AI implementation on your own server
I run a language model on the company's own infrastructure: for searching documents, drafting replies and sorting requests. The data doesn't have to leave the company. I'll also tell you plainly where AI won't work.
You are here: AI implementation.
When does AI on your own server make sense?
- You have internal documents where it's hard to find anything quickly.
- You answer similar questions over and over.
- Requests need sorting before anyone deals with them.
- You don't want to send company data to outside services.
What you get
- A clear agreement on what the AI is for and what data it may process.
- The choice of model and hardware, or a service, depending on the requirements.
- A deployment with access control.
- A test on your own examples before launch.
- Rules of use: what the AI does and what stays a human decision.
What it looks like
A suggested process. We'll fit the details to the project. How I work
- 01Goal and data
What the AI is for, what data it may see and what it may not.
- 02Choosing the model
On the company server, in the cloud or a hybrid.
- 03Deployment and testing
On real examples, with access control.
- 04Launch
Rules of use, an explanation for the team and watching the results.
How much does AI on the company's server cost?
The price depends on the tasks, the number of documents, the number of users and whether the model runs on hardware in the company or in the cloud. An on-site deployment adds the cost of hardware, chosen to match the tasks.
You can pay once or by subscription. How payment works
Questions and answers
Don't see your question? Write to me.
Is AI on your own server safer than in the cloud?
Not automatically. It gives more control over where the data is, but security depends on configuration, updates and access control. Those are needed in every model.
What hardware is needed?
It depends on the size of the model and the number of users. I choose the hardware once we know what the AI is for, not the other way round.
Is a local model as good as the largest cloud models?
For many company tasks, for example searching your own documents or drafting replies, a smaller model is enough. For more complex tasks the largest cloud models are usually better. That's why a hybrid setup is sometimes best.
Will the AI make decisions for us?
It shouldn't. I design it so that the AI prepares drafts and organises data, and a person makes the decision.
Where should an AI implementation start?
With one specific task and the question of which data must stay in the company. More in the guide to AI on your own server versus the cloud.
Industries
How this service looks in specific industries:
Want to find out whether AI can make something easier?
Describe the task and the data involved. I'll tell you plainly whether AI will help and where it can run.See also
IT infrastructure and server administration
Servers, backups, access control and monitoring.
More about the serviceAI in the company