Edge Infrastructure
Can be scoped for environments where local AI capability is required.
AI-in-a-Box
Private AI infrastructure in a controlled form factor.
AI-in-a-Box is a roadmap-sensitive direction for scoped private AI architecture, subject to validated hardware and deployment scope.
Edge siteEnterprise pilot
Packaged private AI architectureHardware · platform · AI runtime
GPUStorageRuntimeSecurityOps
Deployment-dependent directionNot a public product-readiness claim
Packaged architecture direction with clear validation boundaries.
AI-in-a-Box should be positioned as a deployment-dependent and roadmap-sensitive capability until hardware, runtime, operations, and commercial scope are approved.
- Packaged private AI architecture planning
- Edge or controlled-site deployment direction
- Validated hardware dependency mapping
- AI runtime stack planning
- Security and operations boundary definition
- Appliance sizing considerations
- Pilot scope definition
- Roadmap and availability qualification
Where AI-in-a-Box fits
Use this capability only where the AI workload, data boundary, operating model, and validation scope are clear.
Enterprise Pilot
May support bounded AI trials after hardware and scope validation.
Sovereign AI Platform
Aligns private AI direction with controlled infrastructure ownership.
Architecture
Deployment-dependent direction
Not a public product-readiness claim
Validation path
Each AI capability should move through assessment, design, and validation before publication or commitment.
Confirm use case and site
Review workload, location, hardware, security, and operations constraints.
Test architecture assumptions
Confirm hardware, runtime, and support model before packaging claims.
Define commercial boundary
Document availability, support, and contractual responsibilities.
Next step
Discuss AI-in-a-Box scope without product-readiness assumptions.
Start with your workloads, operating model, and control requirements.