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Private LLM infrastructure

On-Premise LLM Deployment in Canada

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.

Canadian companies face a particular version of this problem: their own enterprise and public-sector customers often prohibit data crossing into the United States, which rules out most hosted model APIs regardless of what the vendor's security page says. Combined with a smaller senior AI talent pool and salaries competing directly with US remote offers, that makes a private deployment plus an external specialist a common shape here.

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 or CAD, 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.

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 Canada

What actually applies here.

Regulation and data

The blocker is usually written into a contract your customer gave you rather than into a statute. Once a client says their data cannot be processed in the United States, hosted model APIs stop being an option and the conversation becomes an infrastructure question. Running inference on hardware you control, in the country you need it, settles it.

Contracting and payment

You contract with an individual consultant based in India. Invoices in CAD or USD, paid by Wise or bank transfer. Your accountant treats an overseas supplier in the usual way, and any paperwork they need is provided before work starts.

Working hours

Four or more hours of daily overlap with Eastern Time, with calls in your morning.

Can the deployment stay entirely within Canada?

Yes. On your own hardware, or in a Canadian region of your own cloud account. For teams whose customers prohibit US processing, this is often the only architecture that clears procurement.

How much overlap is there with our working day?

Four or more hours with Eastern Time, with calls scheduled in your morning and same-day replies on working days.

Do we keep control of the accounts?

Yes, throughout. You create and hold the cloud accounts, access is named and limited-privilege, and it is removed at handover with written confirmation.

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.

Full detail

Private LLM Infrastructure

Self-hosted LLM deployment on hardware you own. vLLM serving, private networking, validated output.