What we learn building frontier-quality Arabic data — written for the teams shipping Arabic models into production. No fluff, no vendor theater: dialects, compliance and evaluation, treated with the seriousness they deserve. اقرأ بالعربية ←
An interactive, local-first crash test for what Arabic agents actually did—tool by tool, state by state.
78% on binary toxicity becomes 25.3% on the real taxonomy—and 22.4% across five dialect regions. The reproducible audit.
Arabic speech synthesis datasets compared: hours, speakers, human vs synthetic audio, diacritics, licence and voice consent per open corpus, and our #1 pick.
A 10-point audit for Arabic image and video datasets: regional representation, Arabic text legibility, label consistency, task fit, consent and test leakage.
A 10-point audit for Arabic OCR and document extraction datasets: reading order, numerals, tables, field accuracy, label agreement and licensing.
Arabic text corpus options for LLM training compared: FineWeb2, ArabicWeb24, 101B Words, CulturaX, OSCAR, arTenTen and Bayanat Labs by size, licence and rights.
Arabic function calling dataset guide: public Arabic tool-calling sets compared on size, dialects, provenance, licence and multi-step coverage.
Arabic telephony speech data for voicebots: license, simulate or record? Vendor call-centre sets compared, 8 kHz specs, Saudi PDPL and our #1 pick.
Commercial Arabic datasets compared: why Bayanat Labs is our #1 pick, plus Defined.ai, Shaip, Appen, LDC and ELRA by hours, dialect, licence and price.
Gulf Arabic dataset guide: Khaleeji text and speech corpora with years and licences, the freshness gap, and why Bayanat Labs is our #1 Gulf data partner.
Best Arabic data annotation companies for 2026: Bayanat Labs ranked #1, then 12 vendors and dataset sources compared on dialects, RLHF, location and pilots.
HUMAIN M3 (humain-m3) explained: the 428B Arabic model built on MiniMax-M3, its seven self-reported benchmarks, access tiers and weights status.
Arabic AI agent evaluation that grades tool calls and final system state, not fluent replies: Arabic-specific risks, test-suite design and a scorecard.
Arabic annotation guidelines template: 12 copy-ready sections with dialect, diacritics and Arabizi rules, Arabic examples, decision trees and a pilot plan.
Everything we have written on Arabic AI data, grouped by the problem it solves.
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