ON-PREMISE AI — HARDWARE, MODEL AND AGENT FROM ONE VENDOR
The zenpAI appliance combines hardware, local model, and agent orchestration into a controllable AI system within your own infrastructure. External AI dependencies, manual workarounds, and scattered pilot solutions are migrated into an internal operating environment. That is exactly where true data sovereignty begins: with control over processing, access, logs, and operations.
Public APIs are not infrastructure. They are a lease, in a foreign jurisdiction, with a monthly termination clause.
Training data, process logic, and internal company knowledge do not belong in someone else's operating environments. When AI works with this information, what matters is not just data residency but control over access, processing, models, logs, and results.
The stack is not the problem. What matters is seamless integration into production operations.
Buying GPUs is easy. Installing them in a regulated production environment that respects GDPR, BSI baseline protection and your maintenance window — that is the work. Most AI software is cloud-optimized, not for your rack.
Consulting often ends at the roadmap. We go all the way into operations.
Many AI projects end with a strategy, a prototype, or a recommendation. For us, that is where the real work begins: we deliver an AI system that runs in your infrastructure, can be taken over by your IT, and is safeguarded with maintenance, monitoring, and clear operational documentation.
All requests are processed entirely on-premise: no data leaves your organization, no external interfaces, no cloud dependencies. This meets strict GDPR and BSI requirements and gives you full control over sensitive information.
Most vendors deliver one layer: hardware, model, or application. But the real value only emerges when these layers work together — when AI doesn't just answer questions but supports business processes and connects systems. We deliver hardware, local model, and agent layer as one integrated system for operation within your infrastructure.
The agent layer is the conductor of the system. It decides which model, which tool, or which specialized agent is needed for the next step — from a single RAG agent to the interplay of several specialized agents. It holds context, connects systems, and ensures that tasks are carried out reliably.
At the same time, it sets the guardrails for operations: access, tool calls, and relevant system actions are logged locally, error paths are defined, and processes remain traceable. The result is not just automation, but responsible AI governance.
The models are selected, optimized, and operated locally to fit your data, processes, and hardware requirements. Alongside LLMs, embedding, reranking, or specialized domain models can also be used. Your models and data stay in your hands. And when better models appear, the stack stays upgradeable.
We deliver a GPU server designed for your use case, your number of users, and your operational requirements. It is integrated into your existing IT infrastructure — including monitoring and support. Not an outsourced AI service — a system that runs under your control and belongs to you.
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Many deliver components. The question is who brings them together.
| SOVEREIGN CLOUD | SYSTEMS HOUSE / INTEGRATOR | AI SOFTWARE STARTUP | ZENPAI | |
|---|---|---|---|---|
| Hardware · your rack | ||||
| Local model | ||||
| Agent layer / application | ||||
| System integration across all layers | ||||
| Operation after go-live Monitoring & Support |
The difference is not a missing layer — but who brings them together and stays responsible once the system is running.
You rent the cloud — every request costs extra. You own the appliance — more usage costs nothing extra. After the break-even, the gap widens every year.
The biggest hidden on-premise cost isn't the hardware — it's ongoing operations, something we walk through openly with you in the intro call. By default we hand everything over to your IT; on request we support you optionally. When the appliance pays off depends on your usage intensity (break-even typically 1.5–3 years). We build the exact calculation with your real volumes — methodology based in part on Lenovo Press LP2368.
We look at your infrastructure together and tell you honestly whether zenpAI fits — a technical assessment, not a sales pitch. You decide the next step.