NesAI Nova builds, labels and evaluates the data that frontier and enterprise models depend on. We combine trained African talent, disciplined quality systems and our own delivery platform to produce training data that is accurate, traceable and ready for production use.
Corpora, speech and documents received into a ring fenced workspace.
Entity, intent and segmentation tasks routed to vetted specialists.
Dual pass adjudication scored against gold standard items.
Pairwise ranking and rubric scoring for accuracy, tone and safety.
Versioned datasets released under POPIA and GDPR governance.
Engagements begin with a scoped pilot and scale into managed programmes with fixed quality thresholds, agreed throughput and continuous reporting.
Sourcing and creation of high value training data across text, speech, image and multimodal formats, including low resource African languages, domain specific corpora and synthetic augmentation under strict provenance control.
Precision annotation delivered by trained specialists working to written guidelines, calibrated rubrics and layered quality control, producing datasets that hold up under model training and audit.
Human preference data and structured model evaluation that improve alignment, factual accuracy and safety, from pairwise ranking through to adversarial red teaming and release readiness reporting.
Dedicated AI trainer teams recruited, assessed, trained and managed by NesAI Nova, operating under enterprise governance with transparent throughput, quality and utilisation reporting.
Our in house delivery platform orchestrates task routing, quality scoring, reviewer consensus and client visibility, giving every engagement measurable control from ingestion through to delivery.
Every task entering our operation is routed automatically to a qualified trainer, scored against a rubric, sampled by a reviewer and reconciled through consensus before release. Clients see throughput, agreement scores and exception queues in real time, with a full audit trail retained for every record delivered.
Use case, data definitions, rubrics and success criteria agreed in writing.
A limited batch establishes baseline agreement and throughput.
Dedicated pods run to agreed volumes with layered review.
Sampling, agreement scoring and remediation before each release.
Quality, volume and utilisation reported on an agreed cadence.
Processing aligned to POPIA and GDPR principles, with role based access, confidentiality undertakings and secure client environments.
Fair pay, formal contracts, structured training and career progression for every AI trainer in our operation.
Version controlled guidelines, immutable task histories and reviewer records supporting client and regulatory audit.
Send us your use case and data definitions. We will return a scoped pilot proposal with timelines, quality thresholds and commercial terms.