01
Managed QGI managed
A dedicated QGI-operated environment with defined isolation, access, and service boundaries.
QGI Private AI Enterprise
Private AI Deployment
Choose where the intelligence runs, who operates it, and what can cross the boundary.
Deployment architecture is scoped to workload, region, model, infrastructure, and assurance requirements
What it is
QGI Private AI supports deployment patterns from a QGI-managed isolated environment to customer-operated VPC, on-premises, sovereign, and air-gapped infrastructure. The model, data, identity, tool, and logging boundaries are designed around the organization's control requirements.
Designed for enterprise architecture, security, platform engineering, risk, and public-sector technology teams.
Capabilities
Built around the work—not the demo.
02
Cloud Customer VPC
Private networking, customer identity, logging, and data services inside the customer cloud boundary.
03
AWS AWS deployment
Integrate QGI with existing AWS security, data, observability, and approved Bedrock models.
04
Private On-premises
Run inside customer-controlled infrastructure where cloud movement is restricted.
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Sovereign Sovereign environments
Design for residency, jurisdiction, operator, supply-chain, and model-governance constraints.
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Isolated Air-gapped operation
Support isolated environments with controlled updates, artifacts, and offline operating procedures.
How it works
One governed path.
- 01
Classify the workload
Define data sensitivity, users, model capabilities, tool access, latency, residency, and assurance needs.
- 02
Select the boundary
Choose the deployment and operating model, then define identity, network, model, memory, and logging controls.
- 03
Validate before expansion
Start with one controlled application, test the governance artifacts, and expand through repeatable patterns.
Continue exploring
QGI / QGI Private AI
Design deployment →