudication so labels are not a single unchecked pass.
- 5L5
QA & evaluate
Statistical agreement, quality gates, and model evaluation against the versioned set — displayed only from actual runs.
- 6L6
Version & export
Freeze an immutable dataset version, then export, serve via API, or retrieve through RAG where permissions allow.
- 7L7
Distribute
Share with AI teams or license archives commercially only when ownership, consent, and usage rights permit.
04For scholars and researchers
Bring your research from raw files to a validated, versioned dataset
Keep control of your data while DataYetu provides the infrastructure, workflows and quality controls needed to make it AI-ready.
// researcher path
- Create workspace
- Upload data
- Structure & de-identify
- Annotate
- Review
- QA
- Evaluate
- Version
- Export / distribute
You remain the data owner. DataYetu does not take ownership of researcher data by default.
05For AI teams
Access hard-to-source data with provenance, quality evidence and measurable model impact
DataYetu is not merely selling raw files. Buyers get validated data, evidence, provenance, and measurable AI utility.
Download / export
Permitted files and manifests for a specific dataset version.
API access
Versioned endpoints that respect workspace and dataset permissions.
RAG integration
Retrieval metadata, source references, and provenance identifiers.
Evaluation benchmarks
Standard or custom tasks with stored methodology and scores.
Controlled evaluation
Baseline versus DataYetu-context comparison from measured runs.
Dataset version history
Immutable releases with exact asset hashes and changelogs.
Provenance verification
Integrity receipts and lifecycle history without exposing raw PII.
Usage restrictions
Training, evaluation, commercial use, and redistribution flags.
Quality reports
Agreement, coverage, rights readiness, and known limitations.
06Available datasets
Review datasets with pipeline evidence — license for ML only where rights allow
Published releases show approved sample counts, review configuration, and whether ML licensing is enabled. Living subscriptions receive APPROVED updates as hosts keep shipping. Terms forbid prohibited use; hosts are notified when a licence is paid.
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06Early stage
What we're validating
We're building the first pilot around a focused question: can rights-cleared, context-rich Kenyan language data help scholars, researchers, and AI teams detect and reduce failures that generic benchmarks miss?
The numbers below are pilot targets being validated — not results we claim to have achieved.
Target //01
30–50
30 to 50 reviewed failure examples
Human-reviewed cases where models miss Swahili or Sheng meaning.
Target //02
5–10
5 to 10 reproducible model errors
Errors a design partner can re-run and inspect.
Target //03
>90%
Independent reviewer agreement
Measure how consistently native reviewers agree on meaning and labels.
Target //04
3–5
3 to 5 buyer discovery conversations
Structured conversations with AI teams deploying in Kenya or East Africa.
Target //05
01
First design-partner evaluation
One partner runs their model against the pilot evaluation set.
07Positioning
Why DataYetu is positioned to do this
Local-language data is the origin and proof point. The data layer is for scholars, researchers, and enterprise AI teams who need structured, rights-cleared datasets they can train, evaluate, and build against.
01
Local-language origin
Kenyan Swahili and Sheng — including code-switching — is the origin and proof point, not a generic “African data” abstraction.
02
Meaning-failure lens
We start from where models break on local phrasing, tone, and intent that translation benchmarks miss — then apply that discipline to research and real-world data.
03
Consent, provenance, and licensing
Each release carries provenance so archives can be used for research, evaluation, or — where ownership, consent and usage permissions allow — licensed to AI training labs. Researchers keep control of their data.
04
Independent native review
Meaning, intent, tone, and translation quality are validated by independent native reviewers before delivery.
05
Measurable pilot targets
Success is defined in countable outputs: reviewed failures, reproducible errors, reviewer agreement, and a first design-partner evaluation.
// create a workspace
Turn research and real-world data into AI-ready datasets
Create a workspace, run the pipeline, and leave with a validated, versioned dataset — with provenance and permissions attached.
Prefer to talk first?
08Scope
Pilot focus
Rights-cleared Swahili and Sheng data is the active proof point. Research collections, customer-support records, and other real-world sources are where this data layer is going — not current proof points.
Active pilot
Language and Linguistics
Rights-cleared, human-validated Swahili and Sheng data for scholars, researchers, and AI teams building for African users — preserving code-switching, cultural context, tone, and intent.
// later applications
Later
Healthcare
Later application — not part of the current pilot.
Later
Finance
Capital markets and fintech — later application, not part of the current pilot.
Later
Agriculture
Later application — not part of the current pilot.
Later
Customer support
Chatbots, agents, and conversational products — later application, not part of the current pilot.
Later
IoT & wearables
Later application — not part of the current pilot.