Service AI Infrastructure

Keep your data inside your walls.

Not every organization gets to send its data to someone else's API. Regulated records, classified material, and competitive IP need to stay inside the perimeter — so I build the infrastructure that makes that possible: open-source LLM selection and tuning, GPU strategy, inference management, and on-prem or air-gapped appliances. Full control, nothing leaving.

On-prem Inference runs inside your network

Open-source Models you host, tune, and own

Nothing leaves No data crosses the perimeter

A hosted API is the right call for most companies most of the time. It's not the right call for everyone. If you're sitting on classified material, regulated health or financial records, or IP you can't risk in a third party's logs, "just call the API" isn't a real option — it's a compliance finding waiting to happen.

That's the work I do here: infrastructure for organizations where the model has to come to the data, not the other way around. If your data can't leave the building, neither should your inference.

What an on-prem build actually requires

This isn't "download a model and run it." Getting open-source inference to hold up under real load, inside your constraints, takes three things done properly.

On-prem / local deployment & hardware builds — sized, procured, and racked (or air-gapped) for the workload you actually have, not a vendor's reference architecture.
Open-source LLM selection & tuning — the right base model for your task and hardware budget, fine-tuned or adapted on your data instead of forced to fit a general-purpose default.
GPU strategy & inference management — utilization, batching, quantization, and routing decisions that determine whether your hardware spend earns its keep or sits idle.

For most companies, the cloud is the right answer. For the ones where it isn't, "we'll figure it out later" isn't a strategy. The data stays inside your walls, or the project doesn't ship.

The four pieces of an on-prem stack.

Hardware, models, GPU strategy, and operations — designed together, not bolted on after a proof of concept.

01 / Deployment

On-prem deployment

Local and air-gapped deployment, sized and built for your actual workload — from a single inference box to a racked cluster behind your firewall.

02 / Models

Open-source LLMs

Model selection, evaluation, and fine-tuning against open-weight LLMs you can host, inspect, and own outright — no vendor API in the critical path.

03 / Hardware

GPU strategy

Right-sized GPU procurement and cluster architecture — capacity planning that matches spend to workload instead of over-buying "to be safe."

04 / Operations

Inference management

Batching, quantization, routing, and monitoring that keep latency and cost in line once the system is live and under real traffic.

On-premOpen-source LLMsGPU strategyInference mgmtAir-gapped optionFull control

From constraint to running cluster.

Fixed scope, fixed price, weekly demos during the build.

01Assess

Map the data and the constraint

We identify exactly what has to stay on-prem and why — regulatory, contractual, or competitive — and size the workload before any hardware gets ordered.

02Select

Choose and benchmark the model

We evaluate open-source LLMs against your task and hardware budget, then tune the model that actually earns its place instead of the one with the biggest name.

03Build

Stand up the hardware and the stack

GPU procurement, deployment architecture, and the inference stack go in together — on-prem or air-gapped, sized to what you'll actually run.

04Operate

Manage inference under real load

We tune batching, quantization, and routing against latency and cost once the system is live, and keep tuning as usage grows.

If your data can't leave, let's build where it lives.

If the cloud isn't an option for you, that's not the end of the conversation — it's the starting constraint. I'll tell you what an on-prem build costs and what it takes before we start.

Let's talk