What is a Private AI Assistant?

The practical difference between public and private AI is the data boundary. When an employee uses ChatGPT or Claude for work, every prompt they type passes through an external provider's systems. The model has no knowledge of your business — it generates responses from general training data, not from your specific documents, pricing, procedures, or client records. And regardless of the provider's data use policies, the information leaves your environment.

A private AI assistant changes both of those conditions. The model runs on infrastructure you control — typically a cloud environment within your security boundary or, for organisations with higher sensitivity requirements, on-premise hardware. The knowledge base the model answers from is built from your actual business documents: internal wikis, product specifications, policy manuals, customer service records, technical guides, and whatever else your team currently has to search through manually to find answers.

Most private AI implementations use RAG (Retrieval-Augmented Generation) rather than fine-tuning. RAG retrieves relevant documents from your knowledge base at query time and provides them to the model as context — the model then generates an answer grounded in that specific content. This approach is preferred because it works with your existing documents without requiring expensive retraining, and the knowledge base can be updated by adding or modifying documents rather than retraining the model every time something changes.

The use cases that benefit most from private AI are those where employees currently spend time searching for information: customer support agents looking up product specifications, sales staff checking pricing and availability, operations teams finding procedure documents, and new staff learning company-specific processes. A well-built private AI assistant reduces the time to find accurate answers from minutes to seconds, across the full depth of your business knowledge.