Bayanat Labs press kit
Everything you need to write about Bayanat Labs and our research: a boilerplate, key facts, the LabelBench findings, charts and logos. For interviews and data questions, email hello@bayanatlabs.com.
About Bayanat Labs
Short boilerplate. Bayanat Labs is a Riyadh-based Arabic data engine for AI. We source, annotate, align and evaluate Arabic data across 25+ dialects: text, speech, image and video. Every label comes from vetted native speakers and domain experts, is measured against gold standards, and is kept in-region by default.
Long boilerplate. Bayanat Labs builds the human data that makes Arabic AI work. The company does data only and is model-agnostic: dialect-aware annotation, human feedback and RLHF, domain-expert data (medical, legal, financial), and independent Arabic LLM and agent evaluation. The work is done by vetted native speakers and licensed professionals across 25+ Arabic varieties. Contributors pass dialect and domain screening, work is calibrated against gold standards, and every task is adjudicated and audited. Data stays in-region by default, with on-prem and private-cloud options. Its open research includes LabelBench, an audit of whether AI can label Arabic data.
Key facts
| Name | Bayanat Labs (please use the full name; “Bayanat” alone refers to other organisations) |
| Headquarters | Riyadh, Saudi Arabia |
| What we do | Arabic AI training data and evaluation: data sourcing, annotation and labeling, human feedback (RLHF), domain-expert data, LLM and agent evaluation, red-teaming |
| Coverage | 25+ Arabic varieties; text, speech, image and video |
| Data handling | In-region by default; on-prem and private-cloud options |
| Research | LabelBench (July 2026) and the Arabic Agent Reliability Lab (methods beta) |
| Website | www.bayanatlabs.com |
| linkedin.com/company/bayanat-labs | |
| Media contact | hello@bayanatlabs.com |
LabelBench in four numbers
LabelBench asks when Arabic labeling can safely move from people to models. We ran one fixed zero-shot rubric on identical held-out samples and compared the models with small supervised baselines.
- 78% → 25.3%. A local language model (Qwen 2.5 3B) scored 78% on binary Arabic toxicity. On the real seven-label taxonomy, exact-match accuracy fell to 25.3% on the fixed held-out manifest.
- 22.4%, where chance is 20%. The same model reached 22.4% when asked to identify five Arabic dialect regions.
- 75% of Gulf examples labeled Egyptian. Its errors were not random: it labeled 70–80% of each non-Egyptian region's examples as Egyptian.
- Specialization and native data help. Arabic-specialized Fanar 1 9B did better on all three tasks (47.8% on dialect region), and a character n-gram model trained on 100 examples per region reached 69.8%.
The paper, results (CSV and JSON) and checksums are public; see how to cite or embed LabelBench.
Charts
Free to use with the credit “Source: Bayanat Labs, LabelBench v0.1” and a link to bayanatlabs.com/research/labelbench.
Story angles
- Can AI replace Arabic data annotators? Why a model that looks competent on yes/no labels collapses on real annotation work.
- The dialect blind spot. Why a model can mistake Gulf, Levantine, Iraqi and Maghrebi Arabic for Egyptian, and what that means for Arabic chatbots and moderation.
- Sovereign AI needs sovereign data. Why in-region, native-speaker data and evaluation matter for Gulf AI programs. Background: PDPL and Arabic AI training data.
- The cost of Arabic in AI. The tokenization tax: why the same content can cost more to process in Arabic.
Logos
- Logo mark, PNG, 512×512
- Logo mark, SVG (for light backgrounds)
- Logo mark, SVG (for dark backgrounds)
Please don't recolour or stretch the mark. The wordmark is set as “Bayanat Labs”.
Contact
Interviews, data questions and corrections: hello@bayanatlabs.com. For partnerships or a pilot, use the contact form.