How to Build a Private ChatGPT for Your Company

Build a private ChatGPT by pairing an open model with your own data behind your own walls

A "private ChatGPT" for your company is a chat assistant that runs on infrastructure you control, answers using your internal knowledge, and never sends a word to an external provider. You build it by combining three things: an open model such as Llama, Mistral, or Qwen for the language ability, a retrieval layer that feeds the model your own documents, and a chat interface with authentication so only your people can use it. None of these parts are exotic, and assembled correctly they give you the usefulness of a hosted assistant without the data leaving your boundary.

The three building blocks

It helps to see the system as three cooperating layers, each with a clear job.

• The model layer. A self-hosted open model provides the reasoning and language fluency. Run it with vLLM for many concurrent users or Ollama for a smaller team, on a GPU you own or rent.
• The knowledge layer. Retrieval-augmented generation connects the model to your wiki, policies, tickets, and PDFs, so answers are grounded in your reality rather than the model's generic training.
• The access layer. A chat front end with single sign-on, permissions, and logging ensures the right people get answers and you have a record of what was asked.

Grounding is what makes it yours

A bare model knows a lot about the world and nothing about your company. The retrieval layer is what closes that gap: when a user asks a question, the system finds the most relevant passages from your own documents and places them into the prompt, so the model answers from your material and can cite where each fact came from. This grounding is the difference between a generic chatbot and an assistant that actually knows your policies, products, and history — and the citations are what make sceptical colleagues willing to trust it.

Privacy is the entire point, so design for it from the first line. Keep the model, the documents, and the logs inside your network, gate access behind your identity provider, and make sure no component quietly phones home.

Respecting who is allowed to see what

An internal assistant must not become a way around your existing permissions. If a document is restricted to the finance team, the assistant should not surface its contents to someone in sales. The honest way to handle this is to carry each user's access rights into the retrieval step, so the system only ever considers documents that person is already allowed to read. Bolting permissions on after the fact is fragile; building them into retrieval from the start is what keeps a helpful tool from becoming a data-leak vector.

Start with one team, then grow

The fastest way to fail is to launch a company-wide assistant over every document at once. The reliable path is to pick one team with a clear need and a contained set of documents — support, HR, or engineering are common starting points — build the assistant for them, and earn their trust. Once it demonstrably helps, you widen the document set and the audience deliberately, carrying the lessons forward. A narrow assistant that one team relies on every day is worth far more than a broad one nobody quite trusts.

Measuring whether it actually helps

An internal assistant is worth keeping only if it saves real time, and you should treat that as a measurable claim rather than a hopeful assumption. Decide up front what success looks like — faster answers to common questions, fewer escalations to a senior colleague, less time hunting through the wiki — and check against it after a few weeks of real use. Logging every question and answer gives you both the evidence and a steady supply of examples for spotting where retrieval falls short. When the assistant cannot find something it should have, that gap is usually a missing or poorly structured document, not a flaw in the model, and fixing the source is what steadily makes the whole system better. This habit of measuring and feeding back is the difference between a tool that improves on purpose and one that quietly drifts into disuse. Equally important is asking the people who use it what is and is not working, because the numbers tell you whether the assistant is used while their feedback tells you precisely why, and the two together point at exactly what to improve next.

Prefer it built and managed for you?

A private company assistant touches your models, your documents, your identity provider, and your security posture all at once — which is exactly why it rewards experienced hands. BSH Technologies builds private, grounded, permission-aware AI assistants that stay entirely within your walls, from the retrieval pipeline to the access controls. If your company needs its own ChatGPT, talk to BSH Technologies or see our AI & automation services.