The AI layer on top of the data fabric — owned models, owned data, running inside your enterprise boundary. Your invoices, forecasts, and anomaly flags never leave your cloud tenant for a vendor's analytical platform, because the models come to the data, not the other way round.
Most finance-AI offerings ask you to ship your ledger to their platform and rent the intelligence back. Sovereign Finance Intelligence inverts that: the foundation models, the fine-tuned endpoints, and the feature stores are provisioned inside your own cloud tenant, governed by your IAM, logged in your audit trail.
The stack is deliberately narrow and entirely declared. Amazon Bedrock provides the foundation-model layer — Claude, Llama, or custom models — with Bedrock Guardrails enforcing PII policy on every interaction. Amazon SageMaker carries training, inference endpoints, and the feature store. Amazon Textract handles document extraction with no document retention, and Amazon Comprehend provides Arabic and English entity and PII detection. Nothing in that list is a ClayDesk analytical platform, because there isn't one: the deployment is yours.
For organisations facing in-country residency requirements, the architecture is earmarked for me-central-1 — the AWS Dubai region — when a mandate requires data to stay in-country.
Inbound supplier documents — PDFs, scans, attachments — extracted with Amazon Textract and structured against your master data before a human touches them. Extraction runs in your tenant, with no document retention by the extraction service.
A finance assistant on Amazon Bedrock that answers in Arabic or English from your own figures — receivables positions, supplier exposure, tax status — with Bedrock Guardrails enforcing PII policy on every prompt and response.
Anomaly models trained on your transaction history with Amazon SageMaker — duplicate invoices, out-of-pattern amounts, counterparty behaviour shifts — scored at your endpoints, on your account. Sensitive transaction data never leaves the boundary to be scored elsewhere.
Forecasting models built on the highest-quality dataset a finance function owns: invoices that have already passed authority validation. Cleared-invoice history carries verified amounts, dates, and counterparties — labelled training data you did not have to construct.
With SaaS AI, you are a tenant of your own financial data. The models improve on your transactions, the embeddings live in someone else's index, and when the contract ends, what leaves with you is an export file — not the intelligence built on it.
That trade can be acceptable for marketing copy. It is a harder case to make for the ledger: the dataset that determines your tax position, your credit standing, and your audit exposure. Once real-time reporting mandates put your numbers in front of a tax authority continuously, the asymmetry sharpens — the regulator holds analytical capability over your data, and your own capability sits in a vendor's tenancy.
Sovereign Finance Intelligence is the ownership answer to that question. The models are provisioned in your account, the fine-tuning belongs to you, and an exit means decommissioning infrastructure you control — not negotiating for your own derivatives back.
Read the argument in full →For general drafting, you should. The difference is what happens to the data. A general-purpose assistant or SaaS finance-AI product processes your ledger on the vendor's infrastructure, under the vendor's terms, in the vendor's jurisdiction. Sovereign Finance Intelligence runs the models inside your own cloud tenant — your IAM, your logs, your boundary — which is the difference an auditor, a regulator, or a residency mandate actually tests.
No. The models run in your tenant. Bedrock, SageMaker, Textract, and Comprehend are provisioned in your own cloud account; Textract extraction retains no documents; Bedrock Guardrails enforce PII policy on model interactions. ClayDesk operates the capability under the managed engagement, but there is no ClayDesk analytical platform your data is copied to.
A mature data foundation: governed vendor, customer, item, and GL/tax masters, plus a reliable invoice flow to train and ground against. Most clients build this through the Data Practice first — models trained on ungoverned data simply automate the defects, so we are candid on the discovery call about whether you are ready or what closes the gap.
Bilingual is the design point, not a translation layer. Amazon Comprehend provides Arabic and English entity and PII detection, and the assistant and document-AI use cases handle both languages — which matters in a market where supplier documents, contracts, and tax correspondence routinely arrive in either. The same AR/EN-by-default stance runs through our Data Practice dashboards.
Sovereign Finance Intelligence is available on managed-tier engagements and is quoted per engagement — the cost depends on use-case count, data volumes, and the state of your data foundation. What we commit to: directional pricing in the first conversation, and a written quote within 24 hours of the discovery call.
Tell us your use case and the state of your data foundation. We tell you whether you are ready for the sovereign tier, what closes the gap if not, and what it will cost — with a written quote within 24 hours.