Can my company own the AI model a vendor builds?

Yes, if the contract says so — and many vendor contracts do not, so read the IP clause before the pricing page. In a Blankpage engagement, the client owns the fine-tuned weights, adapters, training code, evaluation suites, and documentation outright, with full transfer at handover. Ownership means you can run, modify, or move the model without us.

Does our data train anyone else's model?

No. In a private LLM deployment your data stays on infrastructure you control, is used only to build and evaluate your own model, and is never pooled, shared, or used to improve any other client's system or any vendor's base model. We put that commitment in the contract, not in a FAQ you cannot enforce.

What is a private LLM and does my business need one?

A private LLM is a language model running on infrastructure your organization controls, so sensitive data never leaves your perimeter. You likely need one if you operate under regulatory or confidentiality obligations, handle data you cannot send to third parties, or want the model itself as owned IP. If none of those apply, a well-governed commercial API is often the cheaper, honest answer.

How long does private LLM development take?

Two weeks for a proof of concept that runs a candidate model on your data and prices the full build. A production deployment typically ships in a 6-12 week Design & Build, including evaluation, red-teaming, and handover to your team. Timelines stretch mainly on data readiness, so we assess that in week one, not month three.

Private LLM Development: Client-Owned AI Models

Private LLM development for institutions that cannot let data leave the building. Your model, your weights, your infrastructure — and a hard no to training anyone else's system.

What is a private LLM?

A private LLM is a large language model deployed on infrastructure the organization controls — on-premises or in a single-tenant private cloud — so prompts, documents, and fine-tuning data never pass through a shared third-party service. In a fine-tuned private build, the organization owns the model weights themselves, not a subscription to someone else's model.

That ownership is the point. Private LLM development turns AI from an operating expense governed by someone else's terms of service into an asset on your side of the ledger.

The problem: on public APIs, your data has a commute

Every prompt sent to a shared AI service is data in transit through infrastructure you do not control, under terms you did not write. For many companies, that is an acceptable trade. For banks, insurers, law firms, healthcare operators, and public-sector institutions, it is often a non-starter — because of GDPR, CCPA/CPRA, sector rules, or plain confidentiality obligations to clients.

The usual vendor answer is a reassurance page. Ours is architectural: the model runs where your data already lives, inside your security perimeter and inside your compliance frame — ISO 27001, SOC 2, or whichever standard your auditors hold you to.

Should we fine-tune a model or use RAG?

Retrieval-augmented generation (RAG) connects a model to your documents at query time without changing the model; fine-tuning changes the model's weights using your data. RAG is cheaper, faster to deploy, and easy to keep current, which makes it the right starting point for most organizations. Fine-tuning earns its cost when you need consistent domain behavior, specialized vocabulary, or output structure a prompt cannot reliably produce. Mature systems usually combine both.

Radical honesty, applied: if RAG on an open-weight model solves your problem, we will tell you not to pay for fine-tuning. We talk more clients out of it than into it.

How Blankpage runs private LLM development

The build follows a fixed sequence. Model selection: open-weight candidates evaluated against your actual tasks, not leaderboard scores. Data pipeline: curation, de-identification where required, documented lineage for every training example. Deployment: on-premises GPU or private-cloud tenancy, sized to your real throughput, with cost modeling before commitment. Evaluation and red-teaming: accuracy, refusal behavior, and leakage testing, with results you can hand an auditor. Hardening and handover: monitoring, runbooks, and your team trained to operate the system without us.

Most private LLM builds ship inside a 6-12 week Design & Build engagement. When agents or automation sit on top of the model, we design them together — see AI agent development — so the model's boundaries and the agents' autonomy controls are one system, not two.

Who owns the model? You do. In writing.

The two questions that unlock or kill these deals get answered here, plainly. Who owns the model a vendor builds on your data? With us: you do — weights, adapters, training code, evaluation suites, and documentation, transferred in full at the end of the engagement. Does your data train anyone else's model? No. Hard no, contractually: your data builds your model, on your infrastructure, and goes nowhere else.

The long version of the ownership question — IP, data rights, build-vs-buy — is in Who Owns a Custom AI Model?. For regulated deployments, our AI governance consulting practice documents the controls your compliance team will ask about before your board does.

Start with two weeks, not a platform commitment

You do not need to bet the infrastructure budget to find out if this works. A fixed-price AI proof of concept puts a private model on your data in two weeks, with an architecture memo and a cost-to-production estimate. Go/no-go in writing. And if a public API with proper controls serves you better, that will be our recommendation — clean exit, no lock-in, no hard feelings.

Frequently asked questions

Can my company own the AI model a vendor builds?: Yes, if the contract says so — and many vendor contracts do not, so read the IP clause before the pricing page. In a Blankpage engagement, the client owns the fine-tuned weights, adapters, training code, evaluation suites, and documentation outright, with full transfer at handover. Ownership means you can run, modify, or move the model without us.
Does our data train anyone else's model?: No. In a private LLM deployment your data stays on infrastructure you control, is used only to build and evaluate your own model, and is never pooled, shared, or used to improve any other client's system or any vendor's base model. We put that commitment in the contract, not in a FAQ you cannot enforce.
What is a private LLM and does my business need one?: A private LLM is a language model running on infrastructure your organization controls, so sensitive data never leaves your perimeter. You likely need one if you operate under regulatory or confidentiality obligations, handle data you cannot send to third parties, or want the model itself as owned IP. If none of those apply, a well-governed commercial API is often the cheaper, honest answer.
How long does private LLM development take?: Two weeks for a proof of concept that runs a candidate model on your data and prices the full build. A production deployment typically ships in a 6-12 week Design & Build, including evaluation, red-teaming, and handover to your team. Timelines stretch mainly on data readiness, so we assess that in week one, not month three.