Own Your AI Stack. Keep Your Alpha.
We build private AI systems that run entirely on hardware you own. No token fees, no usage meters, no data leaving your building. You buy the machines once, and after that the only thing you pay for is electricity.
Why Local, Why Now
Every Prompt You Send to the Cloud Is a Piece of Your Playbook
When your team runs its best thinking through a third-party AI API, your strategies, your data, and your ways of working travel with it. Some companies can live with that. But if your edge is built on proprietary knowledge like deal flow, research, formulas, or client relationships, handing it to an outside vendor is a risk you do not have to take.
Open models have caught up. Drafting, analysis, coding, and document work now run well on hardware you can put on a desk. That means no usage meters, no data leaving your network, and no third party sitting between you and your own intelligence.
Data Sovereignty
Your documents, prompts, and outputs stay on machines you own, inside a network you control.
Protect Your Alpha
The proprietary knowledge that makes your company valuable never becomes someone else's training signal or telemetry.
Predictable Cost
No per-token billing and no usage anxiety. Once the hardware is on the desk, the only operating cost is energy.
Always Available
No rate limits, no vendor outages, no surprise policy changes. Your stack works whenever you do, even offline.
The Hardware
Two Paths to the Same Promise
Your models, your data, your walls. We build on two hardware foundations and help you pick the one that fits your workloads, your space, and your budget.
Apple Silicon
Mac mini & Mac Studio
The Mac mini and Mac Studio are quietly excellent AI machines. Apple's unified memory lets large open models load straight into memory shared between the CPU and GPU, so they run fast, cool, and quiet on a normal desk. If you want serious private AI without building a server room, start here.
- Up to 512GB of unified memory on Mac Studio, enough to run open models with hundreds of billions of parameters entirely in memory
- Quiet, desk-side machines with no server room and no special cooling
- Mac-native tooling: Apple's MLX framework and llama.cpp-based inference servers
- Private chat, document retrieval, and coding assistants for your team
NVIDIA GPU Systems
Workstations & Servers with NVIDIA NeMo
Some organizations need more than inference. If you want to curate sensitive data, fine-tune models on proprietary knowledge, enforce guardrails, and evaluate agents before they ship, we deploy the NVIDIA NeMo suite on CUDA-accelerated workstations and servers that you own and operate.
- CUDA-accelerated GPU workstations and servers deployed on-premises
- NVIDIA NeMo software suite: Curator, Customizer, Evaluator, Retriever, and Guardrails
- Data curation and anonymization pipelines for sensitive datasets
- Domain fine-tuning and agent lifecycle management on your own metal
What We Deliver
You Get a Working System, Not a Pile of Hardware
Buying the machines is the easy part. What you need is a stack your team can use on day one and still maintain on day one thousand. That is what we hand over.
Hardware Sizing & Procurement
We look at your workloads, your team, and your growth plans, then spec exactly the hardware the job needs. That might be one Mac mini on a desk or a rack of Mac Studios. You will not overbuy, and you will not outgrow it in six months.
Local Model Selection & Deployment
We pick open-weight models that fit the work your team actually does, whether that is writing, analysis, coding, or reading documents. Then we stand up the inference servers that keep them running reliably on your hardware.
Private Tooling Integration
Chat for your team. Search across your internal documents. Coding assistants wired into your repositories. Every one of them runs against your local models, inside your network, and nowhere else.
Security & Network Isolation
We lock the stack down so inference never leaves your walls. Network segmentation, access controls, and when the work demands it, fully air-gapped deployments that never touch the public internet.
Governance & Guardrails
Running AI privately does not mean running it without rules. We set up usage policies, output guardrails, and audit trails so your stack holds up to the same scrutiny as any other enterprise system.
Team Training & Handoff
We do not build a black box and disappear. Your team learns to run the stack themselves: swapping in new models, adding tools, and extending workflows. The capability stays in-house, just like your data.
The Economics
Zero Token Cost. You Only Pay for Energy.
Cloud AI bills you for every token, every request, every user, every month. The meter never stops. A local stack flips that: you buy the hardware once, and from then on the only cost of running your models is the electricity that powers the machines.
That changes how your team works. Nobody rations prompts. Nobody opens a surprise invoice because a workflow ran overnight. When usage costs nothing at the margin, your people experiment more, automate more, and squeeze more value out of the same machines every day.
Cloud API vs. Your Local Stack
- Pay per token, foreverPay for hardware once, then energy
- Your prompts leave your networkEverything stays inside your walls
- Rate limits and vendor outagesAlways on, even fully offline
- Terms and pricing can change under youYou control the stack end to end
Ready to Bring Your AI In-House?
Tell us about your team, your workloads, and what you need to keep private. We will map out a local AI stack that fits, whether that is a single Mac mini or a full on-premises deployment, and show you exactly what it takes to own it.