Industry Solutions

How can GCC banks deploy sovereign private AI?

GCC banks use private AI for document-heavy workflows: KYC files, policy search, Arabic customer support and analyst copilots. The practical requirement is sovereign deployment, meaning the API, model serving and data storage stay in-country or inside your own environment, with audit logs you control. An OpenAI-compatible API keeps integration effort low while residency and access stay strict.

Key facts

API surfaceOpenAI-compatible /v1/chat/completions for drop-in integration
Arabic supportMultilingual chat and embedding models in the 30+ model catalogue
RetrievalEmbeddings and RAG are live for policy and KYC document search
DeploymentPlugsky cloud, your VPC, on-prem and air-gapped options
Data residencyRegion selection plus sovereign deployment for in-country processing
Access controlScoped API keys and usage analytics; enterprise SSO and RBAC options
Pricing modelFlat monthly self-serve plans; no per-token billing on self-serve
Product statusChat, streaming, JSON mode, function calling, embeddings and agents are live

TL;DR

  • Run chat, RAG and agents against a sovereign deployment inside your jurisdiction.
  • Multilingual models handle Arabic document and support workflows in one API.
  • Scoped keys, audit logs and VPC or air-gapped options support procurement reviews.
  • Flat monthly plans make internal cost allocation predictable for banking teams.
  • Start on the free plan with synthetic data, then move to a private deployment.

How it works, step by step

  1. Map the use cases by data class: public policy content, internal procedures, customer data and regulated records.
  2. Decide the deployment target per class: shared cloud, in-region hosting, VPC, on-prem or air-gapped.
  3. Build an evaluation set in Arabic and English with known answers from approved documents.
  4. Test retrieval quality with multilingual embeddings before writing any prompt logic.
  5. Define key ownership and rotation rules per application, and connect enterprise identity where available.
  6. Produce the evidence pack for procurement: architecture diagram, data-flow map, logging and DPA terms.
  7. Roll out one workflow to a pilot team, then expand only after evaluation results are signed off.
1Map the use casesby data class:public policy2Decide thedeployment targetper class: shared3Build an evaluationset in Arabic andEnglish with known4Test retrievalquality withmultilingual5Define keyownership androtation rules per6Produce theevidence pack forprocurement:

Try it yourself

Open the sovereign AI readiness score →

Where GCC banks start

The fastest wins are search and summarization over documents the bank already governs:

  • Policy and procedure Q&A: staff ask questions and get answers cited to the current circular or manual.
  • KYC and onboarding: summarize files, flag missing evidence and draft review notes for a human officer.
  • Arabic customer support: draft replies and translate between Arabic and English with consistent terminology.
  • Analyst copilots: turn long reports into briefs while keeping the source trail intact.

None of these require the model to make a decision; they shorten the path to a human decision.

Sovereign architecture options

Residency is an architecture choice, not a setting. Plugsky offers region selection for cloud deployments and private deployment modes for stricter mandates: VPC hosting inside your own tenancy, on-prem installation, and air-gapped environments with no outbound connectivity.

Because the API is OpenAI-compatible, the same application code moves between deployment modes. Evaluate in the cloud on non-sensitive data, then redeploy the identical call pattern to the sovereign environment for production records. Keep the same SDK conventions across modes so tests, runbooks and monitoring stay consistent.

Procurement, audit and model governance

Banking procurement teams ask for three things: where data is processed, who can access it, and what evidence exists after the fact. Prepare a data-flow diagram, a key inventory with rotation policy, and a logging plan that records requests, responses and model versions under your retention rules.

Keep a model allow-list tied to evaluation results, and re-run the evaluation when a model version changes. See the AI procurement checklist for banks and governments for the questions to formalize.

Honest comparison

CapabilityPlugskyUS-only hosted APIBuilding in-house
Data residencyRegion selection plus sovereign and air-gapped deploymentLimited to vendor regionsYou control fully
Arabic capabilityMultilingual chat and embedding models in 30+ modelsVaries by vendorDepends on the model you host
Deployment modesCloud, VPC, on-prem, air-gapped with one API shapeVendor cloud onlyYou build serving and operations
Access controlScoped API keys, usage analytics, enterprise SSO and RBAC optionsVendor account modelCustom identity integration
PricingFlat monthly self-serve plans; no per-token billingUsage-based per tokenGPU plus operations cost
Time to pilotDays, using the same SDK callsDays to weeksMonths

Frequently asked questions

Can data stay inside our country?

Yes for supported regions, and yes by construction with VPC, on-prem or air-gapped deployments. Confirm the exact region and data-flow architecture in writing before production.

Does Plugsky support Arabic well?

The catalogue includes multilingual chat and embedding models suited to Arabic and mixed Arabic-English content. Evaluate on your own documents, because quality varies by dialect and domain.

How do we satisfy model risk governance?

Keep a model allow-list tied to documented evaluation results, record model versions in logs, and re-run evaluations when the list changes. The API exposes chat, embeddings and agent calls so approved patterns are straightforward to enforce.

What does procurement usually ask for?

Region and data-flow evidence, key management and rotation policy, log retention, incident process, and data-processing terms. Prepare these once and reuse them across use cases.

Can we start without exposing customer data?

Yes. Use the free plan with synthetic or public documents to validate retrieval quality, then move to a private deployment for regulated data.

Is fine-tuning available for bank-specific terminology?

Fine-tuning is coming soon. Today, multilingual embeddings plus retrieval over your glossaries and circulars is the practical way to enforce terminology.

How do we handle audit trails?

Log request IDs, model names, timestamps and outcomes under your retention policy, and keep access to logs restricted. See the audit logging guide for a starting schema.