At its core, conversational AI combines natural language understanding, which interprets what a person means, with dialogue management, which tracks what has already been said so the system can respond coherently across many turns rather than treating each message in isolation. Simpler implementations retrieve an answer from a knowledge base and stop there. More advanced implementations, often described as agentic, connect that same conversational layer to external tools and systems so the dialogue can end in a completed task rather than just an answer.
The label covers a wide quality range, which matters when evaluating vendors for MENA deployments: a conversational AI system that only understands Modern Standard Arabic or English will struggle with the dialects and code-switching common on WhatsApp, the region's dominant customer contact channel. Businesses in regulated sectors such as banking and healthcare need to look past the general term and assess whether a system maintains context reliably, understands local dialect, and integrates with the systems it needs to act on.
In Eshal: Eshal's conversational AI layer is built specifically for MENA customer interactions, maintaining context across a conversation in Gulf, Levantine, and Egyptian Arabic as well as English and code-switched messages. Rather than stopping at a dialogue response, it is connected to an agentic layer that can look up account details, update records, and complete workflows, so a conversation can end in resolution rather than a simple answer.