Arabic data, ready to train on.
Data collection, labeling and annotation, and dataset curation for Arabic AI training.
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- Collection
- Labeling and annotation
- Dataset curation
Data for every kind of model.
Text
Named entity recognition, classification, sentiment, intent, keyword extraction, document span labeling and summarization QA.
Audio and speech
Verbatim or clean transcription, diarization, speaker ID, acoustic events, and emotion and tone tags.
Vision
Bounding boxes, polygons, semantic segmentation, keypoints, object tracking and geospatial overlays.
Model training
Instruction tuning, RLHF and RLAIF preference data, red teaming, synthetic data, corpus curation and conversational datasets.
Every major Arabic dialect.
Modern Standard Arabic, Gulf, Levantine, Egyptian and Maghrebi, plus English and other target languages. Code-switching and Arabizi are normalized.
How a data project runs.
A pilot takes 2 to 4 weeks. Scale-up runs in sprints.
Scope
Agree the task and the success criteria.
Design
Write the schema and guidelines, then run a pilot annotation.
Produce
Annotate at scale in batches, with QA gates between them.
Hand over
Exports, integration and an evaluation report.
Quality and care, built in.

Quality
Quality you can measure.
Illustrated guidelines, annotator training and calibration, multi-annotator validation with majority voting, and inter-annotator agreement thresholds.
Security
Data you can trust us with.
Encryption in transit and at rest, segregated VPCs, GDPR-aligned processing with MENA or EU residency, vetted staff and PII redaction.
Getting started
Start with a scoping call.
A 30-minute call, with a proposal within 72 hours.
Talk to our teamWork with us the way that suits you.
Run projects yourself through T-Portal.
A dedicated project manager runs the program end to end.
Human annotation with AI pre-annotation and active learning.
Make your next ambition operational.
Bring intelligence into your knowledge, people and operations. Talk to our team about the work you want to transform.

