Private LLM hosting for real estate

What real estate firms are actually handling

A property management company or brokerage that wants to use an LLM is usually looking at one of a few document types, and each one is sensitive in a different way. Tenant and buyer applications carry income statements, bank details, credit history, sometimes a copy of a passport or national ID, exactly the kind of personal and financial data that a data protection authority would expect you to treat carefully regardless of whether an AI model is involved. Feeding an applicant's pay stubs into a public chatbot to check affordability ratios sends that data to a third party with its own retention and training policies, which you may not have reviewed and the applicant certainly hasn't consented to in that form.

Lease negotiation terms are a second category: draft clauses, rent concessions offered but not accepted, the actual number a landlord will settle at. That's commercially sensitive in a way that has nothing to do with personal data protection, it's negotiating leverage, and it's the kind of text that shouldn't end up in a vendor's logs where a support engineer or a future model update could surface it.

The third category is the one firms tend to underweight: property valuation models and the assumptions behind them, cap rate models, comparable sets, discount rates tuned to a specific portfolio. A firm that's built a good valuation process over years has built something closer to proprietary IP than a spreadsheet. Running that process through a shared API means the inputs, and potentially the reasoning, pass through infrastructure you don't control.

What dedicated hardware changes

A Spark from GPUwerk is single-tenant hardware: nobody else's workload runs on it while it's allocated to you, and the workspace is wiped from the node before it's handed to another customer, as described in our Privacy Policy, section 12. The model you run, applications, lease drafts, valuation spreadsheets, stays inside that instance. We don't access, read, or analyse the content.

128 GB of unified memory handles the document-heavy side of the work well: extracting the numbers off a tenant application, redlining a lease clause against your standard terms, summarizing a stack of comparables. If you're running valuation models across a large portfolio in batches, or fine-tuning a model on your own historical deal data, the linked 256 GB cluster gives more headroom without moving the workload off hardware you control.

Where GDPR applies

Tenant and buyer data is personal data, often financial personal data, and processing it through any AI system puts you squarely inside GDPR obligations you already have for that data. Our Data Processing Agreement under Article 28 covers the infrastructure layer: it states that GPUwerk hosts the machine but does not access, read, or analyse what's on the instance, and that the controller, you, decides what's processed there. We are not your lawyer and this page isn't legal advice; if you're handling applicant screening or credit data under sector-specific rules in your jurisdiction, that's a conversation for counsel. What we can tell you plainly is where the instance runs (EU-Central) and who operates it (PRINT IT! SE, no US parent company), which tends to be a shorter conversation than getting the same answer from a large multinational AI vendor.

Practical starting points

A property manager might start narrow: having the model pull income, employment, and rental history off an applicant's uploaded documents into a standard intake form, cutting the manual data entry without the documents themselves leaving your instance. A brokerage handling commercial leases might use it to compare a proposed lease against a library of past deals and flag clauses that deviate from your standard terms, useful during a negotiation where you want a fast second read, not a public one.

On the valuation side, a firm with years of comparable sales and cap rate data might use a private model to draft a first-pass valuation narrative from that data, something an analyst reviews and adjusts rather than something that goes out unchecked. None of this requires the model to replace a licensed appraiser or a credit decision; it requires it to be fast, private, and pointed at data you'd never paste into a public tool.

Getting started

Deploy directly from the console if you know your workload, or start with an AI Opportunity Session to map your document types and existing systems, property management software, CRM, e-signature tools, before committing to a rollout.

Back up before you terminate. Terminating deletes the workspace and GPUwerk's recovery copy of it. Export tenant records, lease drafts, and valuation work before you stop paying for it.

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