AI Automation for UAE and Dubai Businesses

At a glance

Time difference

India is 1.5 hours ahead of UAE time, with the working weeks overlapping almost entirely

Language reality

Modern Standard Arabic, Gulf dialect, Arabizi in Latin script and English-Arabic code-switching all appear in the same inbox

Primary channel

WhatsApp dominates customer conversation here, which shapes what the automation can and cannot be

Evaluation

Accuracy measured on a sample of your own historical messages, per language variety, before you commit to a build

Escalation

A designed handover to a human, in the customer's language, is a required feature rather than a fallback

Running cost

Per-message model cost modelled against your real volumes, including retries and long conversations, before commitment

Data handling

Where messages are processed and what is retained is decided explicitly and documented, not left to a provider default

Arabizi and code-switching, which naive systems handle worst

Two further patterns are entirely normal here and break systems that were not designed for them. The first is Arabizi: Arabic written in Latin characters, using digits for sounds Latin script lacks — the 3, the 7, the 2 that appear inside words. It is widespread, informal, and has no standard spelling, so the same word may arrive several different ways from several different customers.

The second is code-switching: a single message containing Arabic and English, often mid-sentence, often with the product name or technical term in English and the rest in Arabic. This is not a customer being unusual, it is how a large part of this market writes, and a system that detects one language per message and routes accordingly will misclassify a substantial share of its traffic.

Handling this well is less about a clever model and more about not making brittle assumptions. Do not detect language once and lock it in. Do not normalise messages in a way that destroys the information. Do measure accuracy separately for formal Arabic, dialect, Arabizi and mixed messages, because a single blended accuracy figure hides exactly the failure you care about — and reply in the language and script the customer used, since answering Arabizi with formal Arabic reads as a machine that did not understand.

What it costs to run, before you commit to building it

The pattern we see most often is an automation that is approved on build cost and becomes a problem on running cost. Model usage is billed per token, and a conversation costs far more than a single exchange: the system prompt is resent, conversation history accumulates, retrieved documents are included, and retries after a failed response cost as much as the original.

So before we build anything we model the running cost against your actual volumes — messages per month, average conversation length, how much context each turn carries, and a realistic retry rate. In AED, monthly, at your real numbers rather than at a demonstration's.

That figure changes decisions, and it should. Sometimes it shows the automation pays for itself several times over and the project is obviously worth doing. Sometimes it shows the cost per conversation approaches what a person costs, at which point the honest recommendation is a better-designed form, a clearer FAQ or a search improvement rather than a model. We would rather have that conversation before you have paid for a build than after.

How the project runs

1. 1

   Start from your real message history

   A sample of actual customer conversations is labelled and categorised by language variety, so the decision to proceed rests on measured accuracy rather than on a demonstration.

2. 2

   Evaluate per variety, not in aggregate

   Formal Arabic, Gulf dialect, Arabizi, code-switched and English are measured separately, because a single blended number conceals the cases that will generate complaints.

3. 3

   Model the running cost in AED

   Monthly cost projected at your real volumes including history, retrieved context and retries, so the commercial decision is made before the build rather than after the first invoice.

4. 4

   Design the escalation before the automation

   When and how a conversation reaches a person, and what context arrives with it, is specified first — because that path is what protects the customer relationship when the model is wrong.

5. 5

   Reply in the language the customer used

   Including script: an Arabizi message gets an answer the customer recognises, rather than formal Arabic that reads as a machine that did not understand.

6. 6

   Monitor with a human reviewing samples

   A regular review of real conversations by someone who reads Arabic, because automated quality metrics do not detect answers that are fluent, confident and wrong.

What you get on every project

• A written scope with fixed milestones before any development starts — no open-ended hourly billing.
• A staging URL you can check at any time, so progress is visible rather than reported.
• One named point of contact, not a ticket queue.
• Invoicing in AED, under Easywork Solutions Private Limited.
• Full source code, design files and hosting credentials transferred to you on final payment.