Every dataset your Arabic AI pipeline needs — text, audio, image, video, fine-tuning, and red-teaming — annotated by domain specialists and governed to enterprise standards.
Text, audio, image, video, and fine-tuning datasets — all with native-speaker review, dialect coverage, and QA pipelines that match the rigor of the biggest Western labeling programs.
Named Entity Recognition (general + domain), text classification and taxonomy mapping, sentiment and stance, intent detection, keyword/phrase extraction, document span labeling, passage relevance, summarization QA.
Arabic dialect coverage across MSA, Gulf, Levantine, Egyptian, and Maghrebi; English and other target languages; cultural localization; parallel corpus creation; code-switching handling.
Transcription (verbatim or clean), diarization, speaker ID, acoustic event labeling, emotion and tone tags, domain lexicon normalization, QA with WER/CER metrics.
Bounding boxes, polygons, semantic segmentation, keypoints and landmarks, frame-level action and event labels, object tracking, geospatial overlays for satellite and drone imagery.
Prompt-to-ideal-response pairs; task-specific instructions for customer support, legal, finance, healthcare, public sector; multi-turn dialogue authoring with context continuity.
Pairwise comparisons, Likert ratings, ranking across helpfulness, harmlessness, truthfulness, and cultural appropriateness; structured qualitative feedback for reward model training.
Adversarial prompt sets, jailbreak testing, bias and fairness probes, hallucination assessment, PII leakage checks, safety policy calibration against your compliance standard.
Rule-based generators, model-assisted augmentation, privacy-preserving synthesis, scenario simulation for rare or long-tail events you can't collect naturally.
Content sourcing (public or proprietary), cleansing and normalization, deduplication, decontamination, semantic clustering and topical coverage analysis for pre-training.
Intent and entity schemas, slot-filling, persona-based dialogues, escalation flows, knowledge-grounded responses, and retrieval checks for RAG evaluation.
Most labeling vendors handle Arabic by translating to English, annotating in English, and translating back. We don't. Native annotators. Native tooling. Native QA.
Specialized tasks for tokenization alignment, root extraction, and diacritics restoration or verification. Arabic's non-concatenative morphology requires annotators who understand the difference between surface form and morpheme.
Islamic content expertise, local business and legal conventions, sensitive-content handling frameworks. Annotation guidelines are authored by domain experts from the target market, not generic offshore teams.
Gulf/Levantine English intermix, Arabizi and romanized Arabic normalization. The dataset reflects how people actually write in WhatsApp, Slack, and customer tickets — not how grammar books say they should.
Native RTL annotation interfaces and QA procedures. No bidirectional text bugs, no mirrored shortcuts, no layout shifts between annotator view and reviewer view.
A labeling program is only as good as its QA. Ours was built for customers who need to defend every decision in front of a regulator or an auditor — not just pass a vendor scorecard.
Illustrated manuals with edge cases, decision trees, and do/don't examples. Every annotator is trained against the same rubric, and guidelines are versioned and auditable.
Annotator onboarding, gold-set calibration, and periodic drift checks. Anyone whose inter-annotator agreement falls below threshold is retrained or rotated off the project.
Majority vote or consensus scoring, with expert arbitration whenever inter-annotator agreement (IAA) falls below a configurable threshold per task.
Continuous error analysis, guideline updates, and model-in-the-loop improvements. Your evaluation feedback feeds directly into the next batch, not a report nobody reads.
From self-service portal access for teams that want to own the schema, to fully managed programs with dedicated project leads. Pick what matches your team's capacity.
Spin up projects, configure schemas, invite reviewers, track KPIs, and export via API. For teams that want to drive their own labeling program.
Dedicated PM, scalable teams from dozens to thousands of annotators, weekly reporting, SLA commitments, risk logs. For enterprise programs where delivery is non-negotiable.
Pre-annotation and active learning, automated quality checks, human arbitration for edge cases. Faster turnaround without sacrificing the audit trail.
Use cases, acceptance thresholds, and privacy constraints nailed down before a single row is labeled.
Pilot annotation, gold sets, and calibration. Annotators trained against explicit edge cases, not vibes.
Batches, QA gates, dashboards. Per-batch quality reports with IAA scores and common error categories.
Exports, lineage docs, and the final evaluation report. Optional fine-tuning support with your modeling team.
Pilots and proofs of concept.
Multi-modal programs that need breadth.
Regulated and high-scale operations.
Strategy and enablement for in-house teams.
Data handling is the reason most regulated teams avoid labeling vendors. It's the reason they move to us.
Share your scope in a 30-minute scoping call and receive a written proposal within 72 hours — with acceptance criteria, timelines, and IAA targets agreed upfront.