About This Service
Arabic NLP and Arabic LLM Development for UAE Businesses
Most NLP stacks are built for English and quietly fall apart on Arabic. I build Arabic-first language solutions — sentiment analysis, named-entity recognition, classification, chat and search — that handle both Modern Standard Arabic and Gulf dialect, because a Dubai customer typing a WhatsApp complaint in Emirati dialect looks nothing like an MSA press release. Models are selected and tested on the actual register your UAE audience writes in, not on benchmark Arabic alone.
For generative work I use Arabic-first LLMs such as Jais (developed in Abu Dhabi) and AceGPT alongside multilingual models like Qwen, picking per task based on Arabic fluency, cost and hosting constraints. Arabic brings tokenization quirks that silently degrade quality — orthographic variants like alef and ta-marbuta forms, optional diacritics, and tokenizers that fragment Arabic words into far more tokens than English, inflating cost and breaking length limits. I normalise text, benchmark token counts per model, and tune prompts and preprocessing around these issues rather than discovering them in production.
Delivery covers the full pipeline: right-to-left text handling end to end (storage, APIs, mixed Arabic-English strings, UI rendering), evaluation sets labelled for both MSA and dialect, and integration into your product. Typical projects for Dubai, Abu Dhabi and Sharjah companies include Arabic sentiment dashboards over Google and social reviews, bilingual customer-support chat, NER for compliance and onboarding documents, and Arabic search that actually retrieves what users meant — built for mainland and free-zone businesses serving Arabic-speaking customers.
What's included
- MSA and Gulf dialect coverage — Models tested on Emirati and Gulf dialect text, not just formal Modern Standard Arabic.
- Arabic-first model selection — Jais, AceGPT or multilingual alternatives benchmarked on your real data before committing.
- Sentiment and NER pipelines — Production-ready classification, sentiment scoring and entity extraction for Arabic documents and messages.
- Tokenization and normalisation layer — Alef/ta-marbuta normalisation, diacritic handling and token-cost benchmarking baked into preprocessing.
- RTL-safe integration — Right-to-left and mixed Arabic-English text handled correctly through storage, API and UI layers.
- Labelled Arabic evaluation set — A reusable test set in your domain so future model swaps can be measured, not guessed.
How it works
- 1Language audit
I sample your real Arabic content — reviews, chats, documents — and identify the MSA/dialect mix and failure points of your current setup.
- 2Model benchmarking
Candidate models (Jais, AceGPT, multilingual LLMs) are scored on your data for accuracy, token cost and latency.
- 3Pipeline build
I build the preprocessing, model layer and APIs, with RTL handling and Arabic-English code-switching covered.
- 4Validation and handover
Results reviewed against the labelled eval set with a native-reader pass, then deployed with documentation.
Why work with me
| With me | Typical agency | |
|---|---|---|
| Gulf dialect tested, not just MSA | ||
| Arabic-first models (Jais, AceGPT) evaluated | English LLM + translate | |
| Tokenization cost benchmarked per model | ||
| RTL and mixed-script bugs handled upfront | patched later |