PIPELINEGuide
Connecting AI knowledge base with existing systems
Does everything need to be replaced for this? Usually not. What can be connected to an existing system landscape, where the limits are, and which part typically takes the longest.
For AI knowledge base, in most cases nothing existing needs to be replaced: inventory management, CRM, calendar, accounting, or phone system are connected via interfaces. What matters is whether the respective system has a documented interface — and the time-critical part is almost never the technology, but obtaining the access credentials.
01 — Integration
Connection with existing systems
The most common misconception is that everything must be replaced. In most cases, the existing system landscape remains, and AI knowledge base builds on top — through interfaces, not through replacement.
What gets connected in practice: ERP, CRM, calendar, accounting, phone system, newsletter system, website. What matters is whether the respective system has a documented interface. Where none exists, there's usually a workaround — but that costs effort and needs to be clarified up front, not discovered later.
The time-critical part is almost never the technical integration, but obtaining access credentials. Starting early here noticeably shortens the project timeline.
02 — 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.
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 — 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.
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.
05 — Next
Related pages
- AI Knowledge Base — Overview
- AI Knowledge Base — Implementation
- AI knowledge base — pros and cons
- AI knowledge base — provider comparison
- AI Knowledge Base — DIY
- AI Knowledge Base — Requirements
- AI Knowledge Base — Data Protection
- AI knowledge base — risks
- 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.