Solutions · Infrastructure
For organizations deciding where their AI systems and data should run: we build custom self-hosted AI environments (LLM and RAG) and deliver hosted, managed websites. Cloud and hybrid options are scoped per engagement, so the deployment model follows your data, obligations and budget, not a vendor’s preference.
Self-hosted AI environments
Model choice
Cloud and hybrid, scoped per engagement
Governance across all three
Deployment models
There’s rarely one right answer for a whole organization. The decision usually turns on four questions: how sensitive the data is, what your contracts and regulators require, how steady the workload is, and who will operate the system after launch.
Where data lives
In a cloud provider’s data centers, in the regions you select.
Who operates what
The provider runs the hardware and managed services. Your team, or an operator you appoint, owns configuration, access, monitoring and cost.
Typical fit
A fast start, variable demand, public-facing applications, and teams without their own data-center staff.
Plan for
Data-residency terms, usage and egress costs, and how tightly you depend on one provider’s managed services.
Where data lives
On servers you own or lease, on premises or in a private environment inside your boundary.
Who operates what
Your team, or an operator you appoint, runs the hardware, updates and security. Models, data and logs stay inside your environment.
Typical fit
Sensitive data, strict residency or contractual limits, steady heavy workloads, and organizations that want to own their models and workflows outright.
Plan for
Capacity planning, patching and on-call staffing, and hardware lead times.
Where data lives
Sensitive data and the models that use it stay in your environment; burst compute, public-facing apps or less sensitive workloads run in the cloud.
Who operates what
Responsibilities split by workload, with each boundary documented and assigned to a named team.
Typical fit
Mixed data sensitivity, a migration under way, or cloud services you want to keep while core systems move in house.
Plan for
Consistent identity, logging and policy across both sides, and the integration work between them.
Governance isn’t a fourth location. It’s the layer that spans all three: who can reach what, what gets logged, where a person has to approve, and how long data is kept. We design it into the first blueprint rather than adding it after launch.
What we build
Infrastructure work is scoped per workload. We build custom self-hosted AI environments, designed around the models you choose. Cloud, hybrid, data-platform and migration work can be scoped per engagement.
Private language-model and retrieval (RAG) environments that run inside your boundary, so prompts, documents and outputs stay under your control.
Environments that work with leading commercial and open-source AI models, designed so changing models doesn’t mean rebuilding the platform.
Scoped per engagement
Accounts, identity, networking, logging and cost controls for AI and application workloads on major cloud providers.
Workload-by-workload placement across your environment and the cloud, with one identity and logging model spanning both.
Pipelines, warehouses and retrieval stores that give AI systems and reporting a governed, documented source of truth.
Staged moves off legacy hosting or between deployment models, with rollback paths and parallel running before the old system is retired.
Refuse to rent your intelligence.
It isn’t an argument against the cloud. It means your data, models, workflows and intellectual property stay yours wherever they run, and the architecture is chosen to keep it that way.
Engagement path
01
Inventory systems, data classes, residency and contract obligations, and who operates what today.
02
Choose a model per workload and design identity, networking, logging and approval points before anything moves.
03
Build the target environment and migrate in stages, with old and new running side by side through acceptance checks.
04
Hand over to your team or the operator you choose; what is handed over is set in the scope.
What’s the difference between cloud and self-hosted AI?
In a cloud deployment, a provider runs the hardware and managed services, and your workloads run in its data centers. In a self-hosted deployment, the models, data and logs run on infrastructure you own or lease, operated by your team or an operator you appoint. Cloud usually starts faster; self-hosted gives you more control over where data lives and how the system is configured.
What is a hybrid deployment?
A hybrid deployment splits workloads: sensitive data and the models that touch it stay in your environment, while burst compute, public-facing applications or less sensitive services run in the cloud. It works when every boundary is documented and one identity and logging model covers both sides.
Is OrenGen against the cloud?
No. “Refuse to rent your intelligence” is about ownership, not location: your data, models, workflows and IP stay yours wherever they run, in the cloud or on infrastructure you control.
Which AI models do you work with?
We work with leading commercial and open-source AI models. The deployment model narrows the choice: a fully self-hosted environment runs open-source models inside your boundary, while commercial models are usually reached through the provider’s API or cloud platform.
Which security authorizations does OrenGen hold?
OrenGen does not currently hold CMMC, FedRAMP, DoD Impact Level, CJIS or HITRUST authorizations, is not a 21 CFR Part 11-validated system, and does not maintain corporate personnel security clearances. Our current registrations and certificates are listed on Credentials & Registrations in the Trust Center.
Where do we start?
With a Mission Brief. We map your workloads, data sensitivity and obligations, then recommend a model for each workload rather than one answer for everything.
Mission Brief
Bring your workloads, data classes and obligations. We’ll map which belong in the cloud, which stay in your environment, and what governance has to span both.
