AI chatbot for business on your documents: sourced answers in seconds.

There are two uses. An internal chatbot serves your team: technicians, sales reps, order desk, quality. It replaces digging through shared folders and asking the same colleague the same question ten times. An external chatbot answers your customers, on your website, by email, or in a support tool.

Both rely on the same knowledge base, but not the same settings. Internally, an answer can go out on its own as soon as the source is clear. With customers, you almost always start with human review, then ease off where it has proven itself.

This page is mostly about the chatbot for your internal documents, your team’s AI assistant. Answering your website visitors is a separate service: the AI chatbot for your website. And if the answer needs to trigger an action (data entry, a follow-up, an update), that’s an AI agent.

An AI model like Claude or ChatGPT doesn’t know your spec sheets. To get accurate answers, you use a method called RAG (retrieval-augmented generation): the AI looks up the answer in your documents before it replies. It works in three steps.

• Prepare: your documents are split into passages and indexed, like a library’s detailed catalog.
• Search: for each question, the chatbot finds the few passages that answer it, among the documents the person is allowed to see.
• Write: the AI model writes the answer from those passages alone, and shows the source document and page.

The model learns nothing from your documents: it reads them when it answers. When a spec sheet changes, you just update it in the index. No retraining needed.

An AI model can make mistakes, and it makes them confidently. The risk doesn’t disappear, but four design rules reduce it sharply.

• Answer only from sources: if the information isn’t in your documents, the chatbot says so and offers to hand off.
• Cite every source: the user checks it in one click, on the original page.
• A single reference version: old manuals and drafts are kept out of the index.
• Testing before go-live: the chatbot is tested on a list of real questions from your team, with the expected answers, and the gap is measured.

The question log then shows what’s missing from your documents: often the first unexpected benefit.

No-code chatbot platforms are quick to set up. They work when your documents are few, public, and stored in one place, and everyone can see everything.

The business plans of the big assistants, like ChatGPT Enterprise or Copilot in Microsoft 365, can also search your shared folders. If your documents all live in those tools and their answers are good enough for you, keep them: I’ll tell you so.

Custom-built becomes worthwhile as soon as any of these conditions no longer holds: documents spread across several tools (SharePoint, Google Drive, ERP, ticketing tool), fine-grained access rights by person, scanned PDFs or technical tables that are hard to read, answers needed inside your business software rather than in a separate window. At DigitalCX, the chatbot is built on your tools, with the AI model of your choice, on accounts in your name, with no platform subscription locking you in. To set ground rules for ChatGPT or Copilot beyond documents, see AI integration.

An AI chatbot for business reads sensitive documents: contracts, purchase prices, HR files. Three points are settled at the design stage.

Access rights: the chatbot inherits those of your shared folders. A sales rep won’t see payslips just because they asked the right question. They’ll only see what they could already open.

Hosting: the index of your documents can stay on your servers or with a European host. Relevant passages are sent to the AI model at answer time. You pick a provider whose business plan doesn’t train its models on your data, in a European region when it offers one.

GDPR: a data processing agreement with the provider, an up-to-date record of processing activities, and as little personal data as possible in the index.

Cost depends mainly on three things: the number and variety of documents, the number of tools to connect, and how fine-grained the access rights are. A chatbot on fifty manuals stored in one place is nothing like an assistant connected to three systems and fifteen years of archives.

At DigitalCX, each step is priced separately, in writing, before work starts. The audit (10 days) inventories the documents, frequent questions, and access rights. The prototype (5 days) already answers from a sample of your documents, which your team tests. Go-live follows on the agreed date.

After that, two usage costs, billed directly to your accounts with no middleman: the AI model, based on question volume, and hosting for the index. Monthly support is available, never required.