Sovereign AI, built for your business

We build the AI system your business actually needs — and run it for you.

Myxify is a Canadian AI engineering company. We find the bottleneck in your operation, design a private AI system around it, buy the NVIDIA hardware that workload requires, and operate the result — so your data and your models stay yours.

(a) — What we build

Practical AI, not pilots that go nowhere.

Each engagement ends with something running in your business, on compute we manage for you.

01

Private LLM deployments

Your own model endpoint, your own data boundary — hosted on hardware dedicated to your workload, in Canada.

02

Document & knowledge automation

Retrieval systems over contracts, tickets, drawings and records so staff stop hunting through folders.

03

Fine-tuning on your data

We adapt open models to your terminology, forms and workflows instead of paying per token for a generic one.

04

Inference that fits the job

Right-sized GPU serving for vision, speech or text workloads — sized to your traffic, not to a price list.

05

Dedicated hosting per client

Single-tenant environments we build and maintain, with clear ownership of data, models and access.

06

Bottleneck discovery

A short engagement to find where AI actually pays back in your operation — and where it does not.

(b) — In production

A private AI workspace, running on the client's own box.

This is one of the systems we build: a self-hosted assistant with its own knowledge base, deployed on a desk-side NVIDIA DGX Spark for a small business. Staff chat with company documents, spreadsheets and reports, and work gets tracked — with nothing leaving the building.

  • Company and personal knowledge bases, kept separate and permissioned.
  • Ask questions against PDFs, spreadsheets and reports the team already uses.
  • Notebooks per project, so context is not lost between conversations.
  • Built-in task board, so an answer turns into work that gets done.
  • Runs on the client's own hardware — no third-party API, no data egress.

Deployed on: NVIDIA DGX Spark · single-tenant · fully offline capable

Walkthrough — private assistant, document Q&A and task tracking on a single on-premise device.
(c) — How we work

From bottleneck to running system.

01
Understand the bottleneck

We start in your operation, not in a datasheet. What is slow, manual, repetitive or risky?

02
Design the system

Model choice, data boundary, integration points and the compute the workload genuinely needs.

03
Procure and build

We buy the NVIDIA hardware the design calls for and stand up the environment for that client.

04
Run and improve

We operate it, monitor it and keep tuning as the workload and the business change.

(d) — Hardware we deploy

We buy what the workload needs.

Hardware is part of the system we deliver, never a product line of its own. We size it during design, purchase it through our NVIDIA partners for that specific deployment, and keep operating it on the client's behalf.

On-premise appliance
DGX Spark
Desk-side sovereign AI for small business deployments — one unit per client site.
Training / fine-tuning
H200 · H100
For adaptation runs on client data where time-to-result matters.
Inference
L40S · L4 · RTX PRO
Steady-state serving sized to real request volume.
Mid-range work
A100 80G / 40G
Cost-effective for embeddings, retrieval and smaller models.
Supporting build
DDR5 ECC · Gen5 NVMe
Memory and storage matched to each deployment we assemble.
Sovereign by default

Client data and models stay inside a Canadian boundary the client can point to.

We buy for deployments, not for stock

Every unit we purchase is destined for a system we design, build and operate — never for resale.

Small team, direct work

You talk to the people building it. No layers, no handoffs, no account management theatre.

(d) — Get started

Tell us where the work piles up.

A short conversation is usually enough to tell whether AI helps your case, what it would run on, and what it would cost to build. If it is not worth doing, we will say so.