The zenpAI appliance combines hardware, a local model, and agent orchestration into a controllable AI system on your own infrastructure. On this page we show the components in detail: security architecture, stack, hardware classes, and operations.
Security here is not an add-on feature, but part of the architecture: no shared cloud hardware, no external inference, no third-party administrators on your data. Your data stays where it belongs — with you.
All inference, all model weights, all training pipelines remain on your hardware. No sub-processor, no third country.
Every login, every tool call, and every relevant response is logged locally. This creates traceability for internal controls, compliance processes, and AI Act documentation.
Seven layers, one responsibility. Proven datacenter software, not a hobby rig: a hardened Linux with optimized drivers, Kubernetes on GitOps principles above it — versioned and auditable — and at the top the agent layer that orchestrates — delegating tasks, keeping state, controlling flow — while setting the governance.
What actually runs in the rack — from the application down to the hardware.
Three build sizes, matched to your load. Standard configuration based on the current Blackwell generation; the final GPU, VRAM, and PSU redundancy are set jointly after your load and latency profile.
User counts depend on model and load. The appliance grows with you: it can be scaled up at any time — we add GPU capacity as your needs grow. On request.
Anyone can buy hardware — the real work is delivery: integration, data remediation, agentic workflows. We take it off your hands, from process assessment to ongoing operations — in clearly defined phases, billed per phase — the timeline varies by customer and scope. If the first phase shows it is too early, we say so, before any hardware is ordered.
Assessment: where does it hurt, which data, which tools? We pick the first use case and show a concrete result fast — with the honest option to stop afterwards.
Map data paths, define the goals. Most of it is data remediation — making scattered records production-ready.
The appliance is racked and connected. The model is fine-tuned on your domain and quantized for your GPU.
Agent layer set onto your process, pilot operation, training for your IT, full handover. You take over, we stay on call — no lock-in.
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An on-premise AI has to be operated — monitored, updated, maintained. By default we hand everything over to you so your own IT can run it. But if you prefer, we take operations on entirely — remotely, for a fixed monthly price. No token fees, no usage billing, no surprises.
Not part of the monthly price: the hardware as a one-time purchase (see §04) and — for larger configurations — cooling. Monthly price on request — depending on hardware class and the response time you need.
Specific hardware class, model family, and integration depth are not set by a configurator but together with you — once we understand your data, systems, and first use case.