Product · Private AI
Your AI, running on hardware you own.
We move your AI workloads off OpenAI, Anthropic, and other hosted APIs onto local LLMs running in your own rack. Your data stays in the building, the per-token bill goes away, and we keep the whole stack monitored and up to date.
Why move
Hosted AI is a great place to start. It's an expensive place to stay.
Your data stays yours
Prompts, documents, and customer records never leave your network. That makes privacy reviews, client contracts, and compliance audits much simpler conversations.
Predictable cost
Swap a usage bill that grows with every request for hardware you own and can plan around. Heavy, steady workloads are where the difference shows up fastest.
No surprises from a vendor
No rate limits, no model deprecations, no behavior changing overnight. You choose which model runs and when it gets upgraded.
What we do
The whole migration, from the first audit to the pager.
Assessment & sizing
We inventory every place your apps call a hosted model, measure the traffic, and size the GPU, memory, and storage you actually need. No oversized hardware.
NVIDIA hardware & stack
We spec and set up the GPU servers: drivers, CUDA, container runtime, and GPU scheduling, built as code so the setup is repeatable and documented.
Model selection & testing
We test open-weight models against your real prompts and pick the best fit for quality, speed, and memory. You see the side-by-side results before anything changes.
Drop-in API
Models are served behind an OpenAI-compatible endpoint, so most of your apps switch over with a configuration change instead of a rewrite.
Safe cutover
We run local models in the shadow of your current provider, compare the answers, then move traffic over gradually with a rollback ready.
Monitoring & upgrades
GPU health, utilization, latency, and throughput on live dashboards with alerting. We patch drivers, upgrade models, and plan capacity as you grow.
How it works
Four steps, and you can stop after any of them.
Assess
We map your current AI usage, data sensitivity, and costs, then give you a written plan with hardware specs and an honest read on what should move and what shouldn't.
Build
We stand up the NVIDIA servers, the serving stack, and the models in your environment, with monitoring in place from day one.
Migrate
Shadow traffic, side-by-side comparisons, and a gradual cutover, one application at a time.
Operate
We monitor, patch, and upgrade the stack and the models, and tell you when it's time to add capacity.
Questions
What people ask before moving
For a lot of business work, such as summarizing, extracting data from documents, classifying, answering questions over your own files, and internal assistants, current open-weight models do the job well. We measure that on your real prompts before cutover. Where a hosted model is still clearly better, we can keep a hybrid setup and route only those calls out.
You need GPU servers you control, either in your office, a colocation rack, or a dedicated host. We size them to your workload, help you spec and order them, and can also work with GPU servers you already own.
Usually not. We serve models behind an OpenAI-compatible API, so most applications only need a new endpoint and model name. Where code changes are needed, we make them.
We do. Monitoring, alerting, driver and security patches, model upgrades, and capacity planning are part of the engagement. If you'd rather run it in-house later, we hand over full documentation.
It depends on how much you send to hosted APIs and how sensitive the data is. The assessment answers that with your numbers. If staying hosted makes more sense, we'll tell you.
Next step
Find out what it takes to bring your AI in-house.
Book a migration assessment. You get a plan, a hardware spec, and a straight answer on whether it's worth doing.
Get in touch
Let's talk
Tell us what's going on. We reply within one business day.
or fill out the form below