A resident asks one question. Behind it, three departments, four systems and a queue nobody owns. We build citizen service that resolves in Arabic and English, cases that carry themselves between entities, and backlogs worked down by agents while officials keep the decisions, all on infrastructure the government controls.
A request arrives in Arabic on one channel and is followed up in English on another. The answer depends on which department reads it, and the citizen learns the outcome by asking again. Service standards are published in hours; the process behind them is measured in days.
A licence, a permit or a benefit touches three or four entities, each with its own system and its own queue. The case waits at every boundary, and the citizen is asked to re-supply documents each entity already holds.
Case volumes rise with population and policy; officer numbers do not. Straightforward cases wait behind complex ones because the queue does not know the difference, and experienced officers spend their days on the routine rather than the judgement calls they were trained for.
Most commercial AI tools assume the data can travel to the model. Government data cannot. Entities are left choosing between capability they are not allowed to use and sovereign platforms with no production AI on them, while officers quietly use consumer tools on their phones to get the work done.
Each scenario maps to an engagement pattern we ship with the ninety-day method. Agents work the cases; your people approve at the gates that matter.
Arabic-first citizen agents that understand intent, coordinate across departments and close cases without a single hand-off form. The request is understood on arrival, the relevant entities are queried through their own systems, and the citizen receives one answer in the language they used, with an official approving anything discretionary.
How the pattern worksOne agent owns the case from application to record: it gathers what each department needs from what the applicant has already supplied, runs the eligibility checks each entity defines, works the exceptions, and presents the decision to the officer with the evidence assembled.
How the pattern worksOfficers ask how a regulation applies or how a similar case was decided, and receive an answer that cites the circular, the article or the precedent, filtered by what their role is permitted to see.
How the pattern worksGovernment data is sovereign by definition. Federal and emirate-level data laws, the UAE's information assurance standards and Dubai's electronic security regime, and Saudi Arabia's National Cybersecurity Authority controls and SDAIA's data and AI governance framework all point the same way: citizen data stays in-country, on infrastructure the entity controls, with classification carried through every system that handles it. We deploy on government cloud or on-premise, with models inside the boundary and no third-party inference outside it.
AI in public service carries its own duties. National AI ethics principles in both the UAE and Saudi Arabia expect decisions affecting citizens to be explainable, contestable and overseen by a person. Our patterns keep officials at named approval gates for any discretionary outcome and record the full reasoning chain, so a decision can be explained to the citizen and defended to the auditor.
Arabic is the language of record. Systems we build for government read, reason and respond in Arabic natively, with English as the companion, and the audit trail is legible in both. Classification labels travel with the data through every step, so a restricted document is never summarised into an unrestricted answer.
We have not yet published a government case study. The nearest honest evidence is from the delivery record: service management for a 70,000-person organisation, a workforce platform deployed group-wide under an aviation security standard, and core system integration under a central bank's mandates. Each is pre-AI work at public-sector scale and sensitivity.
A queue and service management platform for the Emirates facilities management division, keeping staff services moving for a workforce of more than seventy thousand people.
A facial-recognition workforce platform with sub-half-second recognition, built during the pandemic to make attendance and access contactless for 70,000+ Emirates Group employees.
A comprehensive digital transformation of core banking operations on Temenos T24, repositioning the bank as a digital-first institution while daily operations kept running.
One of our engineers sits inside your organisation from the diagnostic through to go-live, with an engagement lead, integration engineering and design behind them. In a government entity that means working alongside the case officers and the service centre, in Arabic and English, and mapping the hand-offs between departments as they actually happen rather than as the process manual describes them. Read about the embedded model.
The two-week diagnostic maps where intelligence compounds in your sector's processes, inside your regulator's rules, and leaves you with a plan worth keeping, whoever you choose to build with.
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