Guide

What do you need for an AI knowledge base? The requirements

Before an AI knowledge base even makes sense, a few things need to be in place at the business. What those are — and which supposed requirements actually aren't.

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Quote on Request6 to 10 WeeksAs of 2026

In short

For an AI knowledge base, a business needs three things: a specifically named process that currently costs time or orders, existing content or data to work with, and a person who is authorized to make decisions. Technical knowledge is explicitly not required.

01 — Requirements

What you need for an AI knowledge base

A clearly defined trigger. Not “we should do something with AI,” but a process that demonstrably costs time or orders today.
Existing content or data. AI knowledge base works with what's there. The more complete the starting point, the shorter the setup.
Access to the systems involved. Whoever manages the credentials needs to be involved from the start — this is, in our experience, the most common cause of delays.
One decision-making contact person. One, not three.
Willingness to refine after launch. The first weeks in real operation show what was missing in the concept. That's normal and accounted for.

02 — Participation

What we need from you

A contact person who can make decisions — not just forward them.
Access to the affected systems, in time and not on launch day.
Real examples from daily business — the messier, the more useful for testing.
Two to four hours for coordination, distributed over the project duration.
An answer to the question of what success means. Without it, nothing can be assessed afterward.

03 — 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.

04 — Questions

Frequently asked questions

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.

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.

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.

What if we have multiple languages in our document base?

No problem — the search understands questions and documents regardless of language and finds the right answer even when the question and source are written in different languages.

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.

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.

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.

05 — Next

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.

Send inquiry ↗ office@pipeline-solutions.at OR +43 681 207 64 203