The problem this solves
Many organizations want AI but cannot send contracts, patient records, source code, or customer data to an outside service. Others can, but their security team, regulator, or client agreements say otherwise. Staff then either go without or paste sensitive text into public tools, which is the outcome nobody wanted.
Running models privately is possible, but it is more than installing one. Someone has to size the GPUs, pick a model that is good enough for the task, serve it reliably, control who can use it, and watch what it costs. A proof of concept on one machine often has none of that, so it stalls before real users reach it.
How we build it
We start with the task and the data, not the hardware. We list the workloads you want AI to handle, how sensitive each one is, how many people will use it, and what response time they need. Then we test open-weight models against your real examples to find the smallest one that is good enough, because a smaller model is cheaper to run and easier to host.
From there we design the deployment: serving software, GPU or CPU sizing, network boundaries, single sign-on and role-based access, logging, and updates. We build it on your servers or in your private cloud account, document how to run it, and hand it over to the team that will operate it.
Where it fits
Internal assistants that answer from confidential documents without leaving your network.
Contract, policy, and report analysis for legal, finance, and compliance teams.
Code assistance for engineering teams whose source code cannot leave the company.
Summaries and drafting for regulated records such as clinical or financial notes.
How the work is delivered
We confirm the business case, test the riskiest technical assumptions, and build the smallest version that is useful in production. Then we prepare your team to own and run it.
What you get
A written assessment of which workloads need a private model and which can safely use a hosted one.
A model comparison on your own examples, so the choice rests on measured quality, speed, and cost.
A working deployment with single sign-on, role-based access, usage logs, and a defined update process.
A cost model that shows hardware, power, and staff time against hosted pricing at your expected usage.
A runbook and handover session, so your team can operate, monitor, and upgrade the system.
When a private deployment is the wrong answer
Private is not always better. Hosted models are often more capable than open-weight ones, and they need no hardware to buy, power, or maintain. If your data is not especially sensitive, or your provider can meet your security and contract requirements, a hosted service with the right settings may be cheaper and give better answers.
Running models yourself also takes people. Someone must patch the serving software, watch GPU health, and test each new model before switching to it. If there is nobody to do that, a private deployment becomes a liability within a year.
We say so when discovery points that way, and we compare the options on cost, quality, and risk before you commit to hardware. When the model needs to answer from your own documents, our LLM fine-tuning and RAG service is the usual companion to a private deployment.
Frequently asked questions
What does private AI deployment mean?
It means the AI models run on infrastructure you control, such as your own servers or a private cloud account, instead of being called through a public AI service. Your prompts and documents are then processed inside your own environment.
Will a private model be as good as a hosted one?
Sometimes, depending on the task. Open-weight models handle many business tasks well, such as summarizing and answering from documents. We test them on your own examples first, so you see the quality before you commit.
Do we need to buy GPUs?
Not always. Smaller models can run on modest hardware, and some workloads suit a dedicated cloud account. We size the options and compare costs before you buy anything.
Can this work with no internet connection?
Yes, if the design calls for it. The models, serving software, and tools can be installed inside a closed network. Updates then need a controlled process for bringing new versions in.