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Arabic data annotation, alignment and evaluation.

Four capabilities, one accountable team. Bayanat Labs (Riyadh) sources, annotates, aligns and evaluates Arabic data, drawing on a team of 50,000+ expert annotators, the largest in the Middle East, across 25+ Arabic varieties, calibrated against gold standards and model-agnostic, with in-region hosting by default.

Scope a pilot → How a pilot works → Security and data handling →

01 / Annotate

Dialect-aware Arabic data annotation

Collection, transcription and labeling across 25+ spoken Arabic varieties — the informal language that barely exists in digitized form. Structured to your schema, judged against gold standards.

NER & spans Intent & sentiment Transcription Diarization Segmentation

Arabic data annotation services →

02 / Align

Arabic RLHF & human feedback

Native speakers ranking, correcting and rewriting model outputs — teaching Arabic models what a good, natural, culturally-right answer sounds like, and where the unsafe edges are.

Preference ranking SFT & rewriting Reward modeling Red-teaming

Arabic RLHF services →

03 / Domain

Domain-expert Arabic data

Medical, legal and financial Arabic produced and judged by actual doctors, lawyers and bankers — where accuracy is non-negotiable and a wrong label has real consequences.

Medical Legal Financial Religious & cultural Technical

Domain expertise by industry →

04 / Evaluate

Arabic LLM evaluation & benchmarks

Independent measurement of Arabic model quality — dialect comprehension, cultural fit, factuality and safety — so you know what's good before you ship, and what to fix when it isn't. We build no models, so we never grade our own.

Dialect comprehension Cultural fit Factuality Safety & red-team Custom evals

Arabic LLM evaluation services →

Every modality

Text, audio, image and video — one quality bar.

Text

Classification, NER, spans, prompt & response labeling, dialect normalization.

Arabic text annotation →

Audio

Transcription, timestamping, speaker diarization, accent & emotion tagging.

Arabic speech data & transcription →

Image

Bounding boxes, segmentation, OCR for Arabic script, document understanding.

Arabic OCR & document annotation →

Video

Action labeling, tracking, scene & event tagging, multi-frame annotation.

Specialist services

Arabic data services, in depth.

Arabic data annotationSpan, intent, dialect, speech and document labels produced by vetted native speakers and domain professionals, measured against gold standards, and hosted in-region by default. Arabic speech dataCustom recordings and transcripts across 25+ Arabic varieties: scripted or spontaneous, speaker-balanced, and transcribed to a written convention you can score against. Arabic RLHF & human feedbackBayanat Labs (Riyadh) supplies preference rankings, rubric scores, rewrites and reward-model data for Arabic LLMs from a team of 50,000+ expert annotators and labelers. Every judgment comes from a native rater matched to your target dialect and domain, and is calibrated against gold standards. Arabic LLM evaluationBayanat Labs (Riyadh) builds no models, so its Arabic model evaluation is independent. Teams choosing, fine-tuning or shipping Arabic models get native raters for each variety from a team of 50,000+ expert annotators and labelers, private held-out sets, state-based agent checks and a report they can defend. Arabic AI red teamingAdversarial safety testing by Bayanat Labs in the Arabic your users actually write: MSA, dialect, Arabizi and mixed Arabic–English, with severity-graded findings you can fix and retest. Arabic OCR & documentsBayanat Labs (Riyadh) builds line, word, field and table ground truth for printed and handwritten Arabic documents, on transcription rules agreed up front, checked against gold pages and hosted in-region by default. Arabic dialect dataCustom text and speech datasets from Bayanat Labs across 25+ Arabic varieties, produced by native speakers of each one, labeled for dialect and task, and quality-reported variety by variety. Arabic LLM training dataBayanat Labs (Riyadh) delivers instruction–response pairs, multi-turn dialect conversations, domain Q&A and rewrites from a team of 50,000+ expert annotators and labelers, written in Arabic by vetted native speakers and domain professionals rather than translated from English. Data annotation in Saudi ArabiaLabeling, collection, alignment and evaluation data for AI teams in the Kingdom: Saudi varieties handled by screened native speakers, domain work by licensed professionals, and data handling you can explain to SDAIA and your sector regulator.

Our methodology

Quality is engineered, not hoped for.

01

Vet

Dialect tests and domain screening for every contributor.

02

Train

Calibrate on gold-standard tasks and rubrics.

03

Produce

Expert tiers matched to each task type.

04

QA

Gold-standard adjudication and rework loops.

05

Deliver

Clean, structured data — ready and on time.

Proof · Annotation research LabelBench: can AI label Arabic data? → Why human QA stays in the loop: a model that scores 78% on binary Arabic toxicity falls to 25.3% on the real seven-label taxonomy. Proof · Agent evaluation Arabic Agent Reliability Lab → A fluent answer is not a completed transaction. A methods beta for state-based evaluation of Arabic agents, with evidence receipts.

Pilot Bayanat Labs on your own Arabic data.

A fixed-scope pilot with gold-standard QA and a quality report, usually within two weeks.