Private AI deployment / Hong Kong
Your workflow.
A considered setup.
A private deployment starts with a clear view of access, storage, model processing and operating responsibility. We work through those choices before connecting the workflow.
The useful question
Start with the data flow.
Where the app runs and where a model processes data are separate questions. A privately hosted application may still send relevant context to an external model API. The deployment plan should make that path clear.
A practical starting point
Three things
to work through.
Each engagement is scoped around the actual task. Access, integrations, fees and acceptance criteria are agreed before implementation.
Access and storage
List the systems the workflow needs, the permissions it requires and where inputs, outputs and job records will be kept.
Models and providers
Review which model services receive context, the provider terms and the retention settings for the agreed setup.
Review and ownership
Identify the reviewer, the actions needing approval and the process for export, access revocation and support.