About TruVector
TruVector is Africa's Ground Truth for AI. We build ethical, auditable validation pipelines for African data — so AI systems can understand our languages, accents, and contexts with confidence.
Our mission
Make AI work for Africa — ethically.
Improve AI performance for African languages, accents, and speech patterns.
Ensure validation reflects cultural context and real-world usage.
Build datasets with rich metadata: language, region, consent, age, phenotypical data, validation history, and provenance.
Enable teams to deploy with lower dataset risk and higher confidence.
Transparent consent, fair compensation, and respect for contributors.
The problem we're solving
African languages, accents, faces, and environments remain underrepresented in AI datasets. When data is collected, it's often inconsistent, context-poor, and hard to audit.
This creates downstream risk: models learn noise instead of signal, evaluations look good but fail in deployment, and teams can't explain dataset origin during audits or reviews.
We focus on trust over volume. Auditability matters because it enables responsible deployment, reduces legal and reputational risk, and builds datasets that teams can confidently share internally and externally.
Underrepresentation of African contexts
Context-poor datasets
Weak validation and inconsistent labeling
No clear consent trail
Uncertain provenance and auditability
High dataset risk for teams
Our approach
How we ensure quality and trust in every dataset
Ethical collection
Data is collected with explicit consent, clear context, and participant understanding of what is being contributed and why.
Rigorous validation
Structured multi-stage review processes confirm correctness and filter low-quality or risky samples. Produces traceable decisions and quality tiers.
Rich metadata capture
Every data point includes comprehensive metadata: language, region, consent records, age, phenotypical data, validation history, and full provenance.
Audit-ready delivery
Licensed datasets shipped with complete audit trails, validation history, and quality tiers. Each sample includes full provenance and datasets come with defined usage terms for responsible deployment.
Founder
Daniel Morris
Actuarial Science Honours Student, Wits University
Daniel recognized the rapid rise of AI but saw firsthand how students around him struggled with AI's lack of African context. This gap between AI's potential and its ability to understand African languages, accents, and cultural contexts inspired him to start TruVector. As an Actuarial Science student at the University of the Witwatersrand in South Africa, he brings a data-driven perspective to building trusted datasets that help AI systems understand Africa.
Values & principles
Consent-first
Participants opt in by data type. Clear explanations of what is collected and why.
Human-in-the-loop validation
Structured validation workflows filter noise and reduce dataset risk.
Auditability & provenance
Maintain a clear trail from capture through validation. Enables audit-ready datasets.
Respect for culture and context
Capture languages, accents, and environments with the context needed for real-world deployment.
Fair participation and compensation
Transparent consent, fair compensation, and respect for contributors.
How we measure success
Better model performance
Models trained on TruVector datasets perform better in African contexts — languages, accents, and cultural settings.
Lower dataset risk
Teams can deploy with confidence, knowing datasets have clear consent trails, provenance, and validation history.
Transparent consent and traceability
Every data point includes rich metadata: language, region, consent records, age, phenotypical data, validation history, and provenance — enabling audit-ready datasets.
Scalable contribution
Participants contribute fairly and transparently, without exploitation. Reward mechanisms scale responsibly.
Building for Africa? Start with ground truth.
Contact us to discuss dataset requirements, timelines, and design.