Model selection
Choose models against the actual work, quality target, latency, license, and deployment requirements.
Managed AI Operations
Managed Private AI
More control, when the business case calls for it.
Implementations typically start in the mid-five figures; scope depends on model, data, integration, security, and hosting requirements.
Quick answer
What is Managed Private AI?
Managed Private AI is the design, implementation, and operation of an AI environment with controls tailored to your data, hosting, and business requirements. It may combine private inference, retrieval over your data, model adaptation, and evaluation. Elevated AI first tests whether those requirements justify the added responsibility compared with using existing provider APIs.
You may not need private AI.
Start with requirements and measured tradeoffs. A well-operated provider API can be the right answer.
| Decision | Provider APIs may fit when… | Private AI deserves evaluation when… |
|---|---|---|
| Data handling | Contractual controls and available deployment options meet your requirements. | Specific data boundaries or residency constraints require a different architecture. |
| Model capability | Available models meet the evaluated quality and latency targets. | You need deployment control or adaptation that a hosted service cannot provide. |
| Economics | Variable usage and managed infrastructure fit the workload. | Measured sustained demand justifies infrastructure and specialist operating costs. |
| Operating capacity | You want the provider to maintain the model-serving platform. | There is budget and ownership for serving, updates, security, and evaluation. |
A private environment is a system, not just a model.
Retrieval / RAG
Supply relevant approved information to a model at request time, with clear source and access boundaries.
Fine-tuning & adapters
Adapt model behavior where training is justified. An adapter is a targeted adaptation; neither path replaces evaluation.
Inference
Run the model to produce an answer. Hardware, capacity, availability, and usage still have operating costs.
Evaluation
Check output against representative examples and acceptance criteria before and after a change.
Ongoing ownership
Maintain access, integration, model versions, incidents, and the improvement plan over time.
Ownership should be clear before implementation.
You own your data, business-specific AI assets, and the work Elevated AI creates for you. Foundation models remain subject to their own licenses. Portability depends on those licenses and the agreed architecture.
Implementations typically start in the mid-five figures; scope depends on model, data, integration, security, and hosting requirements.
We document model licenses, hosting responsibility, data access, export options, and handover expectations in the agreed scope.
Fit
Built for teams with real operating work.
- Specific privacy or residency requirements
- A justified need for deployment or model control
- Workloads whose evaluated economics favor a private environment
An architecture matched to actual constraints. Explicit model and data rights. A plan for ongoing evaluation and operation.
Outside this scope: an automatic upgrade for every company; a promise to remove all inference or operating costs.
Quick answers
Do we actually need a private model?
You may not. Provider APIs can be a better fit when their data terms, capabilities, reliability, and costs meet your needs. We compare that path before recommending a private deployment.
What is the difference between RAG and fine-tuning?
Retrieval-augmented generation supplies relevant information to a model at request time. Fine-tuning changes model behavior through additional training; adapters are one way to make a smaller targeted model adaptation. They address different needs and both need evaluation.
What will we own?
You own your data, business-specific AI assets, and the work Elevated AI creates for you. Foundation models remain subject to their own licenses. Portability depends on those licenses and the agreed architecture.
What does a private implementation cost?
Implementations typically start in the mid-five figures; scope depends on model, data, integration, security, and hosting requirements. Infrastructure, inference, support, and ongoing operation also need a budget.
Can the system move to another provider?
We design for practical portability where feasible. The actual transfer options depend on model licenses, provider services, data formats, and architecture; they are documented before implementation.
The operating journal
Understand the work before you commit.
Make the next step concrete
Put an operator behind your AI.
Start with a two-week Blueprint. Leave with an operating plan and one improvement already in place.