Your wiki is full. Nobody opens it.
An AI knowledge base reads what you already have, PDFs, contracts, tickets and transcripts, and answers in plain English with citations. You organise nothing first.
Drag sideways to see the whole diagram
An AI knowledge base reads everything you already have, builds a map of how it connects, and answers questions in plain English, with a citation to the source paragraph.
A wiki needs constant maintenance to stay useful. This runs on what is already there.
The work moves from writing to asking
The biggest cost of a wiki is keeping it current. An AI knowledge base takes that job away.
| Traditional wiki | Generic AI search | Certant AI knowledge base | |
|---|---|---|---|
| How content is created | Humans write and tag pages. | Indexes whatever it's pointed at. | Reads everything, builds a map of how it connects. |
| Has to be organised first | Yes, taxonomy is the project. | Sort of, clean inputs help. | No. Point it at the mess. |
| Answers with | A page you have to read. | A best-guess paragraph, no source. | A plain-English answer and citations. |
| Stays current | Only if someone updates it. | Re-index manually. | Re-reads when documents change. |
| Trust signal | Last-edited date. | Nothing to check. | Click any sentence, see the source paragraph. |
| Lives | One web app. | One search box. | Wherever your team works: Slack, Teams, a portal, an API. |
Read, map, answer
Read everything. Build the map. Answer with citations.
Connects to where your documents already live, and re-reads them when they change.
Shows how everything connects, and you can open it to trace any answer back through it.
Come back to anyone who asks a question in plain English.
You don't have to clean your data first
The usual advice is a long data-readiness project before any AI touches your documents. It isn't necessary.
Clean your data first
Build a taxonomy. Migrate Confluence to SharePoint. Tag every page. Then, maybe, put an AI on top.
Point it at the mess
Certant reads what you have, where it is. The structure comes out the other end, and you can reorganise later once you can see what is missing.
What buyers ask
The questions that come up before anyone tries it.
What is an AI knowledge base?
An AI knowledge base is a system that reads every document and data source you have, builds a map of how everything connects, and lets anyone ask a question and get a cited answer, without you organising the source material first.
How is an AI knowledge base different from a wiki?
A wiki requires people to write, structure, tag and maintain pages. An AI knowledge base reads what you already have: PDFs, contracts, tickets, transcripts, and creates the answers on demand. The work moves from writing pages to asking questions.
Do I need to organise my documents first?
No. Certant reads what you have, where it is, and builds the structure for you. Tags and metadata help, but they are not required.
What makes Certant different from other AI knowledge bases?
Citations on every answer: click a sentence and see the source paragraph. A map of how everything connects that you can open and inspect. And the choice of where it runs, in our cloud or fully inside your own walls.
Can it replace my Confluence or SharePoint?
It can sit on top of them and answer across them. Most teams keep their source-of-truth tools and add Certant as the answer layer. Some replace the wiki entirely once they realise nobody was reading it anyway.
How long does it take to set up?
A connected document set is searchable the same day. A full rollout with permissions, audit logging and a security review takes a few weeks.
Stop writing pages. Start answering questions.
Upload a folder of documents, watch Certant read them, then ask it anything.
A wiki is a place to write. An AI knowledge base is a place to ask.