Private AI
I help you choose and deploy models in your cloud account or on your hardware. The work includes checking what the model can do, how documents are processed, and which services need access to your data.
The starting point
Client files, internal documents, source code, and operational records can make an AI workflow useful. They also raise a practical question: which systems are allowed to process them?
“Private” can mean several things. Your team may need private network access to an approved provider, inference within your own cloud account, or an environment that runs without external connectivity. Defining that requirement comes before choosing a model or buying hardware.
A good first project pairs a defined data boundary with a useful workflow: a document assistant, a reviewed extraction process, or internal support search. The model is one part of that system.
Deployment choices
A managed service reached over a private network path. Useful when approved provider processing is acceptable and avoiding public network exposure is the requirement.
Private transport does not mean inference runs in your own account.
Host model serving and the application in an environment you control. Keep retrieval and supporting services within the agreed boundary.
External dependencies, administrator access, and maintenance still need an explicit design.
Deny external outbound paths from the defined runtime, or use a fully disconnected environment when the requirement calls for it.
Preparation, updates, observability, and support must work within that constraint.
Zero-egress deployment
Keep inference and its dependencies inside a defined boundary, with external outbound traffic denied. The design includes retrieval, logging, model updates, and agent tools—not just the model server.
What an engagement can include
Each deliverable is scoped to your environment, use case, and operating team.
Common questions
No. A customer-controlled cloud environment may fit the requirement. The decision depends on the approved data boundary, who can administer the infrastructure, availability needs, and operating capacity.
No. Retention describes what happens to data after processing. Egress describes where data travels. A provider can process a prompt outside your environment without retaining it afterward.
Potentially. Start with the workflow and the reason for private deployment. Hardware and support costs need to make sense for the expected benefit; a managed service may be sufficient for some requirements.
Hosting location is one design decision. Compliance depends on the full system, its use, contracts, access controls, and organizational obligations. The engagement can provide engineering evidence for your reviewers to assess.
Often, but the integration must be evaluated. API compatibility can simplify a connection; authentication, streaming, tool calls, and behavior still need testing with the chosen model.
We start with the use case, the data boundary, a representative workload, and who will operate the result. An assessment or focused pilot can establish the scope before a production build.
Further reading
Private AI · Fundamentals
A practical starting point for deciding where AI should run, who should control it, and whether a private deployment fits your business.
Read the guide : What is private AI, and when does it make sense?Private AI · Technical guide
Design the complete inference boundary, including retrieval, identity, logs, model preparation, and maintenance.
Read the guide : A reference architecture for zero-egress LLMsEngineering · Technical guide
Build an acceptance test that exercises real work, attempts prohibited connections, and records exactly what was verified.
Read the guide : How to verify a zero-egress AI environmentStart a conversation
Tell me what the system needs to do and where the data is allowed to go. We can discuss the hosting options and what would need testing.
