Data that cannot leave the building
Patient records, legal files, financials, proprietary data. When a third-party API is not an option, the model comes to the data instead, on hardware you own, inside your own walls.
AI Engineering · Local AI
Your AI, under your own roof.
Data that stays inside the building, costs that stop climbing per-task, and AI that answers in milliseconds. On-premise infrastructure: GPU servers, networking, and open models on hardware you own.
The right call for data that cannot leave the building, costs climbing per-task, or work that needs an answer in milliseconds. It is specced, sourced, wired in, and managed for you.
On-premiseAir-gap capableYou own the hardwareBuilt in Canada
When local AI is the right call
Most businesses run AI in the cloud. Some shouldn't.
Local AI is not for everyone, and we will tell you plainly if you don't need it. Here is where it earns its place.
It works when the internet does not
On-site inference keeps running through an outage, a remote location, or a workshop with no reliable connection. The intelligence lives where the work happens.
Predictable cost at steady volume
High, constant AI usage on a metered API adds up. Owned hardware turns a recurring per-task bill into a fixed asset on the books that you control.
Speed measured in milliseconds
A model on the local network answers without a round trip to a distant data centre. For real-time tools on the floor, that gap is the difference between useful and ignored.
The 60-second check
Do you actually need local AI?
Five quick questions. You get an honest answer on the spot, and if you want, we'll email a recommended starting hardware spec to match.
What we build on-site
The whole stack, wired and running.
Compute
GPU servers and workstations sized to your models and your volume, not over-bought, not under-spec.
Networking
The switching, cabling, and segmentation to move data fast and keep the AI tier isolated from the rest of the shop.
Models
Open-weight models selected and tuned to run well on the hardware you own, for the tasks that matter to you.
Operations
Monitoring, updates, backups, and a defined plan for the day a part fails. Managed for you, owned by you.
Recommended equipment
Where the horsepower comes from.
Starting points, not the final build. The exact spec is sized to your models and volume in the Blueprint™. These are the manufacturers we build on.
NVIDIA RTX professional GPUs
A single capable GPU under a desk runs sizeable open models for one team or one workflow. The practical entry point for on-site AI.
View at NVIDIA →NVIDIA L40S and H100 data-center GPUs
When several people or several workflows hit the model at once, data-center GPUs in a rack carry the load with room to grow.
View at NVIDIA →NVIDIA DGX systems
A pre-integrated AI appliance for heavier, always-on demand. More to start, less to assemble, built to run hard.
View at NVIDIA →Workstation and server builders
Where a custom rig fits best, we spec it through proven builders such as Puget Systems or Lambda, then wire it into your network.
See Puget Systems →Manufacturer links are for reference. The right configuration for your workload is specced, sourced, and integrated. The architecture itself is scoped per project.
What changes
What you get for keeping it in-house.
Data that never leaves
The records that cannot go to a cloud API stay inside your building. The model comes to the data, not the other way around.
A bill that stops climbing
Heavy, steady usage that would meter up on an API becomes a fixed asset on your books. The cost is the hardware, not the per-task charge.
Answers without the round trip
Inference happens on the local network. For a tool someone uses on the floor, the response is there before the wait would have lost them.
It keeps working offline
An outage, a remote site, or a shop with no reliable signal does not stop the work. The intelligence lives where the work happens.
More in AI Engineering
Go deeper into the work.
AI Engineering overview → · Wondering why a subscription is not enough? Why Engineered →
The operating system these build toward
Local AI is one module of a connected operating system for your business.
Read the full guideStart with discovery
Where is your business losing value?
Tell us what is quietly costing you hours or revenue. The Blueprint maps where value is leaking, sets a real target, quotes the build, and the same team engineers it.
$2,500, credited · One team, plan to launch
Before you ask
The questions worth answering.
Is owned hardware cheaper than a cloud API?
It depends on volume. Occasional use is cheaper in the cloud, and we will tell you so. High, steady usage is where owned hardware wins: the per-task bill becomes a one-time spend you control. The 60-second check above points you to the right answer, and the Blueprint puts real numbers on it.
Does the data really stay inside the building?
Yes. The model runs on hardware you own, on your own network, and can be air-gapped so nothing reaches the internet at all. What leaves the building is decided in writing before anything is built. For clinics, legal, and financial work, that is the whole point.
Who keeps it running after it is installed?
Upkeep is handled for you, if you want it. Monitoring, updates, backups, and a defined plan for the day a part fails are managed for you. The hardware and the system are yours; the upkeep is a managed service you can cancel.
Do I own the hardware, or is it a rental?
You own it. The servers, the GPUs, and the models on them are your asset, not a seat on someone else's platform. It is specced, sourced, wired in, and handed over.
What does it cost?
The hardware is a real number sized to your workload, and the build is scoped in the Blueprint™ at a fixed quote. The Blueprint is $2,500, credited in full toward the build if it proceeds. The Blueprint quotes a fixed number, with no creeping hourly rates.
Can we just give everyone a ChatGPT or Claude subscription?
For individual drafting, summarizing, and learning, yes, and you should. A subscription is a general-purpose tool a person opens and prompts. It has no standing connection to your systems, no memory of how your business runs, and it only acts when someone drives it. That is a productivity tool for a person, not an AI system for a business. When you need AI embedded in a workflow, governed, and reliable enough to depend on, you have outgrown the subscription.
When does API-based AI make more sense than consumer apps?
When the work needs to run inside your software, not at a keyboard. APIs let you embed models in the tools you already use, choose the model per task (Claude, GPT-class, Gemini, or open-source), set data-retention terms in writing, and pay per token at volume instead of stacking $20–100/month seats across a team. That is the path from "everyone has a login" to a system the business runs on. The Blueprint defines whether you are there yet.