Riyadh, public sector

Government AI in Riyadh is judged on its evidence trail

A ministry programme in Riyadh passes through data classification, an in-country hosting decision and an Arabic service standard before anyone asks how good the model is.

Bad data, bad AI. We fix the data layer under the service first.
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Key takeaways

What you need to know, in a minute

  1. 01

    Classification comes before automation

    Each data class gets a hosting decision and a written reason, so the service can be explained to a reviewer without a scramble.

  2. 02

    Arabic is the service language

    Intake, correspondence and the decision notice are produced in formal Arabic, with English kept as the secondary version.

  3. 03

    A human decision point stays in the loop

    Automation prepares the file and proposes the outcome. An officer approves it, and that approval is recorded.

Service desk

Case intake and routing in Arabic

Requests arrive as scans, forms and free text. We read them, classify the request type and route it with the supporting pages attached.
PDPL

A processing register that stays current

The register is generated from the platform, so it reflects the systems that are actually running rather than last year's spreadsheet.
Reporting

One reporting number per indicator

Programme indicators are defined once, calculated once and published to every dashboard from the same layer.
Use cases

Where this applies in practice

What usually stalls these programmes

Hosting decided late

When residency is settled after the build, the service gets rebuilt. We settle it in the first weeks.

Ownership spread across departments

Every dataset gets a named owner inside the entity before the pipeline is written.

Arabic treated as translation

Translating an English service produces text no citizen wants to read. We write the Arabic first.

Government and public sector in Riyadh, questions we get

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