Workload and capacity assessment
Infrastructure & Deployment
On-Premises AI Systems
Design, budget, build, install, and support AI infrastructure inside the customer’s own facility.
We begin with the workload and available budget, then prepare practical hardware options. The goal is a system the organization can operate and expand—not the largest server we can sell.
Direct answer
Discuss this service →What is On-Premises AI Systems?
On-premises AI places the compute, models, storage, and controls inside the customer’s own facility and network.
When this helps
Common reasons organizations start here.
- The company needs direct control over data and hardware
- Existing servers are not sized for AI workloads
- Leadership needs realistic hardware budget options
- The environment lacks backup, monitoring, or deployment planning
Practical starting example
Build a department AI server with room for additional memory and storage, while deferring expensive GPU expansion until usage proves the need.
What Vai-Nova can build
A controlled system around the actual workflow.
Good, better, and best hardware options
Server build, configuration, installation, and handoff
Backup, monitoring, documentation, and support
Delivery path
Move from discovery to supported use.
- DiscoveryMap the problem, users, data, systems, constraints, and success measure.
- Pilot or essential buildCreate the smallest responsible version that can prove value.
- IntegrationConnect approved systems, test exceptions, document the operating path.
- Support and growthMonitor, improve, and add capacity only when the need is demonstrated.
Security and control
Built into the service.
- Current supported operating systems and firmware
- Segmented management and application networks
- Encrypted storage and protected backups
- Documented administrative access
Budget-aware
Begin at a level the organization can support.
Pilot, Essential, Professional, and Advanced options separate hardware, licensing, engineering, deployment, and ongoing support so scope can be adjusted without hiding the real costs.
PilotProve the use case
EssentialDeliver the core capability
ProfessionalIntegrate for regular business use
AdvancedAdd resilience, scale, and broader controls
Common questions
How does a on-premises ai systems project begin?
Can we begin with a pilot?
Yes. We can define a limited first scope with a measurable outcome before committing to a broader build.
Will security be included?
Security, access, logging, backup, recovery, and human approval are considered as part of the system rather than added after deployment.
Can the design fit our budget?
Yes. We separate the essential capability from later expansion so the organization can begin at a level it can operate and support.
Discuss on-premises ai systems.
Start with the problem, current systems, timing, and available budget. We can help determine whether a focused pilot or a broader build makes sense.