Customer-controlled deployment experience
Inspect our contribution to a private-markets platform and the five client environments live at engagement end.
Private Deployment
Private AI deployment in your cloud or on premises.
Define where the application, documents, retrieval layer, and model calls run. We support customer cloud, on-premise, tenant-isolated managed service, and approved hybrid deployment patterns.
Private hosting and private inference are separate. A privately hosted application can still send information to an external model or document-processing service. We map the complete data path and agree the processing boundary before implementing the deployment.
01
Why sensitive workflows need deployment choices
For confidential diligence, regulatory compliance, legal, and professional-services workflows, the deployment model matters. We design around the client's approved data boundary, whether that means private cloud, VPC, tenant-isolated, or on-premise paths.
02
Private cloud and on-premises deployment
Application components can be packaged for an approved cloud or customer-controlled environment. We confirm platform compatibility, network paths, model endpoints, and operational responsibilities for the specific deployment. Private hosting alone does not remove external model calls.
03
Security controls and identity integration
Deployments can integrate with enterprise identity providers such as Okta or Entra ID using SAML/OIDC, enforce RBAC, use customer-managed encryption where required, and preserve audit trails for sensitive workflows.
04
Self-hosted open-source model integration
If processing must remain on approved infrastructure, we assess self-hosted inference and document-processing options. The design must account for every service and integration, with tradeoffs around latency, quality, cost, and maintenance.
Where this can help
Start with the change your users or business need.
You may want a better customer experience, more capacity for your team, or a faster way to reach an important decision. We help you turn that need into the right product or system.Security and data access
Review how we approach permissions, data processing and human approval within agreed project requirements.
A deployment checklist for advisory teams
Consider hosting, inference, document processing and operating ownership before sharing engagement material.
Your first release
Review the full data path before selecting the hosting model.
Application hosting and AI model processing are separate decisions. Map document parsing, retrieval, inference, logs and every connected service before agreeing the deployment boundary.- 01
A data-flow and access plan
Identify where each component processes and stores information. Agree the allowed endpoints, identity integration, permissions and retention requirements.
- 02
A tested deployment
Package and connect the agreed application components. Test access boundaries, representative outputs and recovery from integration failures.
- 03
An operating handover
Document configuration, monitoring, update procedures and responsibilities. Confirm who operates the application, model endpoints and infrastructure.
Confirm the required model quality, latency, capacity and operating cost using your workload. On-premises compatibility and hardware needs are assessed for the selected components.
Choose the right approach
Private cloud, hybrid or on-premises AI?
Select an architecture that fits your approved data flows and operating capacity. The location of the application alone does not determine where inference happens.| Approach | Useful when | Check before choosing |
|---|---|---|
| Customer-controlled cloud | Your team wants to own the application infrastructure within its cloud account. | Model endpoints, document-processing services, telemetry and outbound connections. |
| Approved hybrid | Some services may use external endpoints under agreed processing and retention terms. | Exactly which information leaves your boundary and the terms for each service. |
| Self-hosted or on premises | The selected processing components must operate on approved customer infrastructure. | Model availability, licensing, compute, updates and capacity to operate every component. |
Deployment experience and its limits
Dotnitron contributed to a private-markets platform with five customer-controlled deployments live at engagement end. The case study states our role and the historical milestone; it does not establish that all model processing was local.
Read the private-markets deployment recordFAQ
Frequently asked questions
What is private AI deployment?
Private AI deployment defines and controls where the application, data-processing services, storage, retrieval layer, and model endpoints run. A VPC or on-premise application does not automatically mean every model call stays inside that boundary, so the complete data flow must be reviewed and approved.
Do we need specialized hardware for on-premise deployment?
Depending on the models used, GPU infrastructure may be required. We work with your IT team to specify the necessary hardware or design architectures that leverage CPU-optimized models where appropriate.
Does private-cloud hosting keep every AI request inside our network?
Not by itself. Model inference, document parsing, telemetry or other integrations may use external services. We review each component and its data flow against the boundary your team approves.
Can you implement self-hosted models instead of external APIs?
We can assess self-hosted inference for your workload. Model licensing, required hardware, output quality, latency and ongoing maintenance must fit the use case. The architecture is agreed after that assessment.
Who operates the system after deployment?
We agree ownership of infrastructure, application support, model endpoints, monitoring and updates as part of scoping and handover. The deployment plan names the responsibilities of your IT team and Dotnitron.
Technology partnership
Build with an OpenAI Select Partner.
We design and integrate OpenAI services within your approved architecture.Have something in mind?