Useful AI.
Inside your boundaries.

Private AI starts with a clear deployment boundary: the model, the sources it can use, and the people authorized to access it.

Define where your data goes

We map prompts, responses, source documents, logs, and backups before choosing an architecture. For a fully isolated deployment, we can design inference and retrieval to run within your own data center or private cloud. Any external services or integrations are identified and agreed with your team.

Access controls, retention settings, and network restrictions are part of the implementation scope. We also evaluate model licenses, infrastructure dependencies, and maintenance needs so you can make an informed choice about ownership and ongoing operation.

Validate the controls with your team

We work with your security and governance teams to translate their requirements into deployment controls and acceptance checks. Hosting a model privately is one part of that work; the project also needs agreed data handling practices, operational responsibilities, and review of the selected services.

Before rollout, we test access boundaries and review the configured data flows with your team. If domain adaptation is useful, we agree which training data can be used and where that processing will happen. The final architecture and any compliance assessment depend on the use case, environment, and controls actually implemented.