Most Arabic speakers use a regional dialect in everyday conversation and reserve Modern Standard Arabic for formal writing and broadcast media, so a model trained only on MSA text encounters vocabulary, grammar, and phrasing it was never trained on the moment a real customer starts typing. Dialect NLP addresses this by training directly on Khaleeji, Shami, and Masri text and speech, including the code-switching with English common in everyday MENA messaging, so intent detection reflects how people actually communicate.
The consequence of skipping dialect-native training shows up directly in performance: MSA-only models misread intent far more often on dialect than dialect-native models do. For a business running customer service over WhatsApp across the UAE, Saudi Arabia, Egypt, and the Levant, that gap translates directly into lower automated resolution rates, more escalations to human agents, and lower CSAT in regulated sectors like banking and healthcare where miscommunication carries real consequences.
In Eshal: Eshal's models are trained natively on Gulf, Levantine, and Egyptian Arabic dialects rather than relying on Modern Standard Arabic alone, closing the accuracy gap that MSA-only models show on real dialectal conversations. This dialect-native foundation, combined with support for code-switched Arabic and English, underpins Eshal's automated resolution rates across banking, healthcare, retail, logistics, and government deployments.