SIZE 01
Team
A single department or project team that needs private AI without a data room.
- Compact footprint
- Standard office power and cooling
- Retrieval over team document stores
Swif7AI: private AI inside your boundary
For organizations whose data cannot go to a third-party API, the cleanest answer to AI risk is architectural. Swif7AI runs the model inside your boundary, logs every prompt and response, and costs the same each month regardless of use.
Why a private stack
Most AI governance work is about managing the consequences of sending your data somewhere else. If the data cannot leave — because of a contract clause, a regulator, or a classification — then no amount of policy makes a hosted API appropriate.
Swif7AI removes the question. The model runs on hardware inside your boundary. Prompts, responses, and retrieved documents stay where they already are, under the controls you already run.
What you get
- Private, on-premises model hosting in three sizes
- Fixed-cost deployment with no per-token billing
- Audit-ready logging of every prompt and response
- Model selection you control, not a vendor roadmap
- Retrieval over your own document stores
- Encrypted, mirrored storage with redundant power and networking
Three sizes
Sized by the number of concurrent users and the document volume, not by a license tier.
SIZE 02Most deployed
Office
A whole office on private AI, with capacity for concurrent use across departments.
- Multi-GPU compute
- High-capacity memory pool
- Encrypted, mirrored storage
SIZE 03
Data room
Rack-scale deployment for organizations putting every employee on private AI.
- Rack-scale GPU compute
- Redundant power and failover networking
- No single point of failure in storage
Specifications are sized to the deployment. We quote after a requirements session rather than publishing a configuration that will not match your estate.
Deploying it is not the same as securing it
A private model removes the third-party data question. It does not remove prompt injection, excessive agency, or the risk that retrieval exposes a document to someone who should not see it.
Where Swif7AI is deployed with retrieval or tool access, we recommend testing it the same way we would test a hosted application.
Compliance position
Running inference inside your boundary makes several obligations materially easier to meet, because the data never crosses a boundary you would otherwise have to assess, document and monitor.
It does not by itself make you compliant with anything. The control set still has to be implemented and evidenced, which is what our readiness work covers.
Work out whether private AI is the right answer
Sometimes it is not, and a governed hosted deployment is cheaper and sufficient. We will tell you which one your obligations actually require.