Your pricing strategy is a trade secret.
Don't hand it to someone else's model.
Private LLM hosting on a dedicated machine in EU-Central, for retailers and e-commerce teams running customer analytics, product copy, or demand forecasting without exposing purchase history and pricing strategy to a shared AI provider.
Not legal advice. This page describes GPUwerk's infrastructure and contractual commitments. It is not an opinion on your obligations for customer personal data or your rights over your own pricing and inventory strategy. Check with your own legal or privacy team before deciding what can run where.
Two different things, both worth protecting
Retail AI workloads carry two distinct risks at once: customer personal data under GDPR, and competitive data your business has no legal obligation to protect but every commercial reason to.
"Customer purchase history is personal data at scale"
A loyalty program's purchase history, browsing behaviour, and contact details are personal data under GDPR the moment they're tied to an identifiable customer. On a GPUwerk instance, that data runs on hardware assigned to your business alone. GPUwerk operates the machine but, under the data processing agreement, does not access your content except at your request for support or where a legal obligation requires it.
"Our pricing model is the one thing a competitor would pay to see"
Dynamic pricing logic, margin targets, and inventory positioning aren't protected by any statute the way personal data is, which makes accidental exposure through a shared AI vendor's logs or retention policy purely a self-inflicted risk. A dedicated single-tenant machine means no other retailer's workload, and no vendor's model training pipeline, is anywhere near that data.
"Merchandising teams already use consumer AI tools for copy and analysis"
Drafting product descriptions or running a quick sales trend summary through a public AI tool is routine and rarely reviewed for what data went in with it. A sanctioned alternative on your own instance, same chat interface, keeps that workflow off shared infrastructure. See the private ChatGPT setup →
Demand forecasting sits between the two risks
A forecasting model built on purchase history, seasonality, and store-level inventory touches both categories at once: the underlying transactions are customer personal data, and the resulting forecast is itself a competitive asset. Sending either through a shared API means trusting a third party's retention and access policies with both at the same time. Running the same pipeline on a dedicated instance keeps the raw transaction data, the model, and the forecast output on one machine your business controls, with no other retailer's traffic anywhere near it and no vendor able to see what your forecast actually predicts.
What's actually in the contract
For your privacy lead or IT lead to review directly.
| Question | Answer |
|---|---|
| Where does it run? | EU-Central, on a dedicated single-tenant machine assigned to your business |
| Who operates it? | PRINT IT! SE, a Societas Europaea registered in Prague, Czech Republic. No US parent entity. |
| Who can reach the instance? | Through the instance itself, only holders of your SSH keys; password login is disabled fleet-wide. GPUwerk keeps infrastructure administrator access to the underlying machine, as on any hosted service, and under the DPA does not use it on your content except at your request for support or where a legal obligation requires it. |
| Does GPUwerk see customer or pricing data? | No. We host the hardware and, under the DPA, do not access, read, copy, index or analyse workload content. |
| Sub-processors for the workload? | None, listed at /legal/sub-processors |
| DPA (GDPR Art. 28)? | Published at /legal/dpa, no charge |
| Data on termination? | Container and workspace volume deleted from the node, then the node is sanitised before reassignment. Filesystem deletion, not a cryptographic erase; export what you need before terminating, since it isn't reversible. |
Questions we get from retail and e-commerce teams
Is loyalty program data safe to run through an LLM?
That depends on where the model runs and who can reach it, not the model itself. On a GPUwerk dedicated instance in EU-Central, the machine is assigned to you alone and reachable only through your SSH keys; GPUwerk does not access, read, copy, index or analyse what runs on it except at your request for support or where a legal obligation requires it. Whether a specific use of loyalty data has a valid GDPR basis is for your own legal or privacy team to confirm.
Does GPUwerk see our pricing or inventory data?
No. GPUwerk operates infrastructure, not models. Pricing models, inventory data, and competitive analysis processed on your instance are handled by software you install, and under our data processing agreement GPUwerk does not access that content except at your request for support or where legally required.
Do you sign a DPA covering customer purchase data?
Yes, a standard GDPR Article 28 DPA is published at /legal/dpa at no charge, and there are no sub-processors for instance workloads.
Does GPUwerk train on our data?
No. GPUwerk runs infrastructure, not models. There is nothing for GPUwerk to train on, since we do not access instance content except as described in the DPA.
Related pages
Keep the pricing model and the customer file off someone else's servers.
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