Private AI infrastructure · Your data never leaves your boundary

Your models.Your GPUs.Your data stays home.

Every prompt sent to a public AI service is a copy of your business leaving the building — contracts, salary data, source code, customer records. We deploy open-weight models on dedicated GPU servers inside your own network or a Saudi-hosted facility, so the intelligence comes to your data instead of your data going to someone else's cloud.

Scope your deploymentSee how it works

Runs in your datacentre or hosted in the Kingdom · No prompt leaves your network · Fixed monthly cost · Every request logged

What private hosting changes
0Prompts sent to third partiesnothing crosses your boundary
100%Residency you controlyour datacentre or hosted in Saudi Arabia
FixedMonthly costcapacity, not per-token billing
24/7Monitored and maintainedpatching, upgrades, capacity planning
Why this matters now

Your team is already using AI. The only open question is whether your data went with it.

  • 01Contracts, payroll files, source code and customer records get pasted into consumer AI tools that nobody approved and nobody can audit.
  • 02You cannot say where that data was processed, how long it is retained, or under whose jurisdiction it now sits.
  • 03Per-token pricing turns a successful pilot into a line item finance never forecast — and the bill grows exactly as adoption succeeds.
  • 04The model moves under you. A provider deprecates a version, and prompts that worked last quarter quietly behave differently.
  • 05Anything touching personal data under Saudi PDPL needs a defensible answer about processing location. "It goes to an API abroad" is not one.
  • 06The moment you want the model to read your ERP or your document store, sending that data outside stops being a policy question and becomes a hard blocker.
Two ways to run AI

Public API versus private hosting.

Public AI APIs

  • Every prompt and document leaves your network to be processed on infrastructure you do not control.
  • Cost scales per token, so the better it works the more it costs — with no ceiling you set.
  • Model versions are deprecated on the provider's schedule, not yours.
  • Data residency and retention are governed by someone else's terms of service.
  • Rate limits and outages are outside your control and outside your SLA.
  • Connecting live ERP or HR data means exporting it beyond your compliance boundary.

Private LLM hosting

  • Models run on dedicated GPUs inside your network or a facility hosted in the Kingdom. Prompts never leave.
  • You pay for capacity. Usage can grow to fill it without the invoice moving.
  • You choose when to upgrade a model — and you can keep a version frozen for as long as a validated process needs it.
  • Residency, retention and access are your policies, enforced on your hardware.
  • Capacity is yours alone. No shared rate limits, no noisy neighbours.
  • The model sits next to your ERP, file store and databases, so connecting them is an internal integration.
What we deliver

A complete private AI stack — sized, deployed and operated.

Not a licence and a manual. We size the hardware to your actual workload, deploy the serving stack, connect it to the systems your teams already use, and keep it running.

Nothing leaves your boundary

Inference happens on your GPUs. No prompt, document or embedding is transmitted to a third-party service — which makes the data-residency question answerable in one sentence.

Dedicated GPU capacity

We size VRAM, throughput and concurrency against your real usage — how many people, how long the documents are, how fast an answer must come back — then provision accordingly.

Open-weight models you choose

Llama, Qwen, Mistral, Gemma, DeepSeek and other open-weight families, including models with strong Arabic. You are not locked to one vendor's roadmap.

Governed access and full audit

Role-based access, per-team quotas and a complete log of who asked what and which model answered — the same governance model as ZIJ.

Forecastable cost

A fixed monthly figure for capacity instead of a per-token bill that rises with adoption. Finance can budget it like any other infrastructure line.

Operated, not just installed

Monitoring, patching, driver and model upgrades, and capacity review as usage grows. Handover is not the end of the engagement.

Where it earns its place

The work that could never go to a public API.

These are the cases where teams have wanted AI for two years and compliance has said no. Private hosting is what changes the answer.

Finance & legal

Contracts and ledgers that cannot leave.

Summarise agreements, compare clause versions and question the general ledger in plain language — without a single document crossing your firewall.

HR & people data

Employee records under PDPL.

Screen CVs, answer policy questions and draft correspondence over real personnel data, with processing location and retention you can evidence to a regulator.

Engineering

Source code that stays proprietary.

Code review, refactoring and investigation over private repositories, with no code sent to an external model provider.

Customer operations

Support history without exposure.

Draft replies and surface patterns across tickets and call notes containing customer identifiers, entirely inside your own network.

Government & regulated

Sovereignty as a hard requirement.

For entities where processing must demonstrably remain in the Kingdom, the deployment target is a decision you make, not a provider's default region.

Manufacturing & field

Air-gapped and low-connectivity sites.

Plants and remote operations that cannot depend on an internet round trip still get an assistant, because the model is on the local network.

How we deliver

From assessment to a running private model.

01

Assess

We map the workloads you actually want served, who will use them, and — most importantly — what categories of data must never leave. That defines everything downstream.

02

Size the hardware

Model class, VRAM, concurrency and expected response time turn into a specific GPU configuration. We size for the workload in front of us, not for a brochure number.

03

Deploy

Installed in your datacentre, or provisioned in a facility hosted in the Kingdom. Network isolation, storage and access control are set up as part of the build, not afterwards.

04

Integrate

The serving layer exposes an OpenAI-compatible API, so your existing tools, ZIJ, Business Central and internal applications connect without being rewritten.

05

Operate

Monitoring, patching, model and driver upgrades, and a capacity review as adoption grows — run by the team that deployed it.

Questions we get

Before you scope it.

For the majority of enterprise work — summarising documents, answering over your own knowledge, drafting, extraction, classification, querying business data — current open-weight models are strong, and several handle Arabic well. We benchmark candidates against your real tasks during the assessment rather than assuming, and we will tell you plainly if a workload is better served another way.

Next step

Find out what it would take to bring AI inside.

Tell us the workloads you want served and the data that cannot leave. We will come back with a model recommendation, a GPU sizing, a deployment target and a monthly figure — and an honest view of whether private hosting is the right answer for your volume.

Scope your deployment

Saudi-based delivery · Arabic-capable models · Governed and audited · Operated after go-live