Model and data stay in your network
The inference path runs behind your own controls. Model weights, regulated holdings, and inferences never travel to a vendor region.
Services
On-premises AI infrastructure
On-premises AI infrastructure is the right-sized compute, storage, and inference stack installed inside your network. It is the foundation for every activity in your organization that carries professional secrecy, data residency, or confidentiality - in Legal, Finance, and Technology. The model and data never leave your systems. A shared public cloud cannot match that protection.
What this capability holds
The hardware stack this capability installs is what the delivery's Building and Validation stage sizes and validates against your own workload before any large capital commitment.
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Two directions of exposure, both defended
A model touching regulated data ingests those holdings and emits inferences that may carry regulated content. The on-premises footprint holds both directions inside your network.
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Nothing here is recoverable
A privilege break does not re-seal. A residency breach is a regulatory event. A model moved outside your network is no longer under your exclusive control.
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Why a shared cloud tenant falls short
The API works for an internal demo. A regulated workload inherits the vendor posture rather than your own. That makes it indefensible.
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Sized against the real workload
Performance under your real document set, memory under real concurrency, and cost across the inference path are measured before procurement, not estimated on a slide.
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Validated, not estimated
The build stage sizes the footprint and proves it against the same control posture the production deployment will hold, before any large capital commitment.
Sizing validated on our RTX Pro class bench before any hardware is procured. Building and Validation GPU Lab
Served verticals
We size infrastructure in our GPU lab against your real workload before capital commits. The environment is private, local-first, and inside your network, integrated with the storage, identity, and CI/CD you already run. Every sizing decision is documented and handed to your team.
Integrated with the systems you already run
The systems named on the card are a sample of completed integrations, shown for breadth rather than completeness. They span the document formats, business systems, model runtimes, and media surfaces the delivery wires in. The toolchain for each build is chosen for best fit, so nothing on the card is a required stack.
Where the proof lives
The framing above names a capability. The working systems are produced directly inside the build stage. There we size the hardware stack, build the model around your data, and validate controls against production standards. The resulting system is the working deliverable produced before any capital commitment, so you procure only what the validated prototype proves necessary.