Private AI systems without exposing proprietary information
Secure internal AI assistants and retrieval-augmented generation architecture for clients that cannot put proprietary data on a shared tenant.
- PrivateAI
- RAGready
- Ownedinfra
- NDArespected
Internal assistants trained on company knowledge, with controlled data handling end-to-end.
Retrieval-augmented generation grounded in the client’s actual documents, not the open web.
Source, deployment, and documentation handed to the client. No platform lock-in.
Never named publicly. The work happens whether or not the case study runs.
The shape of the problem
Several Salvus engagements involve businesses that need real AI inside the operation but cannot put proprietary information on consumer chat tools or shared-tenant SaaS. The brief from these clients is the same: give the team the ergonomics of modern AI without the custody trade-off.
What we build
Secure internal AI assistants and retrieval-augmented generation architecture, deployed on infrastructure the client controls. The assistants are trained on company documents, SOPs, and operational knowledge. Access is controlled, logs are auditable, and the documents stay where they are supposed to.
What clients get
Private AI access with controlled data handling, source code in the client’s repo, deployment in the client’s environment, and a runbook the client’s team can maintain independently if the relationship ever ends.
Your turn
Same problem? Different industry?
We work with one client per vertical at a time. If your shape matches one of our dossiers, we’ll tell you straight.