AI chatbot for company documents compared with a general AI chatbot

A general AI chatbot is built to answer almost anything. It draws on what it learned in training, and when you attach a file it reads that too, mixing both sources into one fluent reply. That breadth is useful for brainstorming, drafting and explaining concepts.

A document chat is built for one job: answering from a defined set of documents and showing where each answer came from. It deliberately does not reach beyond those documents. That narrowness is the point when the question is "what does our contract say", not "what do contracts usually say".

The table is not a judgement on general chatbots. It is a description of what each is designed to do.

• Mixed sources. When a reply blends your document with general knowledge, you cannot tell which sentence came from where. "Notice period is 30 days" might be your contract or a common default.
• Gaps get filled. Ask about a clause the document does not contain and a general chatbot often produces a reasonable-sounding answer anyway. In legal, HR or finance work, that is a risk, not a convenience.
• One file at a time. Comparing five supplier agreements or pulling renewal dates from forty contracts means repeated uploads and manual copy and paste.
• Personal by default. A colleague cannot see your chat or reuse your answer with its source, so the same question gets asked again and answered differently.
• Unclear data handling. Depending on the account type and settings, content sent to a general chatbot may be retained or used to improve models. Your company policy may forbid pasting client documents at all.

• Answers only from your files. Every answer is grounded in the documents of the selected space. If they do not answer the question, you are told so, with the closest passages checked.
• A citation for every statement. Each sentence points to a document and page (slide for decks, sheet and cell range for spreadsheets), and the cited passage is checked in code to exist and contain the quoted text before the answer is shown.
• Work across many documents. Ask a folder, compare documents side by side, or extract the same fields from hundreds of files into a table.
• Team spaces. Owners, editors and askers, saved threads and answers shared by link. Business and Enterprise plans add an audit log.
• Clear privacy terms. Documents are encrypted in transit and at rest, never used to train models, and processed by AI model providers bound by contract not to train on your content. Deleting a space deletes its documents and index.

More on the privacy side is on the secure AI document chat page.

A general AI chatbot remains a good tool for work that does not depend on a specific document: drafting a first version of an email, explaining a financial term, brainstorming headings for a report, rewriting a paragraph you already checked. Many teams use both, with a simple rule about which questions go where.

When a document chat is the right choice

• The answer must match a specific contract, policy, report or proposal.
• Someone else will rely on the answer and needs to check it.
• The work spans many documents or a whole shared drive.
• The documents are confidential and company policy sets rules for where they go.
• The output is a table, a comparison or a summary that has to be traceable line by line.

Write a two-line rule into your team guidelines:

1. 1Questions about what our documents say go to the document chat, and the answer is quoted with its page.
2. 2Questions about general concepts, drafting and wording can go to a general AI chatbot, but nothing it says about our documents is used without a citation from the source.

This one rule removes most of the confusion about which tool to trust for what.

• Does it answer only from documents you select?
• Does every statement carry a document and page citation that opens the passage?
• Is each citation checked against the page before the answer is shown?
• Does it say plainly when the answer is not in the documents?
• Can it connect the drives where your files already live, such as Google Drive, SharePoint, OneDrive or Dropbox?
• Can it compare documents and extract fields to a spreadsheet?
• Does it have team spaces with roles?
• Are your documents excluded from model training, by contract?
• Is the price published, with clear limits on questions and pages?

For a closer look at the verification side, read why AI answers need citations.

• Pick the documents people paste most often, such as the handbook or the standard contract, and put them in a shared space first.
• Show the team how a citation opens the page, so checking becomes a habit.
• Ask everyone to share answers by link, not by screenshot.
• Agree on who owns each space and removes outdated files, so the answers stay as current as the documents.
• Review the first month of questions together and note which ones ended in an honest refusal. Those are gaps in your documents worth fixing.