When nothing is allowed to leave the building.
Where public cloud is legally barred, Pexon deploys open-weight models and vector search inside your own data centre. That covers GPU sizing, model deployment and quantisation, an offline retrieval pipeline, and access control bound to your existing directory, so nothing about the system depends on an outside provider.
Sometimes the cloud is simply not an option.
Regulation, contractual obligation or a classification decision can rule out sending data to a hosted model. That constraint is real, and it does not have to mean going without the capability.
What running it yourself requires
Hardware sizing
GPU selection and capacity planning against your actual workload, so the investment matches the use rather than a benchmark.
Model deployment
Open-weight models deployed and quantised for your hardware, trading precision against throughput deliberately rather than by default.
Local retrieval and access
An offline retrieval pipeline with access control bound to your existing directory, so entitlements come from the system of record.
What changes
- The capability becomes available where a hosted model was never permitted.
- There is no outside dependency to review, renegotiate or explain.
- Your data classification stops being the reason the project cannot start.
Sizing comes first: the most expensive mistake in on-premise AI is hardware bought before the workload was understood.
Sovereignty questions
Are open-weight models good enough?
For a large share of enterprise retrieval and extraction work, yes — and where they are not, that shows up in the eval harness before anything ships. The honest answer depends on the task, which is what the first weeks establish.
Who operates it afterwards?
Your team, by design. We hand over runbooks and documentation, and the managed operations tier exists only if you would rather we kept it running.
Not a sales call. An architecture call.
Thirty minutes with the architect who would actually run the engagement.