Arabic presents structural challenges that generic multilingual models handle poorly. Words are built from root-and-pattern morphology rather than simple prefixes and suffixes, script runs right to left, and everyday speech diverges sharply from Modern Standard Arabic depending on the speaker's country or region. Arabic NLP systems are trained directly on native Arabic text, including dialectal and code-switched examples, so they can parse intent and meaning without first converting the input into another language and losing information along the way.
For customer-facing AI in the Gulf and wider MENA region, most real conversations happen in a spoken dialect mixed with English terms, not formal Modern Standard Arabic, and often over WhatsApp. A system that only understands MSA or relies on machine translation will misread intent in everyday requests, which matters directly in regulated sectors like banking and healthcare where misunderstanding an instruction has real consequences, and in measured outcomes like automated resolution rate and CSAT.
In Eshal: Eshal's NLP processes Gulf Arabic, Levantine Arabic, and Egyptian Arabic natively, including code-switched Arabic/English, reducing accuracy loss by up to 24 percentage points compared with MSA-only models, per the AI Customer Experience Benchmark 2026. This dialect-native approach underpins Eshal's automated resolution rates across MENA deployments, since intent detection only works reliably when the underlying model understands how customers actually speak.