Trusted training data for serious AI.
Annotation, labeling, evaluation, and RLHF pipelines delivered by ADBOK-credentialed teams under defined SLAs. We work with AI labs, model providers, and enterprise AI teams who need consistent, audit-ready data — not crowdsourced guesswork.
Multi-modal data operations for the full AI lifecycle.
Whether you're training a foundation model, evaluating a generative system, or building a domain-specific classifier, the quality of your data sets the ceiling for your model. We embed inside your pipeline as a trusted external operations team — calibrated to your guidelines, governed by your QA standards, and accountable to measurable accuracy thresholds.
Our delivery teams are recruited through the Annotator Academy, certified against the ADBOK Framework, and operated under structured QA with multi-layer review. Every batch ships with audit trails, error analysis, and inter-annotator agreement metrics.
Text annotation & entity extraction
NER, relation extraction, intent classification, sentiment, summarization gold sets, and instruction tuning corpora across 19+ languages.
Image & document labeling
Bounding boxes, polygons, semantic segmentation, key-point annotation, OCR ground truth, and visual reasoning datasets.
RLHF & preference data
Response ranking, pairwise comparisons, harmful output flagging, and instruction-following evaluation with calibrated raters.
Evaluation & red-teaming
Hallucination detection, bias and safety assessments, capability evaluations, and adversarial testing under structured rubrics.
Dataset validation & QA
Gold-standard sampling, blind double-blind review, inter-annotator agreement, and continuous calibration drift monitoring.
A QA model built for auditability.
Every workflow ships with defined accuracy thresholds, multi-layer review, calibration sets, and full reviewer-level audit trails. Gold-standard items are seeded into production batches to detect drift early. Disagreements are escalated through a structured adjudication path — never silently resolved. You receive accuracy reporting on every delivery, not just at the end.
AI teams that can't afford bad data.
Foundation Model Labs
Pre-training corpora curation, multilingual fine-tuning data, and RLHF preference collection at scale.
AI Platforms & APIs
Continuous evaluation, regression detection, and human-in-the-loop pipelines for production model monitoring.
Enterprise AI Teams
Domain-specific labeled datasets for in-house models — legal, medical, financial, multilingual support, and regulatory.
Computer Vision
Polygon and segmentation for autonomous systems, agriculture, retail analytics, and document-heavy verticals.
Speech & Audio
Transcription, diarization, accent labeling, and emotion annotation across African and global languages.
Safety & Alignment
Red-team data, harmful output classification, jailbreak resistance, and policy compliance evaluation.
Set the data floor for your next model release.
Book a 30-minute scoping call. We'll talk taxonomy, throughput, quality bar, and pricing — no commitment.