Models deployed on your own hardware
For organizations that cannot send data out, the model comes in, running on your GPUs, inside your network.
Some organizations cannot send data to a third party, not because of preference but because of regulation, contract, or classification.
The local deployment runs the model and the workspace on hardware you own, with no inference leaving your network.
Why local
Data residency, regulatory constraint, air-gapped environments, and contractual obligations to clients who forbid third-party processing. In each case the blocker is not the model’s capability, it is the network path.
We run on our own GPUs and our own serving stack, so the same stack can be installed inside your walls.
What runs inside
The model, the retrieval index, the workspace, and the tool layer. Nothing about a request, whether prompt, documents, output, or telemetry, crosses the boundary unless you configure it to.
Updates without exfiltration
New model versions arrive as signed artifacts you install. Evaluation runs against your own held-out set before you promote a version, so you are not taking our word for a regression.
What you give up
Honestly: some speed of iteration. A local deployment lags the hosted one, and features that depend on centralized infrastructure arrive later or not at all. That trade is usually worth stating plainly before a contract rather than after.
Who this is for
Regulated industries, government and adjacent agencies, and enterprises whose own customer contracts prohibit third-party processing.
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