I deploy language models and AI agents on your company servers or private cloud. Documents, conversations and customer data are processed locally and never reach external AI providers.
Private AI, also called on-premise AI, is a language model running on servers owned by the company or in its private cloud rather than in an external provider’s service. Documents and data processed by the model never leave the company’s infrastructure, and the company decides on the model version, access and updates.
We define what AI will be used for, what data it will process and your security requirements.
I choose an open model for the job and size the server or GPU.
I run the model on your infrastructure with no calls to external APIs.
I build a local knowledge base from your documents, with control over who can see what.
Monitoring, model updates and new use cases over time.
Accountants ask the assistant about the firm’s procedures and past explanations given to clients, and the model suggests document descriptions and drafts replies to client questions. Everything runs on the firm’s own server, so client data never leaves it.
| Aspect | Cloud AI | Private AI |
|---|---|---|
| Where data goes | An external provider’s servers | Only your company’s servers |
| Cost | A fee for every request | Hardware and upkeep, no per-request fees |
| Model control | The provider decides on changes | You decide on versions and updates |
| Best for | Non-confidential data, quick start | Confidential data, regulated industries |
Client data, contracts and know-how stay in the company.
Easier to meet GDPR, professional secrecy and internal security policies.
No fees per model request.
You decide on the model, updates and access.
It depends on the model and number of users. Smaller models run on a single server with a GPU; larger ones need more powerful GPUs. During the assessment I size the model and hardware to your tasks, so you do not pay for capacity you will not use.
For most business tasks, such as answering questions about documents, summarising or drafting, open models are more than enough. The latest cloud models can be better at very complex tasks, so I sometimes combine both approaches: confidential data stays local and other tasks go to a cloud model.
Personal data is processed only on the company’s infrastructure, so there is no need to share it with an external AI provider or track where it is stored. Access control and logs show who used the system and when, which makes compliance easier to demonstrate in an audit.
Book a free 30-minute consultation. We'll walk through your process and I'll tell you where to start.
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