ChatGPT is convenient — but your data goes to OpenAI's servers. For many businesses, that is a deal-breaker. Here is how to build a private equivalent that runs on your own infrastructure, answers from your own documents, and keeps everything inside your network.
When people say they want a private ChatGPT, they usually mean three things: a conversational interface like ChatGPT, answers grounded in their own company's data rather than the internet, and full control over where the data lives and who can access it.
This is entirely achievable. The underlying technology — large language models — is available from multiple providers, some of which can run entirely on your own hardware. Combine that with a RAG pipeline that retrieves answers from your documents, and you have a private ChatGPT that knows your business.
The goal is not to replicate ChatGPT. It is to build something better for your specific context: an AI that knows your products, your processes, your customers — and never shares any of it with a third party.
A private enterprise AI assistant has four components:
The good news is you do not need to build any of this from scratch. Platforms like Open Enterprise assemble all four components for you.
One of the most important decisions is where your language model runs. There are two options:
Cloud LLM (OpenAI, Anthropic, Azure OpenAI) — the model runs on the provider's infrastructure. Your prompts and retrieved context are sent to their servers. This gives you the highest-quality models with no hardware requirement, but involves a data transfer. For many organisations, this is acceptable — especially under a GDPR-compliant enterprise API agreement with data processing protections.
Local LLM (Ollama, LM Studio) — the model runs on your own hardware. Nothing leaves your network at all. Quality is lower than frontier models like GPT-4 or Claude, but for many internal use cases the gap is smaller than people expect, and the privacy guarantee is absolute.
Open Enterprise supports both options and lets you switch between providers without rebuilding anything.
Deploying a private ChatGPT with Open Enterprise takes under 15 minutes:
Step 1 — Pull and run the Docker image:
Step 2 — Log in and configure your LLM. Go to Settings → Instance Settings, add your OpenAI API key (or configure Ollama for a fully local setup), and set your embedding provider.
Step 3 — Create a workspace and upload documents. A workspace is an isolated environment with its own knowledge base. Upload PDFs, Word docs, Markdown files — anything your team references regularly.
Step 4 — Invite your team. Create user accounts, assign roles, and your team can start asking questions immediately.
The initial deployment gives you a working assistant. Making it genuinely useful is an ongoing process:
Because everything runs inside your own infrastructure, you control the security perimeter. The database is local. The vector embeddings are local. The only external traffic is the queries you choose to send to a cloud LLM — and that can be eliminated entirely by using Ollama.
Access control is built in: workspaces are isolated, user roles restrict what each person can see, and admin logs show exactly what was accessed and when. This is the standard that private AI is built to.
Open Enterprise deploys in one Docker command. Your data stays on your infrastructure. No per-seat pricing, no data leaving your servers.
Get Started Free →