Customer support · Solution
Soluvide builds bilingual AI support agents for UAE businesses that genuinely handle Arabic and English — including Gulf dialect, Arabizi, and messages that switch script mid-sentence. Answers are grounded in your own knowledge base, kept on-topic by guardrails, and escalated to a human on low confidence. Every agent passes a dedicated Arabic and mixed-script validation layer before it ships.
The problem
What the day actually looks like — described as an operator would, not as a brochure.
A customer messages your support line in Arabic. Not the textbook Modern Standard Arabic from a course — real Gulf dialect, half of it typed in Latin letters and numbers because that is how people actually text. One sentence is Arabic, the next is English, and a product name sits in the middle in a third register. Your chatbot, demoed and signed off entirely in clean English, has never really seen anything like it.
So it does the worst possible thing: it answers confidently and wrongly. It misreads the intent, retrieves the wrong article, or replies in stiff, literal Arabic that any native speaker can tell was written by a machine. In English the same bot looks polished. The failure is invisible to the people who approved it, because they tested in the one language it happens to be good at.
Underneath, several things break at once. Gulf Arabic differs from the Arabic most models see most of. Code-switching — Arabizi and mixed Arabic/English inside a single message — confuses systems built to assume one language per turn. Right-to-left text renders incorrectly when Arabic, English, numbers, and punctuation share a line. And dialect variation across the Gulf means the same question arrives in dozens of shapes.
The result is a support experience that quietly collapses for a large share of your customers while your English metrics stay green. People stop trusting the bot, escalate everything to already-stretched human agents, or simply leave. It is not a dramatic outage — it is a slow erosion of service that nobody's dashboard is measuring.
Why the obvious fixes fail
None of them are stupid. They fail because the work itself stays manual — they just move who does it.
Bolting a translation API onto an English bot translates words, not meaning. Gulf expressions come out literal and culturally wrong, politeness registers get flattened, and idioms turn into nonsense. The customer reads a reply that is technically Arabic and obviously not written for them.
Many models and pipelines are tuned for English first. They mis-tokenise Arabic, mishandle right-to-left rendering, and truncate or garble mixed-script input before the reasoning even starts. The agent isn't answering your customer's question — it's answering a mangled copy of it.
Rule-based bots match keywords and canned intents. Real dialect has too much variety for that: the same request arrives spelled ten ways, half in Latin letters, so the bot falls through to "I didn't understand" and dumps the customer into a queue. It scales your fallback rate, not your resolution rate.
What we build
The honey step is where a person stays in control. Everything else is the typing and checking the system does for you.
Engineering decision. Arabic reliability is measured, not hoped for. Every agent runs through a validation layer for Gulf dialect, Arabizi, code-switching and right-to-left rendering before it ships, and when it does not have a grounded answer it says so and hands off rather than inventing one.
We ingest your own content — help-centre articles, product docs, policies, past tickets — into a retrieval layer. Every answer the agent gives is pulled from and matched against your material, so it speaks with your facts and your policies rather than whatever a general model happens to have absorbed.
The agent is built on an LLM that handles Arabic and English together, including Gulf dialect, Arabizi, and messages that switch script mid-sentence. It reads what the customer actually typed — mixed, informal, right-to-left — instead of forcing it through a one-language-per-turn assumption.
Retrieval-augmented generation keeps replies anchored to your knowledge base. Guardrails keep the agent on-topic and inside your policies — it answers what it is meant to, declines what it shouldn't, and doesn't wander into promises you never made or facts you never gave it.
This is the real engineering. Before anything ships, the agent runs through a validation layer built specifically for Arabic and mixed-script failure modes — dialect variation, Arabizi, code-switching, right-to-left rendering, and tokenisation edge cases. We test the language most tools only test in English, and treat Arabic reliability as a measurable capability, not a hope.
When confidence is low, the question is ambiguous, or the customer asks for a person, the agent hands off to a human with the full conversation in context — no restarting, no lost thread. Human handoff is always available. The agent absorbs volume; your team keeps the hard and the sensitive.
The same agent deploys across the channels your customers already use — WhatsApp, your web chat widget, Instagram — and syncs into your CRM. One grounded, validated, bilingual brain, consistent everywhere, instead of a different half-built bot on every platform.
We confirm exactly which channels and systems the agent connects to during scoping. The deployment commonly touches:
Engineering decisions
What the fixed price covers, and the things we will not build. Both are written into the scope before we start.
In scope
Not in scope
What you get
Most bilingual support agents go live in about three to six weeks. What moves it inside that range is the size of your knowledge base, how many channels you deploy to, and the depth of Arabic validation your use case needs. No hourly billing, no surprises.
A short call, then we document how support reaches you today and what your customers actually type — including the exceptions and the workarounds people built around it. You get a written scope, acceptance criteria and a fixed price before any build begins.
You get: Scope + fixed quote
WhatsApp Cloud API, Web chat widget, Instagram DMs and the rest of your stack — no rip-and-replace. Outputs are structured and checked against your own data, not trusted blindly.
You get: Working system on your stack
The human step is tested with the people who will own it before anything goes out. Logging and alerts so problems surface early, documentation your team can read, and you own the code and the accounts.
You get: Approval gate, monitoring, full ownership
Tell us how support reaches you today and what your customers actually type. We reply within one business day, and the price is fixed after a free scoping conversation.
Questions buyers ask
If yours isn't here, send it on WhatsApp and you will get a straight reply.
Prefer a form? Contact pageYes — that is the point of the build. The agent is designed for real Gulf Arabic and for Arabizi, where Arabic is typed in Latin letters and numbers. It is also built for code-switching, where a customer moves between Arabic and English inside a single message. We validate exactly these cases before launch instead of assuming they work because an English demo looked clean.
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Services behind this build
Each service page lists exactly what is included and the questions owners ask before they start.