Data-Sovereign AI — Proof of Performance

The problem

Most AI tools send your data to a model hosted abroad. For an insurer, a clinic, a bank or a family business handling personal data, that means:

Regulatory exposure — personal data leaving the jurisdiction sits awkwardly with PDPL and sector rules.
Contractual risk — many of your own clients forbid third-party, cross-border processing of their data.
Vendor dependence — your costs and your access are at the mercy of a foreign API's pricing and terms.

The answer

We deploy open AI models on your infrastructure, in your jurisdiction. The data is processed where it already lives and never crosses the wire to anyone else.

Your data→Model on your box→Your answer Nothing leaves ×

No external API key. No cloud round-trip. The system keeps working with the network unplugged — the clearest possible test that nothing is being sent out.

The proof

We don't ask you to take this on faith. Every engagement opens with a witnessed proof on representative work, structured in three legs:

1 · Sovereignty

Inference runs locally with no external key. We disconnect the network and it still answers — demonstrating zero data egress.

2 · Capability

We run your real task — claim triage, document summarisation, redaction, search over your own files — and you judge the output.

3 · Performance

We measure it: speed, throughput per hour, and the cost — which, once deployed, is effectively zero per query.

Worked example — claim handling, processed entirely on-box

Run on a Mac Studio reference rig. Synthetic data shown — no real records.

Task

Summarise this motor-insurance claim, and list every item of personal data to redact before it leaves the building.

On-box output (verbatim)

Claim summary • Toyota Land Cruiser rear-ended on Sheikh Zayed Rd, 14 Jun 2026. • Estimated repair cost: AED 18,400. • Dubai-registered vehicle. Data to redact • Name — redacted • Emirates ID — redacted • Mobile number — redacted • Vehicle plate — redacted • IBAN — redacted

Result

5 of 5 personal-data identifiers correctly flagged. Processed locally — no external call.

5 / 5

PII identifiers caught

81 tok/s

Generation speed (measured)

Cost per query, once deployed

Bytes sent externally

What this means for you

The workflows that benefit most are the high-volume, data-sensitive ones you currently can't safely put through a cloud API:

Workflow	Why local wins
Claims / case triage & summarisation	Personal and financial data never leaves; runs at volume for $0 per item.
Document redaction & PII detection	Sensitive records processed in place, before anything is shared.
"Chat with your own documents" (search/RAG)	Your entire corpus stays on your infrastructure — answers cite your files, nothing is uploaded.
Bulk content drafting & repurposing	High-volume first drafts at zero marginal cost; people review, the machine drafts.

Where top-tier quality or hard reasoning matters most — flagship deliverables — we still route to frontier models. The point isn't "local for everything"; it's local for the sensitive, high-volume majority, with the right tool reserved for the rest.

How we deliver

Proof first — the three-leg demo above, on your representative task, before any commitment.
Deploy on your infrastructure — your server or a UAE-region box you control. We install and configure.
You own it — at handover the stack is yours: your hardware, your models, no per-token bill, no lock-in.

See it on your own data

We'll run a proof on a task that matters to you — sovereignty, capability and performance, measured in front of you.

Book a proof session →

Articulate · Data-Sovereign AI — Proof of Performance. Performance figures measured on a reference Mac Studio; your numbers depend on your hardware and task and are established during the proof. Example uses synthetic data only.