Plan a private AI deployment

An on-premises AI system runs on hardware at your premises. A private-cloud deployment runs in a cloud environment with access and network boundaries configured for your organization. Neither label proves that every part of the workflow stays inside that boundary.

Start with processing-location requirements, the number of people using the system at once, the document workload, and the available operating support. Then compare hosting options against those requirements. A Canadian cloud region is a placement choice; confirm separately where backups, logs, external services, and support access operate.

Use representative documents and simultaneous users to measure response time before buying capacity. Model size, context length, retrieval design, and document processing all affect the result. A hardware quote without a workload test leaves the performance question unanswered.

The following is a reference architecture for planning, not a diagram of a deployed client system. Its purpose is to make every processing step and data copy explicit.

1. A staff member signs in through the organization’s identity provider. The application checks access to the chosen workspace.
2. A retrieval service searches approved documents using that person’s permissions. A document index may hold extracted text and numerical representations used for search.
3. The model receives the question and permitted passages, then generates a draft with source references.
4. The application presents the answer and controls access to conversation history. Logging and retention follow the approved policy.

Draw a separate path for document ingestion: the source repository, text extraction, any OCR service, the embedding model, and the index. Mark each network connection. A locally hosted chat model paired with an external document-processing API still sends data outside the local deployment.

Test that a user can retrieve an authorized document and cannot retrieve another team’s material, including through a paraphrased question. Repeat after changing source permissions and removing the user from the team. Define how quickly access changes must take effect across indexes, caches, and saved conversations.

Treat uploaded documents as untrusted content. A document can contain instructions intended to redirect the assistant. Keep retrieved content separate from system instructions, restrict available tools, and require approval before an assistant sends information or changes another system. OWASP describes these risks in its prompt-injection guidance.

The confidential client-data guide covers client separation and retention decisions. Those decisions belong in the acceptance criteria, alongside answer quality.

Identify who patches the operating system, application, model runtime, and document connectors. Record how updates are tested and how the team returns to the previous version if a change breaks answers or access controls.

Back up the configuration and records needed to recover the service. Some indexes can be rebuilt from source documents; others contain additional state that needs its own recovery plan. Test a restore and record what was recovered, how access was verified, and which dependencies were unavailable.

Keep a repeatable evaluation set for model changes. Include questions with known answers, restricted documents, and attempts to make the assistant follow instructions embedded in a file. Approve a new version only after reviewing the differences.

The brief should identify the approved task, source systems, user groups, data-processing locations, expected load, retention rules, recovery requirements, and release owner. That gives suppliers a common scope to price and gives the firm a basis for accepting the work.

If your workflow already fits Microsoft 365 or Google Workspace, compare the existing business assistant before committing to separate infrastructure. Our Private AI, Copilot, and Gemini guide explains that decision.

Bring the brief to a managed Private AI deployment review to scope the build and ongoing operation together.