How to Build a Private ChatGPT for Your Company Documents

What does a private ChatGPT for your company actually mean?

When people ask for a private ChatGPT they usually want three things: the convenience of asking questions in plain language, answers drawn from the company's own documents and assurance that confidential information stays under control. In practice this is a retrieval-based assistant. Your files are indexed, relevant passages are found at question time and a language model composes the reply.

Private does not have to mean running your own model on your own servers, although that is possible. It means you decide where data is stored, who can ask what, which model provider processes the prompts under which terms and what is logged. The right level of privacy depends on the sensitivity of your documents and your regulatory obligations.

Which deployment option fits your company?

There are broadly three paths. The first is a ready business-tier assistant from a major AI vendor, with contractual data protections and little engineering. The second is a custom application on your cloud account, such as AWS or Azure, using a model API under business terms and your own document store. The third is a self-hosted open model on your own infrastructure.

Most mid-sized companies are well served by the second option. It balances control and effort, supports single sign-on and gives you room to integrate with SharePoint, Google Drive or your ERP. The self-hosted route gives maximum control but needs strong technical capacity and hardware, and open models may trail commercial ones on quality for some tasks.

• Vendor business assistant: fastest, least control over customisation
• Custom app on your cloud with a model API: balanced control and effort
• Self-hosted open model: maximum data control, highest technical load
• Hybrid: sensitive documents stay local, general tasks use a cloud model

How do you prepare your documents?

Start by deciding which repositories are in scope: policy manuals, product documentation, SOPs, proposals, past tickets. Exclude anything unnecessary or highly sensitive on the first pass, such as salary files and personal records. Remove outdated versions, because a retrieval system cannot tell which of two conflicting policies is current unless you mark it.

Check file quality. Scanned documents need text recognition, tables may need conversion and spreadsheets may need explanatory headers. Keep metadata such as department, version date and owner, because it helps with filtering and permissions. This preparation is unglamorous, but it has more effect on answer quality than the choice of model.

How do you handle security and access control?

The central rule is that the assistant must never reveal a document to someone who could not open it directly. Implement this by attaching permissions to each indexed document and filtering retrieval by the logged-in user's groups. Connect login to your existing identity system so leavers lose access automatically.

Review the data path end to end. Where are documents stored, how are they encrypted, what does the model provider retain and for how long, and who can see logs? Keep an audit trail of questions and sources used. For regulated information or personal data, consult your legal or compliance advisers and check current Indian data protection requirements.

• Single sign-on and role-based access to each document set
• Retrieval filtered by the user's existing permissions
• Encryption in transit and at rest
• Written terms on whether prompts are retained or used for training
• Audit logs of questions, sources and administrative changes

How do you make answers accurate and trustworthy?

Instruct the assistant to answer only from retrieved passages, to cite the document and section, and to say plainly when the answer is not in the documents. Show the citations in the interface so users can click through and verify. This one habit builds trust and exposes wrong answers quickly.

Create a test set of real questions from different teams with known correct answers, and run it whenever you change documents, prompts or models. Collect thumbs up and down from users and review the poor ratings. Where answers drive decisions with legal or financial weight, require a person to confirm before acting.

How do you roll it out in your company?

Pilot with one team that has a clear pain, such as support engineers searching manuals or HR answering policy questions. Give them a short briefing on what the assistant can and cannot do, how to phrase questions and how to report mistakes. Measure the time they spend searching before and after.

Expand in steps, adding document sets and departments as quality is proven. Assign an owner for content freshness and another for monitoring. A technology partner such as A Plus Solution can design and build the system, but whoever builds it, insist on clear documentation of the data flow and an agreed process for updates.

Frequently asked questions

Is a private ChatGPT safer than the public one?

It can be, because you control storage, access and the contractual terms with model providers. Safety still depends on how it is configured, so verify the data path rather than relying on the word private.

Do we need our own GPU servers?

Not necessarily. Many private assistants call a hosted model under business terms. GPUs are needed only if you choose to run an open model yourself.

Can it connect to Google Drive, SharePoint or our ERP?

Usually yes, through connectors or custom integrations. The key is that permissions in the source system carry over to the assistant so access remains consistent.

How long does a first version take?

A pilot over a limited document set can often be built in a few weeks. Time grows with the number of sources, security requirements, languages and the amount of document cleanup needed.

Need help with this? See our Generative AI & LLM Apps service or talk to Yash Parikh.