Private-GPT

A private GPT for your organisation, inside your perimeter.

For organizations worried about sovereignty, data privacy and local inferencing, private models inside your perimeter, on your data, under your compliance. Sovereign AI with built-in token cost management: real-time spend visibility, per-query tracking, and budget guardrails. No surprises. No waste. No runaway AI costs. Nothing leaves the boundary.

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YOUR KNOWLEDGE YOUR MODELS YOUR COMPLIANCE
Sovereign enclave — your knowledge, models and compliance operate inside a sealed perimeter.
Sovereignty

Your organization's digital twin. Grounded in your data, not the web.

Private-GPT is not a hosted AI service you connect to. It is a generative AI capability deployed inside your infrastructure, with inferencing, retrieval and audit logs never leaving your perimeter.

Your knowledge

Grounded in your data, not the web, and connected to your internal documents, databases, and systems through the Grounding & Substrate layer. It knows what your organization knows — nothing more, nothing less — and reflects your unique context, not the internet's.

Your models

Model selection by use-case, not hype. Whether you need reasoning for judgment, precision for forecasting, or adaptability for learning, we deploy and manage the right open-weight or licensed models inside your perimeter. No shared inference endpoints without explicit approvals.

Your compliance

Audit logs, access controls, data residency, and retention policies, all configured to your regulatory obligations, not a generic SaaS baseline. HIPAA, GDPR, SOC 2, or your own framework. We match your requirements, not our defaults. Your compliance, your way. No shared tenancy. No data leakage.

Local inferencing

Your data stays home. Your token bill stops climbing.

A policy chip decides the route: most traffic runs to Local inference, in perimeter on your hardware, at a flat spend; a smaller share escalates to Cloud AI, frontier models on demand, metered per token.

The bulk of your work runs against local models inside your perimeter. On your own hardware or your cloud tenancy. Nothing leaves the boundary, and the spend curve is flat: you pay for the infrastructure once, not per token, so cost stops scaling with usage.

Where a workload genuinely needs a frontier cloud model, a policy layer (not evelopers / agents) decide. That escalation is then metered, budgeted, logged, and reviewable, so the CFO sees every cloud token before it becomes a surprise on an invoice.

What the enclave means

Nothing outbound. Not a prompt, not a log.

Every query stays inside the perimeter. Inference happens on your hardware or your cloud tenancy. Retrieval pulls from your knowledge bases. Logs write to your SIEM. Nothing crosses the boundary to a public cloud inference endpoint.

If an outbound path is attempted, it is refused. That refusal is logged.

In-perimeter inference

Model inference runs inside. No prompt leaves your boundary, unless as-per a pre-approved policy.

Retrieval-augmented

Connected to your document stores, intranets, and operational data, not the public web.

Audited 24×7

Every query, every retrieval, every refusal is logged and available for audit.

Generative AI that never leaves your perimeter.

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