Private AI. Built around your business.
Build AI on infrastructure that fits your data and operating requirements. We connect models, enterprise knowledge and applications within a deployment environment you can govern.
The perspective
Choose the capability. Then build its home.
Infrastructure should serve the work you want AI to do. We start with the use case, then shape the model, deployment and operating approach around the access, performance and control your business needs.
Capacity with perspective
Match the model to the workload.
Extraction, retrieval and difficult reasoning do not necessarily need the same model. Test smaller models where they meet the task requirements and reserve more expensive routes for cases that justify them.
A closer look
What goes into the work.
Explore the questions that matter for your project.
What needs to stay
inside your environment?
Choose where the workflow runs based on the data it uses and the task it performs. Assess access controls, monitoring and compliance requirements alongside hosting.
| Model | Why consider it | Operational trade-off |
|---|---|---|
| Managed model endpoint | Access to a provider-operated model where the data terms and processing locations fit the task. | Provider dependency, retention settings, network access and change management need review. |
| Dedicated private cloud | Workloads and data services run in a customer-controlled cloud environment. | Your team needs an operating model for identity, networking, capacity, patching and support. |
| On-premises or isolated | The intended use requires local infrastructure or tightly restricted connectivity. | Model availability, hardware capacity, updates and recovery become explicit engineering responsibilities. |
Control extends
beyond the prompt.
Map source documents, embeddings, caches, logs, backups and support access. Decide retention and deletion across the whole workflow.
Price the accepted result.
A low inference price can hide expensive review or infrastructure. Evaluate the workload at realistic volumes and concurrency.
Quality at the required task
Use representative documents and exceptions to compare models. Count the cases that need human correction or cannot be completed.
Capacity under load
Test document size, peak demand and response time. Account for idle capacity, queues and the cost of making the service available when work arrives.
An owner for the service
Assign monitoring, patching, model changes, incident response and recovery tests. Include these responsibilities in the operating budget.
Before choosing private infrastructure.
Does private cloud mean no data leaves our environment?
Only if the complete architecture is designed that way. Document extraction, embeddings, inference, telemetry, support tools and backups can each create an external data path. We map these before choosing services.
Can we use open-weight models?
Where their license, quality and operating requirements fit. Evaluate them on the actual workflow and include the cost of hosting, updating and supporting the model.
Can we change providers later?
We can design interfaces and retain evaluation cases to make a change more manageable. A provider change still requires testing of quality, permissions, cost and system behavior.
Build your private AI capability.
Bring the intended workflow, data constraints, expected demand and existing infrastructure. We can compare the deployment options against the work.
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