Why Arabic is different
Your customers do not write in Modern Standard Arabic. They write in Tunisian, Gulf or Egyptian dialect, mix French and English into the same sentence, and sometimes type Arabic in Latin letters and numbers. An assistant tested on clean standard text looks brilliant in a demo, then stumbles on its first real WhatsApp conversation.
Then there is the interface: right-to-left text, numerals, dates, product names in several languages. A good Arabic experience is not translated at the end; it is designed from the start.
Where an Arabic assistant actually helps
Three uses deliver clear value at low risk. First, answering frequent customer questions such as prices, opening hours, order status and returns, with a hand-off to a person when needed. Second, searching your internal knowledge: procedures, contracts and guides written in Arabic, so the team finds answers in seconds. Third, extracting data from Arabic forms and documents into your systems.
What we advise against at first: letting the assistant make decisions that touch money, health or reputation without human review.
Data first, then the model
The strongest model cannot make up for data that does not look like your customers. Start by collecting a sample of real conversations with personal data removed, and label them by intent: price question, complaint, booking. That sample becomes your real benchmark for comparing models.
Ground the assistant's answers in your own sources, your FAQ, catalogue and policies, instead of letting it improvise. When it cannot find the answer there, it should say so plainly and pass the conversation to a person.
Trust and ethics are part of the product
Tell people clearly that they are talking to an automated assistant, and always leave an easy path to a real person. Do not collect data you do not need, and do not reuse conversations for purposes customers did not agree to.
Some limits belong in the design itself: no religious rulings from a machine, and no medical or financial advice presented as certainty. An assistant that is honest about its limits earns more trust than one that claims to know everything.
How to start in one month
Week one: gather fifty real conversations and find the ten most frequent intents. Week two: write approved answers for those intents from your own sources. Week three: launch a pilot with a person reviewing replies. Week four: measure what changed in response time and satisfaction, then decide whether to expand.
At HOLMENA we build this kind of assistant in Arabic, French and English, and we work alongside MARSAD, an Arabic NLP research project. If you would like us to review your conversations and propose a first version, talk to us.
Written by
Mohamed Rashad Rouine
Founder & Project Manager
Oversees strategic direction, operations and R&D.