PIPELINEGuide
AI knowledge base: measuring success instead of guessing
Without a pre-defined metric, you can't tell afterwards whether an AI knowledge base was worth it. Which numbers work, which mislead, and when to capture them.
The success of an AI knowledge base can only be measured if the baseline was captured beforehand — two weeks of tracking is enough. Useful metrics are processing time per case, proportion of cases completed without intervention, and external response time. Team satisfaction in the first two weeks is not a useful benchmark.
01 — Measurement
How you measure success
- Define the one number beforehand. Without a baseline value, nothing can be proven afterward. Two weeks of tracking is enough for a reliable reference point.
- Processing time per case. The most direct metric: How long does the affected process take today, how long afterward.
- Percentage of cases that complete without intervention. This ratio typically rises significantly in the first weeks when refinements are made — it's the best early indicator.
- External response time. How quickly does someone who inquires receive an initial binding answer.
- What you should not measure: Team satisfaction in the first two weeks. Every transition feels worse initially than it actually is.
02 — Calculation
How to calculate it
Four numbers are enough: How often does the process occur? How long does it take? What does an hour cost internally? And what does the solution cost — one-time plus operation over three years?
An example: A process that occurs twice a week and takes 40 minutes each time ties up around 69 hours per year. At an internal hourly rate of 45 euros, that's roughly 3,100 euros per year locked in there. If a solution reduces this effort by two-thirds, it often pays for itself — depending on the actual quote — within one to two years. We'll give you the exact figure for your case after the initial meeting.
This calculation is deliberately conservative: it only accounts for the time saved, not the errors avoided or the orders that come through due to faster response. In practice, the benefit is usually higher — but you shouldn't count on it before you've measured it.
03 — Context
What experience shows
Most time isn't lost in answering, but in searching — and in interrupting those who know it by heart.
04 — Questions
Frequently asked questions
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 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.
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.
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.
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.
05 — Next
Related pages
- AI Knowledge Base — Overview
- AI Knowledge Base — Implementation
- AI knowledge base — provider comparison
- AI knowledge base — process
- AI knowledge base — how it works
- AI knowledge base — common mistakes
- AI knowledge base — pros and cons
- AI knowledge base — alternatives
- 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.