NLP combines several sub-tasks that together let a system make sense of a message: intent detection identifies what the customer wants, entity extraction pulls out specific pieces of information like an order number, date, or product name, and sentiment analysis gauges the customer's emotional tone, such as frustration or urgency. These outputs feed into the system's decision about how to respond or which workflow to trigger, and are also what allow generated responses to sound natural rather than robotic or templated.
NLP quality is especially decisive in MENA customer service because it has to work reliably across Modern Standard Arabic, regional dialects like Gulf and Levantine Arabic, Arabizi, and code-switched Arabic-English messages, not just one clean language register. Weak NLP shows up as missed intents, wrongly extracted order numbers or dates, or a sentiment analysis system that cannot detect frustration expressed in colloquial Arabic, all of which drive unnecessary human escalation and undermine metrics like first contact resolution across WhatsApp and other channels.
In Eshal: Eshal's NLP layer is built to handle the full mix of language MENA customers actually use - Modern Standard Arabic, Gulf and Levantine dialects, Arabizi, and Arabic-English code-switching - across intent detection, entity extraction, and sentiment analysis. This dialect-aware approach supports accurate routing and natural-sounding responses on channels like WhatsApp, and underpins Eshal's reported first contact resolution and overall average resolution performance.