Defined data boundaries
Understand where information travels and which services can access it.
Trusted and Supported By
Control built into the design
Controlled access
Connect AI capability to the permissions and responsibilities of your users.
Operational visibility
Plan evaluation, monitoring, maintenance, and ownership alongside deployment.
Architecture around your needs
Deployment architecture
Choose an environment around your infrastructure and data constraints. Compare on-premises, private-cloud, and hybrid options with the work your team needs to support.
- Review infrastructure and data boundaries
- Compare deployment options and tradeoffs
- Clarify capacity and operational requirements
Knowledge connections
Make approved company information available to authorized users. Shape retrieval around the sources, access rules, and source visibility the workflow needs.
- Identify approved knowledge sources
- Plan retrieval and source references
- Respect existing information access boundaries
Identity and permissions
Define who can use the environment and who is responsible for administering it. Connect user access to the permissions and responsibilities already present in your organization.
- Map user roles and access needs
- Define administrative responsibilities
- Plan identity connections and permission checks
Evaluation and operations
Plan for operation alongside deployment. Establish the evaluation, logging, maintenance, and handover practices needed to understand behavior and support the environment.
- Evaluate behavior on representative tasks
- Agree on logging and monitoring requirements
- Prepare maintenance ownership and handover
Where it could work
Illustrative applications for your team.
Internal knowledge assistant
Help authorized staff find information in approved company documents.
Controlled document processing
Extract and summarize information within an agreed environment.
Private AI workspace
Provide a defined place for teams to use approved models and workflows.
Before we begin
A few practical questions.
Does private AI mean on-premises?
It can. We compare on-premises, private-cloud, and hybrid options against your data boundaries, existing infrastructure, capacity, and operational needs.
Where will our information travel?
We map the proposed data flow, including model hosting, retrieval, external services, and logging. Those boundaries are agreed before deployment; the answer depends on the selected architecture.
Can access follow our existing permissions?
We review approved knowledge sources, user roles, identity systems, and access rules. Retrieval and permission checks are designed around the information each user is authorized to access.
Can it connect to our existing systems?
Integration depends on the available interfaces, permissions, data sources, and selected architecture. We review those requirements when defining the deployment scope.
What happens after deployment?
Evaluation, logging, monitoring, maintenance responsibilities, and handover are planned alongside the environment. Ongoing support and improvements are agreed as part of the scope.
Does private deployment guarantee compliance?
No. Hosting is one part of the design. Security and compliance also depend on configuration, access controls, organizational processes, applicable requirements, and ongoing operation.
Build on your next step
Plan your AI environment
Bring your data constraints, available infrastructure, and the tasks your team needs the environment to support.