Private AI Infrastructure
Run AI on your own hardware — data never leaves the building.
We help organisations build and run AI on-premise — from hardware selection and assembly to model deployment and day-to-day operations. Suited to medical records, financial and internal data that cannot go to the public cloud.
How we work
We start by mapping your data, use cases and confidentiality requirements, then plan the right hardware and model mix. We build small, validate against real data and scale up once it proves itself — handing over a complete environment with access control and monitoring, plus training and long-term operations.
What this covers
- Hardware planning and assembly (GPU servers, storage, network)
- On-premise LLM deployment and fine-tuning
- Private knowledge base with retrieval Q&A (RAG)
- Data isolation, access control and audit
- Operations, monitoring and redundancy
What private AI can do for you
Running AI on your own servers means one thing above all: your data never leaves your environment. These are the most common internal applications — usually starting with the single most painful workflow, proven small, then scaled.
- 內部知識庫問答
- 合約與文件摘要
- 客服輔助回覆
- 會議紀錄整理
- 單據辨識與歸檔
- 報告初稿產生
- 資料分類與標記
- 文件比對與審核
- What “data never leaves” meansThe model runs on GPU servers in your own facility or private cloud. Questions, documents and answers all stay on your network — nothing passes through an external cloud service, whether it’s medical records, financials or customer data.
- How the AI learns your businessYour documents, SOPs and records become a private knowledge base (RAG): the AI retrieves from your data before answering and cites its sources, instead of guessing from memory.
- Access control & auditWho can ask what — and see which data — is controlled by department and role, and every query is logged: evidence on hand for internal control and regulatory audits.
- How the costs workBuild-out is a one-off cost (hardware plus deployment) — after that there are no per-call API bills. The heavier your usage, the better it compares to cloud AI.
Want AI features inside your product or website, for your customers? That lives under AI App Development — we build those too.See AI App Development
Example project
AIHealthcare · On-prem AI
Hospital records AI system
In-house records Q&A AI — no data leaves the premises.
FAQ
Not necessarily — setups range from a single GPU workstation to a cluster, sized by concurrent users and model scale. Start small, prove value, then grow.
Your data never leaves your environment — essential for medical, financial and other no-cloud data. And with no per-call API bills, heavy usage costs less over time.
No. The system runs on your hardware and you own every account. Maintenance happens within agreed access scopes, with every action audit-logged.
Ready to get started?
Start a project