Private & local AI
Own Your AI: Your Compute, Your Models, Your Data
Most companies rent AI by the seat and send their data to someone else’s servers. You can own it instead: an open model on your own computer, an AI workstation for your team, or an in-house AI server on your company network that answers from your files. When the cloud fits better, we set that up properly too.
Six ways to run AI, from simplest to fully yours
Start anywhere on the ladder and move up when it pays off. Each step links to the details and starting price.
Cloud AI, set up right
ChatGPT, Claude, Copilot or Gemini with company sign-in, data controls and real training.
Private AI on one computer
An open model and private document search on a single PC or Mac. Nothing leaves it.
An AI workstation for your team
A GPU workstation or large-memory Mac that serves a private assistant to a whole team.
Your company’s in-house AI server
A dedicated server on your network: your own ChatGPT, answering from your files.
Open models on your infrastructure
Production model serving with an API, company sign-in and logs, for your software too.
A model trained on your work
Fine-tuning that teaches an open model your style, terms and tasks, measured before and after.
Also: private document search, hybrid local + cloud AI, offline (air-gapped) AI and monthly care.
Renting AI vs. owning it
| What matters | Cloud AI by the seat | Private AI you own |
|---|---|---|
| Where your data goes | The provider’s servers, under their business terms | Stays on your hardware, in your building |
| How you pay | Per person, per month, for as long as you use it | Hardware and setup once, then optional care |
| Quality on the hardest tasks | The best available | Very good, and better every few months |
| Works without internet | No | Yes |
| Choosing or switching models | The provider’s models | Any open model, any time |
| Trained on your own work | Limited | Yes, with fine-tuning |
Many companies run both: sensitive work on their own model, approved tasks on a cloud model, with the rules enforced by the system.
Hardware, roughly
| Who uses it | Typical hardware | Good for |
|---|---|---|
| One person | A recent PC with a strong graphics card, or a Mac with 32–64 GB of memory | Drafting, summaries and private document search |
| A team of 5–20 | A GPU workstation with 32–96 GB of GPU memory, a large-memory Mac Studio or an NVIDIA DGX Spark | A shared private assistant and document search |
| A department or company | A multi-GPU server in your rack or network closet | Many people at once, larger models and fine-tuning |
Exact sizing depends on the models you need and how many people use them at once. We size it after a free call and quote it upfront. Readiness and hardware sizing.
Built on today’s standards
- Open models from the Llama, Qwen, Gemma, Mistral, gpt-oss and DeepSeek families, chosen by testing on your work
- Proven serving software: vLLM, llama.cpp and Ollama
- An OpenAI-compatible API, so existing apps and automations can connect
- MCP connectors so the AI can use your tools, with permissions
- Company sign-in (SSO), roles and audit logs
- Private document search (RAG) with citations and folder permissions
- Encryption, backups and a documented update process
Every system follows our AI safety rules: people stay in charge of anything that matters.
How it works
- 1
Free fit call
What should the AI do, which data can it see and who uses it? You get an honest answer on local, cloud or hybrid.
- 2
Sizing and pilot
We size the models and hardware, then pilot on your real work before you buy more.
- 3
Build and lock down
Built, tested under load, behind company sign-in with permissions and logs, with staff trained.
- 4
Own it
Documented and in your name, with optional monthly care to keep models and security current.
Questions
What does it mean to own your AI?
The model runs on hardware you own, your documents and conversations stay on it, and the setup, any fine-tuned models and the know-how are documented and yours. You aren’t locked into one provider’s prices or policies.
Does owning cost less than buying ChatGPT or Claude seats?
It depends on your numbers. A private setup is mostly a one-time cost for hardware and setup, then optional monthly care. For a team that would pay for many seats for years, owning is often cheaper over three years; for a few people, a cloud plan usually is. Our readiness assessment compares both for you.
Which open models do you use?
We test current open models from families like Llama, Qwen, Gemma, Mistral, gpt-oss and DeepSeek on your real tasks, check each license for your use, and pick the best fit for your hardware. You can switch models later as better ones arrive.
Is my data really private?
With a local setup, prompts and documents are processed on your own hardware and don’t need to leave your network at all. We add company sign-in, permissions, logging and encryption, and we can build fully offline systems for the most sensitive work.
Can we start small?
Yes. Many companies start with a cloud plan set up properly or a private assistant on one computer, then move to a workstation or server once they see the value. Nothing is wasted along the way.
Plan your private AI
Tell us what you want AI to do and what data it should never send out. We will tell you what it takes to own it, and what it costs compared with renting.
- Big projects and small ones are all welcome
- Fixed-price quotes, no surprise invoices
- You own your code, data and accounts
- A client portal to track progress, invoices and payments
- Rooted in Wisconsin, serving businesses nationwide
Rather talk it through? Call us