Private Cloud AI Platforms
Give internal AI projects a foundation they can share.
Design a private cloud foundation for approved AI applications and connected information.
The opportunity
The problem we help you solve
Separate experiments can create separate hosting, access and maintenance arrangements. What began as a few useful tools becomes harder to govern and operate as more teams depend on them.
Voxd helps design a private cloud AI platform around the applications you want to support. We consider identity, networking, model access and information connections together, then establish how the environment will be monitored and maintained. The goal is a practical shared foundation, with room for distinct business use cases.
What changes for your team
Reduce fragmented setup
Provide agreed platform capabilities that suitable internal applications can use instead of rebuilding the same foundations.
Make access more consistent
Connect identity and permissions to the intended audience and information boundaries.
Improve operational visibility
Understand the dependencies, usage and responsibilities behind the services running on the platform.
What we deliver
What Voxd can deliver
Platform architecture
Define the workloads, hosting arrangement and shared capabilities the environment needs.
Identity and service configuration
Set up the agreed access, networking and model-service connections.
Operating model
Prepare monitoring, maintenance and responsibility arrangements for ongoing use.
Why Voxd
Infrastructure connected to the application it serves.
Voxd combines AI integration and custom software capability with infrastructure design. We consider how users will access the service, how it will retrieve information and how the surrounding application will behave when a dependency is unavailable.
We can help compare options before hardware is purchased and stay involved through deployment and operation. The goal is a usable business service with an appropriate hosting model, not a model running in isolation.
How we work
Prove the workload before committing to the infrastructure
- 01
Define the boundaries
We establish the use cases, data requirements, users and operating expectations, including the systems and dependencies around the model.
- 02
Evaluate the options
We test suitable models and deployment approaches against representative tasks, comparing quality, performance and likely total operating cost.
- 03
Build the environment
We configure the infrastructure, access and application connections, then test the complete workflow and its operational controls.
- 04
Prepare to run it
We agree monitoring, maintenance, recovery and ownership, with handover or ongoing management matched to your team’s capacity.
Before you get started
Before you get started
Does private cloud mean on-premises hardware?
No. It describes an agreed cloud environment and control model. We assess the hosting and data paths against your particular requirements.
Can the platform support different models?
That can be part of the design. Model choices should reflect the tasks, cost and operational implications rather than being added without a use case.
Build a clearer home for your internal AI services.
Tell us which applications exist today and what your teams want to introduce next.