Buyer guideWhat is an enterprise
AI knowledge base?
An enterprise AI knowledge base lets staff ask questions in plain language and get answers from approved company documents, data and systems, with the source to check. This guide explains how it works, how it differs from a chatbot, and what to test before you buy.
Answers from your own documents,
with proof.
A public AI chatbot answers from what it learned on the internet. An enterprise AI knowledge base answers from your material: policies, SOPs, contracts, manuals, spreadsheets and connected business systems. It finds the relevant passages first, then writes the answer from them. This method is called retrieval-augmented generation (RAG).
Because the answer is built from your sources, a good system shows which document and page it used. Staff can check it in seconds, and managers can see why an answer was given. Access rules decide who can search what, so a staff member only gets answers from sources they are allowed to see.
iKB is one example of this kind of platform. It adds reports, dashboards and a Decision Record for each answer, and can run hosted, in a private cloud, on-premise or fully air-gapped.
Knowledge base, chatbot
or plain search?
Each tool solves a different problem. This is a general comparison of the approaches, not a ranking of products.
| Enterprise AI knowledge base | Public AI chatbot | Keyword search | |
|---|---|---|---|
| Where answers come from | Your approved documents and systems | General training data and the web | Your documents, as a list of files |
| Shows the source | Yes, document and page | Not reliably | Yes, but you read the file yourself |
| Respects staff access | Yes, per topic or role | No | Depends on the tool |
| Gives a direct answer | Yes | Yes | No |
| Can run on your own servers | Often, check the vendor | Rarely | Often |
Eight things to test
before you buy.
Use real questions from your team and a set of documents you know well. Check what happens when the answer is not in the documents.
- 01
Source references
Every answer should point to the document and passage it used. Open a few and check they really support the answer.
- 02
Missing information
Ask questions your documents cannot answer. A good system says it does not know, rather than making something up.
- 03
Access control
Sign in as staff with different roles. Confirm each person only gets answers from sources they are allowed to see.
- 04
Source updates
Replace a policy with a newer version. Check that answers use the current version and the old one is no longer cited.
- 05
Where data goes
Map which parts run on your infrastructure, and which AI models or connectors send data outside. Private hosting does not automatically mean no data leaves.
- 06
Audit trail
Check what is recorded behind an answer: sources, policy checks and the AI model used. Ask how records are exported and retained.
- 07
Channels
Test where staff will actually ask: web chat, Microsoft Teams, Slack or your website widget.
- 08
Model choice
Check whether you can choose AI models, use your own local model, and see usage and cost.
Common buyer questions.
Is an AI knowledge base the same as RAG?
Does an AI knowledge base replace staff judgement?
How should we start?
Test an AI knowledge base on your own documents.
Book a demo with real questions from your team. Check the answers, sources and controls yourself.