PIPELINEGuide
AI knowledge base: what alternatives exist
Not every problem needs an AI knowledge base. What simpler routes exist, where ready-made standard solutions are sufficient, and when the right answer is: do nothing at all.
Before an AI knowledge base comes into question, it's worth looking at simpler routes: a more clearly written page, a different phone policy, a cleaned-up process. Ready-made standard solutions cover part of the spectrum and hit their limits where proprietary data and integrations come into play. Doing nothing is also a legitimate answer.
01 — Alternatives
What else is out there
Before an AI knowledge base comes into question, it's worth looking at simpler routes. Sometimes a clearly written page, a different phone policy, or a cleaned-up process solves the same problem without any system.
Ready-made standard solutions cover part of the use cases and are quickly available. Their limit lies where your own processes, your own data, or integrations come into play — and in the question of who ultimately owns the data.
The third option is to do nothing. That's a legitimate answer when the process in question happens rarely or when the real bottleneck lies elsewhere. We'll say so if that's the case.
02 — Boundaries
When we advise against it
- The process is still changing. As long as you're adjusting the process itself, manual work is more flexible and cheaper.
- Exceptions outweigh standard cases. Then you're automating exceptions, and that gets expensive.
- There's no data foundation. Without digitally available source data, the basis is missing.
- Volume is too low. With five transactions a month, no system pays off — not even an affordable one.
- The real bottleneck lies elsewhere. If you don't have enough inquiries, you don't need faster processing.
03 — Criteria
How quotes differ
- What exactly is included. Two quotes with the same total can cover completely different deliverables. Ask for scope as a list, not as running text.
- Who delivers what. Content, images, data, access — if it's not specified, it becomes a point of conflict later.
- What operation costs. Calculated over three years, including licenses, hosting, and support.
- What happens if you want to switch. Do you get access, data, and documentation? The answer to this question says more about a provider than any reference.
- Who actually does the work. With small providers this matters: who sits in the meeting, and who then does it.
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.
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.
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.
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.
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 — provider comparison
- AI Knowledge Base — Implementation
- AI knowledge base — how it works
- AI knowledge base — AI disclosure
- AI knowledge base — checklist
- AI Knowledge Base — Data Protection
- AI Knowledge Base — Team and Acceptance
- AI automation
- Document processing with AI
- AI chatbot
- All guides
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.