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Private LLM infrastructure

On-Premise LLM Deployment in Minneapolis

The best On-Premise LLM Deployment in Minneapolis, at the best available price.

$0 upfrontPay per accepted checkpointSenior engineer, no agency markup

You have the GPU and you have the model. What is missing is the leg in between: the model exposed as a reliable private API, reached securely from your application, returning data you can trust. That is the whole engagement.

Minneapolis and St. Paul host a remarkable number of large corporate headquarters for their size, including health insurers, retailers, food companies and one of the world's densest medical device clusters. Software demand here is dominated by enterprise buyers in health, retail and medtech.

Medtech and insurance companies in Minneapolis want language models over device documentation, quality records and claims. A private deployment keeps that data under their control.

What you get

  • vLLM serving tuned to your GPU: quantisation on the native kernel path, KV cache sizing, continuous batching, prefix caching
  • A private network path with no public exposure, verified as a direct peer connection rather than a relay
  • Schema-constrained responses plus a deterministic QA gate, so a wrong answer is rejected rather than rendered
  • Documented baseline: tokens per second, time to first token, real concurrent capacity
  • Infrastructure as code in your repository, a runbook, and every credential held by you
Fixed-fee assessment, credited in full against the build.

No upfront payment and no escrow required. You hold every dollar until a checkpoint is delivered and accepted, and thirty days of defect correction is included. Scope and price are set on a call.

Invoiced in USD, payable by Wise or bank transfer.

Request a callback

You speak to the engineer who does the work. No sales rep, no deck.

No spam and no sales team. You talk directly to Neeraj.

Minneapolis, Minnesota

Who builds here

Medical devicesHealth insurance and careRetail and consumer goods headquartersFood and agribusiness
Best fit in Minneapolis

On-Premise LLM Deployment

Minneapolis medtech companies and health insurers hold regulated data that rarely can leave their environment, so a private model is usually where AI starts.

You are on it
A single GPU server lit in a dark data-centre aisle, the hardware a self-hosted model runs on
Architecture diagram of a private LLM deployment inside your network: your application calls a backend gateway, which reaches a vLLM model server running an open-weights model on your GPU over a private encrypted network with no public endpoint. Every response passes a deterministic validation gate before it returns to the application. Secrets sit in a managed store, the model port is bound to a private interface, output is review-only, and third-party model APIs are not used.
The request never leaves your network, and nothing reaches your users without passing the validation gate.
How you pay

Get it built at $0.

That is not a discount. It is when you pay. The work is split into checkpoints with acceptance criteria written down before anything starts, and each checkpoint is invoiced only after you have seen it and accepted it. No deposit.

$0 to start
You hold every dollar until a checkpoint is delivered and you accept it. No approval, no invoice.
Fixed cost, unlimited features
Or hire the team outright: one fixed monthly cost, unlimited feature development, any stack.
The engineer takes your call
The person on your first call is the one who architects and writes it. No account managers, no bench time.

A US agency quotes $50,000 to $150,000 for the same build and asks for 40 to 50% of it before a line is written. Account managers, project managers, sales commission and bench time. None of it appears in your product.

How it works

Three stages, nothing hidden.

01

Fixed-fee assessment

Five business days from the day access is in place. Your stack examined end to end, existing work classified as preserved or replaced with reasons, the connection design, and acceptance criteria written as testable statements.

02

Implementation in checkpoints

Around three weeks. Each checkpoint has written acceptance criteria agreed before work starts, and is invoiced only after you accept it. Appoint an independent technical reviewer if you want one.

03

Handover and closeout

One real request through your real application, on synthetic data, passing every QA rule. Runbook, recorded handoff, and all our access removed with written confirmation.

Working in Minneapolis

What actually applies here.

Regulation and data

The Minnesota Consumer Data Privacy Act took effect in July 2025 and adds a right that most state laws lack: consumers can question the result of a profiling decision and learn what led to it. Medical device software falls under FDA rules, and health insurers under HIPAA.

Contracting and payment

You contract with an individual consultant based in India rather than a US entity. Invoices are issued in USD and paid by Wise or bank transfer, with no payroll and no benefits load on your side. Whatever documentation your finance team or counsel needs from an overseas contractor is provided before work starts.

Working hours

Three or more hours of daily overlap with Central time, calls in your morning, and same-day replies on working days.

What does Minnesota's profiling right mean for an AI feature?

If your product makes decisions that produce legal or similarly significant effects, consumers can question the result, learn the reasons and, in some cases, have it reviewed. We build the logging and explanations that make that possible.

Do you work on medical device software?

We build with the documentation, traceability and testing a regulated route needs. Classification and submissions are led by your regulatory team.

We already have hardware and a model running. Is that a problem?

It is the ideal starting point. Existing work is classified during the assessment as preserved unchanged, preserved with changes, or replaced, with reasons, and nothing is replaced without your written agreement.

Can it be fully air gapped?

Yes, including model and dependency mirroring, offline updates and local evaluation, with no outbound network access at all.

Who holds the accounts?

You do, throughout. Cloud accounts are created and held by you, our access is named and limited-privilege, and it is removed at handover with written confirmation.