When Does an Organization Need an On-Premise Private LLM?
An AI assistant that answers from internal documents without sending data outside. When you need it, what to prepare, and how to get started.
AI assistants based on large language models (LLMs) can answer questions in everyday language. The problem is that many organizations are not allowed to upload internal documents to public AI services. That is where an on-premise private LLM comes in.
What is an on-premise private LLM?
The AI model and your documents run on servers owned by your organization — in the office, a data center, or a private cloud. Questions and answers never leave your network.
These systems usually use a retrieval approach: internal documents are indexed first, then the AI answers based on the relevant document passages and includes references.
Signs your organization needs one
- The answers are in SOPs, regulations, or contracts, but they take a long time to find.
- The same team keeps answering the same questions.
- Documents are sensitive and must not be processed on public services.
- Document access differs by division.
What to prepare
- A document set you want to be able to query, and who owns it.
- Access rights: who may see which documents.
- Servers: requirements are sized from the number of users and documents.
- Answer testing with a team that knows the documents well.
Important controls
Make sure every question and answer source is logged (audit log), and that users only get answers from documents they are allowed to see.
Start small
Choose one division and one document set, for example operational SOPs. Measure whether the answers help, then expand to other divisions. sentrAI is designed to run 100% on-premise.
Tell us about the process. We'll help map out the most proportionate approach.
