PIPELINEGuide
AI knowledge base: where to start without losing focus
The most common mistake when starting is not the wrong technology, but the wrong first step. A sequence that has proven itself in small businesses.
Getting started with AI knowledge base doesn't begin with the technology, but with a week of recording: what repeats, what costs the most time. After that, a single, clearly defined process with a measurable goal — only with clear scope do quotes become comparable at all.
01 — Getting started
Where to begin
Name the bottleneck
Not the most interesting thing, but what takes the most time. A week of tracking what repeats delivers a more realistic list than any workshop.
Define a small scope
One process, one measurable goal. What works gets expanded — what doesn't costs little.
Survey the starting situation
What content, data, and access are available. For AI knowledge base, this is the factor that most strongly influences effort and timeline.
Decide who is responsible
For the implementation and for operations afterward. Without this definition, every system becomes outdated.
Only then request proposals
With a clear scope, proposals become comparable. Without it, you get numbers that have nothing to do with each other.
02 — Fit
When it's worth it
- The process in question occurs at least weekly.
- It follows rules you can write down — even if there are exceptions.
- Today it's manual work that no one enjoys doing.
- There's one person who decides, not three who need to coordinate.
- You can name what would tell you it worked.
03 — Checklist
Clarify before the first quote
- What should improve? In one sentence, no jargon.
- How will you know? One number that should change.
- Who decides? One person, not a committee.
- What budget is available? A range is enough — it makes quotes comparable instead of arbitrary.
- By when? And what the deadline depends on.
- Which systems are involved? And who has access to them.
- What do you already have? Content, data, preliminary work — this reduces the cost.
04 — Questions
Frequently asked questions
Does this have to run in the cloud?
No. For sensitive areas, we use models that run entirely on your own hardware. That costs more to set up and slightly reduces answer quality, but no document leaves the building.
How long does setup take with a large document base?
It depends less on volume than on condition: well-structured repositories with few duplicates are ready in a few weeks, grown repositories with many old versions need a cleanup round first.
What is RAG?
The abbreviation stands for retrieval-augmented generation. Instead of having the language model answer from memory, your documents are searched first, and only the passages found are used to generate the answer. That's why the answer has a source.
Can we roll this out to more departments step by step?
Yes, that's the usual approach. Start with one department, learn from their questions and results, then connect the next one — rather than releasing everything at once.
Our documents are a mess. Will it still work?
To a certain extent, yes — the system handles different formats and structures. What it cannot do is resolve conflicting information. If three versions of a price list are in circulation, you need to clarify which one applies first.
Will everyone see everything?
No. The permissions from your existing systems are carried over. If someone isn't allowed to open a folder today, they won't get answers from it either.
How do we detect outdated answers?
Through date references and marking of documents flagged as obsolete. Analytics also show which sources are used most often — those you maintain first.
05 — Next
Related pages
- AI Knowledge Base — Overview
- AI Knowledge Base — Requirements
- AI knowledge base — measuring success
- AI knowledge base — process
- AI knowledge base — risks
- AI Knowledge Base — Implementation
- AI knowledge base — practical examples
- AI knowledge base — provider comparison
- AI automation
- Document processing with AI
- AI chatbot
- All guides
Next step
We'll look at your starting position.
Tell us what's eating up time today. That becomes an assessment with effort and benefit — and only then an offer.