Approved sources
Policies, procedures, client documents, email extracts, and knowledge bases chosen deliberately, not scraped casually.
Private AI Stack
Controlled AI for Isle of Man work that has to stay protected.
A Private AI Stack is not just a hardware package. It is a controlled way to use AI with sensitive documents, internal knowledge, permissions, review, and audit logs.
Architecture
The product is control, not the box it happens to run on.
Hardware may be part of the answer, but only after the business problem, data sensitivity, retrieval needs, and day-to-day operating model are clear.
Private retrieval
Controlled model access
Workflow tooling
Audit logs
Optional local infrastructure
What it includes
Permission-aware retrieval
Search and context retrieval shaped around what each user should actually be allowed to see.
Controlled model access
Local, private cloud, or hybrid model routing chosen according to sensitivity, capability, and performance.
Human review
Drafting, summarisation, classification, and routing with clear review standards for sensitive work.
Auditability
Logs, escalation points, ownership, and governance notes so AI use is visible rather than whispered about.
What it is for
Sensitive document search, compliance support, confidential client operations, and internal knowledge work where public-tool use would make people rightly uncomfortable.
What it is not
It is not a generic server sale, a claim that local models are always better, or a shortcut around governance and human review.
How to start
Begin with a Fit Check and one sensitive piece of work. The stack should be designed around actual operating needs, not assumptions.
Next step
Need AI without losing control of sensitive work?
Bring one sensitive piece of work and the constraints around data, access, review, and audit. The first conversation should clarify whether a private stack is justified.