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Private & offline AI

AI that never leaves the building.

Frontier-class assistance on hardware you own — open-weight models on a DGX Spark-class appliance, retrieval over your own documents, and a hard boundary the data never crosses. Built for work where confidentiality is the product.

Book an AI strategy call →Start with the audit

Legal · Medical · Research · Education · Finance · Government

YOUR PREMISES

A DGX Spark-class appliance: petaflop-scale AI in a desktop footprint.

Why offline

Some work should never ride a third-party API

Cloud AI is the right answer for most teams — we deploy it every week. But for privileged, regulated, or sovereign work, the only defensible architecture is one where the data physically cannot leave.

Zero data egress

Prompts, documents, and answers stay on hardware you control. No cloud API, no third-party processor, no cross-border transfer to explain to a regulator.

You own the stack

Open-weight models running on your appliance. No per-seat licence creep, no vendor deprecating the model your workflows depend on.

Predictable cost

One hardware purchase plus a support plan — instead of usage-metered API bills that grow with adoption. Heavy internal use stops being a budget risk.

How it works

The whole model, on your desk

Your documents are indexed locally. An open-weight model answers from them on the appliance. The boundary is physical — nothing crosses it.

NOTHING CROSSES THIS LINEYour documentscontracts · charts · researchLocal model + retrievalopen-weight · on the applianceYour team's answersdrafts · summaries · searchno cloud API · no telemetry · no training on your data

Who this is for

Built for work where confidentiality is the product

Legal

Solicitor-client privilege

Draft, summarize, and search matter files without waiving privilege. Client documents never touch a third-party cloud.

Medical & clinics

PHIPA / HIPAA posture

Chart summaries, intake triage, and letters on-site. PHI stays inside the circle of care.

Research & IP-heavy R&D

IP containment

Query your unpublished results, patents-in-progress, and lab notebooks without leaking them into anyone's training data.

Education

FERPA-aware

Tutoring, grading assistance, and curriculum tools that keep student records where the law expects them.

Finance & accounting

Confidentiality by design

Analyze client books, model scenarios, and draft filings with records that never leave your office.

Government & public sector

Data sovereignty

Sovereign deployments on air-gapped networks. The model runs where the mandate says the data must live.

How we deliver it

From audit to an appliance your team runs

Private AI is delivered through the same ladder as every ReadyIQ engagement — assessed first, proven on your documents, and handed to a trained team.

  1. 1 · Assess

    An AI Audit scopes the workflows, the sensitivity boundary, and the hardware tier that fits.

  2. 2 · Provision

    We configure DGX Spark-class hardware with vetted open-weight models — sized to your workload.

  3. 3 · Deploy

    Private retrieval over your documents. Everything indexed locally; nothing phones home.

  4. 4 · Train

    Role-based enablement for the people who will run it — prompts, guardrails, escalation paths.

  5. 5 · Operate

    Managed optimization: model updates, evals, and governance on a schedule you approve.

Platform enablement

Already approved an AI platform? We train on what you have.

Not every workflow needs to be offline. For everything else, we deliver role-based training inside the systems your IT and security teams already signed off on — no new vendor review required.

  • Claude for Work

    incl. Claude Cowork + Claude Code

  • Microsoft 365 Copilot

    Word · Excel · Teams workflows

  • ChatGPT Enterprise

    incl. Codex for engineering teams

  • Amazon Q Business

    AWS-native assistants

  • Google Gemini

    Workspace-integrated AI

Explore team training →Book a call

Training is workflow adoption for the roles that run it — not generic AI literacy. Platform names are the property of their respective owners.

FAQ

Offline AI, honestly answered

›Is it really offline?

Yes. Inference runs entirely on the appliance. It can operate on an isolated VLAN or fully air-gapped network. Model updates arrive as scheduled, reviewed imports — not a live cloud dependency.

›What hardware does it run on?

For most teams, a compact DGX Spark-class desktop appliance delivers petaflop-scale inference in an office-friendly form factor. Larger workloads step up to workstation- or server-class NVIDIA hardware. We scope the tier during the audit.

›Which models can it run?

Vetted open-weight models — the current generation of Llama, Mistral, gpt-oss, and domain-tuned variants — selected per workload and evaluated on your own documents before go-live.

›How does it stay current?

Through the managed-optimization plan: periodic model refreshes, retrieval re-indexing, and eval runs, each applied as a reviewed update rather than a silent change.

›What does it cost?

Private deployments are scoped like any ReadyIQ engagement: the AI Audit (US$7,500) defines the build, then implementation covers hardware, deployment, and training. Book a call for a scoped estimate.

Find out if your work belongs offline

30 minutes with a senior consultant. We map the sensitivity boundary, the workload, and the hardware tier — and tell you honestly if cloud AI is the better fit.

Book an AI strategy call →