A private ChatGPT for business is a conversational assistant powered by large language models (LLMs) that knows your organisation's internal data and runs in a controlled environment, so your information never leaves your walls and never trains a third party's model. It solves a problem most companies already have: your team is using ChatGPT anyway, but with no control over the data and no access to the company's own knowledge. This is exactly the approach César García Cabeza, an AI consultant in Andorra, champions when he helps SMEs adopt generative AI without losing grip on their information.
What exactly is a private ChatGPT?
It's a system that brings together two things: the raw power of an LLM and the specific knowledge of your business. Unlike the public version, a private ChatGPT:
- Knows your documents: it answers questions about your manuals, contracts, policies, and internal procedures.
- Connects to your systems: Drive, SharePoint, ERP, CRM, and other sources.
- Respects your privacy: it's deployed securely, and your data is never used to train outside models.
- Controls access: by role, with a clear audit trail of who asked what.
In practice, it's the difference between a brilliant intern who knows nothing about your company and one who has read every page of your documentation and respects your confidentiality rules.
| Criterion | Public ChatGPT | Private ChatGPT (Enclave) |
|---|---|---|
| Knows your internal documents | No | Yes |
| Your data trains third-party models | Possible | Never |
| Role-based access control | No | Yes |
| Audit trail of queries | No | Yes |
| Connects to your systems (ERP, CRM, Drive) | No | Yes |
Why do you need one?
If your team already uses generative AI, the risk runs both ways. On one side, sensitive data slipping out of your control: client details, contracts, or strategy pasted into public tools. On the other, knowledge left on the table: the public LLM knows nothing about your business, so the answers stay generic.
A private ChatGPT tackles both problems at once. Your team gets an assistant that's genuinely useful, and the company takes back control of its information.
"The most common mistake isn't technical: it's building the most powerful assistant in the world on top of messy documentation. If the right answer isn't written down properly somewhere, no model is going to invent it. Before you deploy anything, tidy up your sources and decide who can see what."
— César García Cabeza, AI consultant in Andorra
Which use cases deliver the most value?
These are the uses where the return shows up fastest:
- Internal support: answering questions about processes, HR, or IT straight from the official documentation.
- Assisted customer service: draft replies grounded in your knowledge base.
- Information retrieval: finding a contract clause or a figure buried in a report in seconds.
- Writing with context: proposals, reports, and communications that already know your tone and your data.
If what you mainly need is to query documentation with cited answers, it's worth looking at internal documentation search with RAG too, a closely related piece. In fact, it's the internal knowledge search César García Cabeza builds when a client prioritises traceable answers grounded in their own files.
What are your options for a private ChatGPT?
Not every company needs the same thing. Here are the four most common routes, with their genuine pros and cons:
| Option | Pros | Cons |
|---|---|---|
| Public ChatGPT / AI | Free or very cheap, available instantly, no project needed | Doesn't know your data, exposes it to a third party, and offers no access control or audit trail |
| Generic "chat with your data" SaaS | Quick to set up, fixed per-user price, maintenance included | Limited fit with your processes, your data lives in their cloud, and you depend on the vendor's roadmap |
| In-house development | Full control and knowledge that stays at home | Needs scarce AI talent, high cost and long timelines, and maintenance falls on your team |
| Custom solution (Enclave) by César García Cabeza | Adapts to your sources and permissions, private deployment, hands-on expert support | Requires an upfront investment and a real project, not a subscription you switch on in five minutes |
The right choice comes down to how strict your privacy demands are, whether you have an in-house technical team, and how much value you expect to pull from your internal knowledge.
How is privacy guaranteed?
It all comes down to the deployment. A private ChatGPT runs in a controlled environment, with agreements that ensure your data is never reused to train third-party models, role-based access control, and activity logging. When requirements are especially strict, you can use open-source models deployed in a fully private setup.
This is precisely the problem Enclave solves, the private ChatGPT for business that César García Cabeza builds.
In short
A private ChatGPT gives you the power of an LLM working over your company's own knowledge, without giving up control of your data. It's one of the AI projects with the clearest return for an SME.
Want to see how it would fit in your company? Book a diagnosis and we'll assess it together.