PIPELINEGuide
How long does an AI knowledge base take? Realistic timeline
An AI knowledge base project isn't a weekend job, but it's not a six-month project either. What happens in which order, where things typically get stuck, and how to shorten the timeline.
An AI knowledge base takes 6 to 10 weeks in practice. The biggest time factor is almost never the implementation itself, but how quickly content, access, and decisions come from your side.
01 — Timeframe
How long it takes
- Typical duration
- 6 to 10 weeks
- Price
- Individual based on effort
The timeline starts after approval of the quote. Lead time for initial meeting and quote: typically one to two weeks. We provide a fixed price after this initial meeting.
02 — Process
The steps in detail
Define sources
Which repositories go in, which stay out. Usually less is more.
Clarify permissions
The permissions model from your systems is adopted, not reinvented.
Index and test
We test with real questions from your business and measure whether the right documents are found.
Rollout
Start with one department, then expand. The analytics show where documentation is missing.
03 — Bottlenecks
What typically causes delays
- Content and deliveries. By far the most common cause of delay. Text, images, price lists, access credentials — what's missing blocks progress.
- Decisions. When it's unclear who approves, the project waits. A named contact person with decision-making authority noticeably shortens the timeline.
- Access to third-party systems. Getting access to inventory management, phone systems, or CRM regularly takes longer than the integration itself.
- Endless review rounds. Two correction rounds are normal, five are a sign that the goal wasn't clear at the start.
04 — Scope
What's included
- Indexing your repositories: file servers, SharePoint, Google Drive, Confluence, email archives
- Answers with source references and jump to the original document
- Permissions model — who can see what also applies to search
- Detection of outdated documents to prevent obsolete answers
- Analytics on what questions are asked and where documentation is missing
- Hosting either in the EU cloud or on your own infrastructure
05 — 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.
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.
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.
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.
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.
06 — Next
Related pages
Next step
Let's talk briefly about whether this makes sense for you.
A twenty-minute conversation is usually enough to clarify whether a project makes sense. If not, we'll say so — that's cheaper for both sides than an offer nobody needs.