Arabic AI Training Data
El Turco builds Arabic datasets for speech, NLP and LLM projects. Native speakers of each dialect collect, label and review the data under guidelines written with your team.
- Languages
- 19
- Languages
- Vetted Native Linguists
- 700+
- Vetted Native Linguists
- Localization Tasks / Year
- 100K+
- Localization Tasks / Year
- Founded
- 2016
- Founded
El Turco provides Arabic AI training data for enterprise AI teams, model builders and language service providers. Native Arabic speakers collect speech, annotate text, rank model responses and build parallel corpora across Modern Standard Arabic and the Gulf, Levantine, Egyptian and Maghrebi dialects. Projects run from our production office in Istanbul and headquarters in London under ISO/IEC 27001 information security.
AI training data is the speech, text and image material a model learns from, together with the labels that say what each item means. For Arabic, the hard part is coverage. People speak and write dialects that differ from MSA, switch into English or French mid-sentence and type Arabic in Latin letters. Our first clients are enterprise teams that train or evaluate their own models. Our second are LSPs and data vendors that need a native Arabic annotation team inside their own workflow.
Shared workflow: AI Data Solutions.
Scope
What does Arabic AI training data cover?
Speech data collection
Scripted and spontaneous recordings from native speakers by dialect, age, gender and acoustic environment, delivered as WAV or FLAC at 16 to 48 kHz with speaker and environment metadata.
Transcription
Time-aligned transcripts in TextGrid, SRT or VTT, written to an agreed convention for dialect spelling, code-switched words and non-speech events.
Text annotation
Intent, sentiment, named entity, dialect identification and classification labels for chatbots, search and NLP models, delivered in JSONL, CSV/TSV or your own schema.
RLHF and model evaluation
Native linguists rank and rate Arabic model responses, score factual accuracy and cultural fit and write preferred answers for instruction tuning.
Content moderation data
Hate speech, toxicity, misinformation and sentiment labels that account for regional slang, sarcasm and cultural references that generic models misread.
Custom MT and LLM data
Parallel corpora, domain-specific datasets and evaluation sets for fine-tuning machine translation engines and large language models on your terminology.
Arabic specifics
What makes Arabic different for AI training data?
Dialect coverage
MSA is the written standard, but daily speech and social media use Gulf, Levantine, Egyptian and Maghrebi Arabic. Vocabulary, pronunciation and word order differ. We recruit speakers and annotators per dialect and label every item with its dialect so the dataset can be balanced and filtered.
Arabizi and code-switching
Users often write Arabic in Latin letters with digits standing in for some sounds, such as 3 for the letter ain. Maghrebi speakers mix in French and Gulf speakers mix in English. The guidelines state whether to keep, tag or transliterate each case.
Spelling variation
The same word can appear with or without short-vowel diacritics, with different alef and hamza forms or with ta marbuta written as ha. We agree a normalization policy per project and keep the raw text next to the normalized version when the client needs both.
Clitics and entity spans
Arabic attaches conjunctions, prepositions and the definite article to the word, as in wa, bi and al. Annotators need a written rule on whether an entity span includes these prefixes. Without that rule, NER labels drift between annotators.
Right-to-left text in data files
Arabic must be stored in logical order as UTF-8, not in visual order. Lines that mix Arabic with Latin brand names, URLs and numbers need consistent bidirectional handling. We check that JSONL and CSV exports open correctly in your tools before delivery.
Digit styles
Arabic text may use Eastern Arabic or Western digits, and Maghrebi sources usually use Western digits. The guidelines record whether to keep digits as written or normalize them, since mixed digit styles affect tokenization and evaluation.
Process
How we run an Arabic AI training data project
Turnaround is agreed at scoping, with a 24-hour option for small urgent updates.
Step 1
Scoping and free pilot
We review the use case, data types, dialect mix, volume, formats and acceptance criteria. A free pilot batch tests the guidelines and the team before production.
Step 2
Guidelines and calibration
Annotation guidelines are co-developed with you and versioned per batch. Calibration rounds align annotators on hard cases such as dialect labels, Arabizi and entity spans before production starts.
Step 3
Native collection and annotation
Vetted native speakers of each dialect collect and label the data. Every linguist and annotator signs an NDA, and contributor consent is recorded for speech collection.
Step 4
Layered quality control
Gold tasks are seeded throughout production and annotators are tracked against them. Inter-annotator agreement is measured and reported per delivery, with WER spot-checks on transcription against criteria agreed up front.
Step 5
What's Wrong? QA for text
Parallel corpora and translated evaluation sets follow ISO 17100, with ISO 18587 applied where post-edited MT output is in scope. Every text delivery then passes through What's Wrong? QA, our own tool, which checks terminology, numbers and consistency.
Step 6
Secure delivery
Data moves via secure transfer, API or your own platform, encrypted in transit and at rest, with separate processing environments per client under ISO/IEC 27001 and GDPR-aligned handling.
Industries
Who buys Arabic ai training data?
Industry
Technology & IT
AI and ML teams at SaaS companies and enterprise software vendors training Arabic speech, chatbot and LLM features, plus LSPs and data vendors that need native Arabic annotators under their own brand.
Industry
E-Commerce & Retail
Marketplaces and retailers that need Arabic search relevance, product classification and trust and safety moderation data for Gulf and wider MENA markets.
Industry
Finance & Fintech
Banks and fintech teams building Arabic intent detection for customer service chatbots and voice channels across several dialects.
Related
Language page
Arabic translation and localization
Modern Standard Arabic and regional dialects for software, games, e-commerce and finance, with RTL testing and Arabic AI training data.
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Arabic Game Localization
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Arabic E-Commerce Localization
Arabic product catalogs, right-to-left storefronts, checkout and campaigns for Gulf, Egyptian and wider Arabic markets, with MTPE for volume.
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Arabic Software Localization
Arabic UI strings, right-to-left layout review, help content and release notes, delivered through continuous localization for SaaS and enterprise software.
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Turkish AI Training Data
Turkish speech collection, text annotation, RLHF, moderation and MT training data from native speakers, with gold tasks and agreement reporting.
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Persian AI Training Data
Farsi speech, annotation, RLHF, moderation and MT datasets built by native speakers, with Persian character normalization and agreement reports.
FAQ
Arabic ai training data FAQ
Which Arabic dialects can El Turco cover for AI training data?
El Turco covers Modern Standard Arabic plus the Gulf, Levantine, Egyptian and Maghrebi dialect groups for speech collection, transcription and text annotation. Native speakers of each dialect handle that dialect, and every item carries a dialect label so the dataset can be balanced or filtered. Dialect mix and quotas are agreed during scoping and checked in the free pilot batch.
How does El Turco handle Arabizi and code-switching in Arabic datasets?
Arabizi and code-switching are handled through written rules in the annotation guidelines. The guidelines state whether Latin-script Arabic is kept, tagged or transliterated and how English or French words inside Arabic sentences are labeled. Annotators practice on these cases in calibration rounds before production, and gold tasks check that the rules hold in every batch.
What data formats does El Turco deliver for Arabic AI data?
El Turco delivers Arabic text data as UTF-8 JSONL, CSV/TSV, XLIFF or your custom schema, with Arabic stored in logical order. Recorded speech comes as WAV or FLAC at 16 to 48 kHz, paired with speaker and environment metadata. Transcripts are time-aligned and delivered as TextGrid, SRT or VTT. RLHF rankings and ratings are exported in the format your pipeline expects.
How does El Turco measure quality on Arabic annotation projects?
Quality on Arabic annotation projects is measured against acceptance criteria agreed before production. Gold tasks are seeded throughout, annotators are tracked against them and inter-annotator agreement is reported with each delivery. Transcription receives WER spot-checks. Translated corpora follow ISO 17100 and pass What's Wrong? QA. Guidelines are versioned per batch, so every change in labeling rules stays traceable.
Can LSPs and data vendors outsource Arabic annotation to El Turco?
Language service providers and data vendors can outsource Arabic speech collection, transcription, annotation and RLHF work to El Turco on a white-label basis. Our annotators work in the client platform or the LSP tooling and follow the end client guidelines. El Turco runs a free pilot batch before the first order. Pricing is per-word, hourly or project-based, quoted after scoping.
How does El Turco keep Arabic speech and text data secure?
El Turco keeps Arabic speech and text data secure under its ISO/IEC 27001 certified information security management system. Data is encrypted in transit and at rest, each client has a separate processing environment and every linguist and annotator signs an NDA. Handling is GDPR-aligned, contributor consent is recorded for speech collection and access to data is traceable.
Ready to Scope Arabic AI Training Data?
Share the content type, volume and timeline for a scoped plan. A free pilot comes first if you want proof before committing. Pricing is per-word, hourly or project-based, quoted after scoping.