Generative AI & Arabic Localization
Overview
Arabic-first generative AI, tuned to your culture, dialect, and domain.
The generative AI revolution is happening primarily in English. Most large language models are trained predominantly on English-language data, evaluated against English benchmarks, and deployed with English-language alignment and safety frameworks. When these models are deployed in Arabic-speaking markets, the result is AI that is technically capable but culturally misaligned, producing outputs that feel translated rather than native, that miss the register and tone appropriate to Gulf audiences, and that create real compliance risk in regulated sectors.
Our Generative AI & Arabic Localization practice exists to close this gap. We develop and adapt generative AI solutions that are genuinely Arabic-first, collecting and curating Arabic-language corpora across dialects, building fine-tuned models using LoRA and QLoRA techniques, developing RAG pipelines grounded in your specific domain knowledge, and running the cultural quality assurance and safety evaluation that global vendors skip.
Why choose our Generative AI & Arabic Localization service?
Arabic is the native language of 420 million people and the lingua franca of every significant market in the Gulf. For organizations serving these markets, deploying English-first AI with a localization layer is not equivalent to deploying genuine Arabic-first AI, the difference is felt immediately by users in the quality of language, the appropriateness of cultural references, and the confidence with which the system handles domain-specific terminology.
UXBERT’s Arabic AI capability is built on more than a decade of working with Arabic-language data, content, and user experience in the Gulf region. We understand the difference between MSA, Khaleeji, Hejazi, and Egyptian dialects as they present in customer interactions, and we build models that handle this variation rather than collapsing everything into formal written Arabic that few customers actually speak.
Our governance approach is equally distinctive. Every model we fine-tune or deploy is evaluated against a Safety & Governance Scorecard covering toxicity, bias, hallucination, and PII/compliance dimensions. We run red-teaming and jailbreak testing as standard. And we produce the audit-ready documentation that your legal, compliance, and data protection teams will need when regulators ask questions.
Discover more about our digital services get to know our expert team.
Our Process
Corpus Collection, Curation & Use Case Scoping
Structured dialogue to identify generative AI use cases, content generation, document summarisation, classification, extraction, conversational AI, knowledge retrieval, or domain-specific generation. Arabic corpus collection across relevant dialects and domains, with cleaning, labelling, and quality scoring applied before any model work begins. Prompt Engineering Playbook drafted covering core task patterns for your use case portfolio.
RAG Architecture & Fine-tuning Design
Technical architecture design, balancing RAG with Arabic-capable vector stores for knowledge-intensive use cases and fine-tuning (LoRA, QLoRA, or full fine-tune) for tasks requiring deep domain or dialect adaptation. Model selection framework applied: evaluating base Arabic LLMs, multilingual models, and proprietary frontier models against task requirements, latency constraints, and governance obligations.
Fine-tuning, Evaluation & Cultural QA
Model fine-tuning using curated Arabic corpus, evaluated against automated benchmarks (BLEU, ROUGE, custom Arabic metrics) and human evaluation by native Arabic speakers with domain expertise. Cultural QA applied systematically, reviewing outputs for register appropriateness, dialectal accuracy, brand tone alignment, and cultural sensitivity. Safety evaluation covers toxicity, bias, hallucination, and PII detection, with red-teaming applied to identify jailbreak vulnerabilities.
Deployment, Governance Documentation & Monitoring
Production deployment with monitored endpoint and a Model Refresh & Monitoring Plan defining the cadence and triggers for model updates. Complete Safety, Governance & Audit documentation package covering model lineage, training data provenance, evaluation results, known limitations, and governance controls, satisfying SDAIA, PDPL, and NDMO requirements. Ongoing monitoring tracks output quality, safety metrics, and usage patterns.
FREQUENTLY ASKED QUESTIONS
Which Arabic dialects does your generative AI support?
What is the difference between RAG and fine-tuning, and which does our use case need?
How do you handle hallucination and accuracy risks in Arabic generative AI?
What compliance documentation do you produce?
Can you fine-tune models on our proprietary data?
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