Data stays inside controlled infrastructure
Prompts, files, embeddings and generated answers can remain in the company network with local inference and private access rules.
On premise AI / on-premise AI
On premise AI infrastructure for companies that need control.
On premise AI means running inference, document search, embeddings and internal assistants on infrastructure you control instead of relying only on external AI APIs. OPA packages the GPU server, model runtime, private RAG, access rules and integration work needed to make that infrastructure usable inside the company.
Costs become easier to forecast
Recurring workloads move from variable token billing to owned capacity, maintenance and clear server sizing.
Works with real enterprise tools
Connect IDEs, SharePoint, document repositories, internal chatbots and agent workflows to a private AI layer.
Key concepts are explained in the page content instead of being exposed as a raw keyword list.
Related pages
Explore sizing, models, integration and Let's talk about it options to turn this requirement into a practical infrastructure project.