Local & Private AI Deployments
Evaluate local and self-hosted models against your hardware, data handling and workload requirements.
Discuss Your Project ↗Start with a concrete outcome.
This service is for founders and teams with a specific workflow, existing system or deployment challenge. We define success in terms of the work the system must perform, the constraints it must respect and the failures it must handle.
Where this service helps
A private deployment starts with the workload and the data boundary, rather than a model-size recommendation. Evaluate task quality, latency, context needs and concurrent usage on the intended hardware. A model loading into memory is only the beginning of a useful test.
Questions we resolve
- Which data must stay within your infrastructure?
- What are the actual task and concurrency requirements?
- Who will maintain the model, runtime and access controls?
Potential deliverables
A workload evaluation, hardware considerations, deployment configuration and reproducible tests. The final scope depends on your current systems and the evidence gathered during assessment.
How the engagement works
- Understand the workflow and review the current system.
- Identify the highest-risk assumptions and agree acceptance criteria.
- Validate with a bounded prototype or assessment.
- Implement, verify and document the agreed scope.
What to bring to the first discussion
A description of the current process, the tools involved, your data handling requirements, the intended users and your timeline. Avoid sending credentials or confidential documents through the inquiry form.
Scope and limitations
Hardware compatibility and speed are verified with the chosen setup. No configuration is recommended from invented benchmark measurements.
Related engineering work
Understand the work before scoping it
Pick a problem you are working on. Each guide includes a method, sources and a tool or example to try.