A private ChatGPT for business is an AI assistant that answers employee questions based on company documents. What people type into it does not end up in public tools or in training someone else’s models. Your employees are most likely using AI already, often on private accounts. So the question is not “allow it or not?” but “how do we get it under control?”. Below you will find three deployment options, questions to ask a vendor and the first steps.
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Why is pasting company data into public ChatGPT a risk?
Text pasted into a free account leaves the company. It can be stored or used in ways you have no control over. What do people paste there? Usually contracts, customer data, sales offers and source code, because they want to summarize or fix something quickly. Nobody keeps a record of it. The company does not even know what has leaked. And the law makes no exceptions here: GDPR also covers personal data processed by models, which the European Data Protection Board discussed in its opinion on data protection in AI models. Where to start? In my view, with written rules for using AI, before you even choose a tool.
Private ChatGPT for business: three deployment options
There are three options: a business account with a contract, your own application built on the API, or a model running locally. Each one balances control, cost and workload differently:
- Business account with a contract - the fastest start, and the data processing terms are spelled out in the contract. On the other hand, you have little say over integrations with company systems.
- API-based application - your own interface, permissions for each department and connections to your CRM, intranet or document base. A good choice when the assistant needs to work on live data.
- Local model - data never leaves your infrastructure. But hardware requirements go up and you need a team to maintain the whole setup.
What decides the choice? How sensitive the data is, industry requirements, the infrastructure budget and the number of systems the assistant has to talk to.
How does an AI assistant work on company data and where do its answers come from?
The assistant does not learn from your documents. For each question it searches for matching passages and only then builds an answer from them. This method is called RAG (Retrieval-Augmented Generation). What does it mean in practice? Knowledge is up to date as soon as a file is added, the model makes things up less often, and the user can click the source and check where the answer came from. Permissions work like in a file system: an employee only gets answers from documents they already have access to. We write more about when a chatbot needs RAG. Our Ragable (SaaS or on-premise) works on this principle and cites its sources with every answer.
What questions should you ask a vendor before deployment?
A good vendor will tell you straight where the data is processed, who has access to it and how to check the source of an answer. Before you sign a contract, ask:
- Where does the model physically run and where is the document base stored?
- Is our data used to train any model?
- How do permissions, logins and the query log work?
- Does every answer point to the document it comes from?
- How do we update documents and how quickly does the assistant see the changes?
- Who maintains the system after deployment?
- Will we sign a data processing agreement?
When should alarm bells ring? When you ask where the data is stored and get vague answers. Or when someone promises results instead of proposing a pilot.
Where to start with deploying an AI assistant in your company?
With one process. One with a clear set of documents and a measurable problem, not the whole company at once. An order that works: first choose the area (customer service, internal documentation or procedures), then clean up the files, because outdated versions give outdated answers. Next, decide on the option and run a pilot with a small group of users. Evaluate the answers on real questions, not ones made up for a demo. Only then move on to other departments. In the meantime, agree with your people on what they may type into AI tools and which tools they may use at all.
Thinking about a private ChatGPT for your business? Talk to Web Systems. We are a software house from Łódź, in business since 2006, building AI applications and custom software. Describe your process in the form on our offer page, where we show what an AI assistant built for business looks like, and we will set up a free consultation.
Frequently asked questions
Is a ChatGPT business account enough to protect our data?
It depends on the contract terms and how sensitive the data you want to process is. A business account sorts out the legal side, but on its own it will not connect the assistant to your systems or assign permissions by department. For specially protected data, your own application or a local model is a better fit.
Can an AI assistant work offline, on a company server?
Yes. That is the local model option, meaning an on-premise deployment. Documents and queries then stay with you. You just need the right hardware and people who will take care of updates and security.
How long does it take to deploy ChatGPT on company data?
It mainly depends on the state of your documents and the number of systems to integrate. An organized knowledge base and one area? You can get started quickly. Complex integrations make the work take longer. That is why it is best to start with a pilot that shows the real scope of the project.
