AI Knowledge System
Your company knowledge, instantly available. Precise answers from your own documents, with sources, without making things up.
From problem to solution
Your knowledge is there. Nobody can find it.
The information sits in the scan — but not in the search
Old files, plans and forms often exist only as a scan. A file search finds the document, but not the passage you are looking for inside it.
Visual RAG reads the page as an image, takes tables and layout into account and points directly to the relevant page.
Why: That keeps content usable where classic text recognition (OCR) delivers little or nothing at all.
The question across two sources
“Which deadlines are tied to client Z's cases?” Nobody answers that quickly; it means manual digging.
A knowledge graph links people, cases and deadlines, the answer emerges along the chain.
Why: Exactly where simple tools reach their limit.
General chatbots guess
The ChatGPT or website bot sounds convincing and invents a plausible answer when in doubt.
Our system answers only from your material, with sources. What isn't backed by a document isn't claimed.
Why: The answer comes from your document, not from the model's general training data.
The right method for each question
The right engine for every question.
Depending on the question, a different method makes sense. A router automatically recognises which one fits, often a combination of several.
Answers with a source
The system answers only from your material and names the source for every statement.
Measurable
We deliver accuracy metrics (recall) so you can see how reliably the system answers.
EU-hosted
Sensitive data never leaves the EU. Self-hosting on your own infrastructure is possible.
Quality in a knowledge system
Seven points at which a knowledge system can fail.
- Search (retrieval)
- Are the relevant passages among the hits at all?
- Re-sorting (reranking)
- A second, more precise scorer re-sorts the hits. This stage can be measured separately and directly determines which context reaches the language model.
- Drawing conclusions (reasoning)
- The right passages in the context do not yet guarantee the right answer. The model can still draw a wrong conclusion from them.
- Question quality (query)
- An unclear query with little context can send the search in the wrong direction before the index is queried usefully at all.
- Conflicting sources (reconciliation)
- When two documents contradict each other, the system must not simply smooth the conflict over. It has to recognise it and make it transparent, or resolve it by a defined rule.
- Sufficient context (sufficiency)
- The system needs a criterion for whether the information found is enough for a complete answer. Otherwise it answers too early or keeps searching unnecessarily.
- Time reference (temporality)
- Being up to date is part of answer quality. An outdated document can produce answers that fit formally but are factually superseded.
These seven points can be tested separately. That is exactly what we do, because a good overall score does not show where a system is reliable — and where it is not.
Use cases by area
Where a knowledge system delivers results immediately
Law & legal practice
Files, deadlines, precedents, EU-secure and linked across multiple sources (multi-hop). The question that has to connect two files is answered in seconds, instead of someone piecing it together from several files.
- Query mandates, deadlines and filings in context
- Find precedents across the entire case load
- Data sovereignty: EU-hosted or self-hosted
Sales
“What did we quote for a job like this?” The system finds comparable past quotes in seconds.
Support
Instant answers from the manual relieve the team, around the clock.
Engineering
Datasheets, maintenance history and standards, findable even across thousands of PDFs (Visual RAG). Even poorly scanned legacy files where standard text recognition (OCR) fails become searchable, backed by the exact page.
Service packages
From check-up to ongoing operation
AI health check
Analysis of your existing or planned AI solution: what does it find, what does it invent, where are the gaps?
Company memory
Building and running your knowledge system, on a proven stack and EU-hosted.
Document intelligence
Search in visually complex documents such as tables, plans and forms (Visual RAG): the system reads the page as an image, even without a clean text layer.
Integration & custom
We connect your tools and assistants in a standardised way via MCP and build custom solutions where the standard ends.
Not every case needs a large system. For a plain FAQ in a small shop, a ready-made chatbot will do. We'll tell you that, too. We build where solutions like that reach their limit.
Impact
Seconds
to a sourced answer, without long searches across drives and inboxes.
−70 %
less time spent re-finding knowledge in day-to-day work.
100 %
of answers with a source, each one verifiable.
EU
Hosting in the EU (Hetzner) or self-hosted at your premises.
Project-dependent indicative figures.
Proof in practice
We run it ourselves.
Jukeep is our own AI knowledge memory, in productive use every day. It bundles scattered knowledge, organises it automatically and answers questions with sourced answers, always only from your own material. So we use what we offer ourselves. This website is part of that: the chat in the corner answers from the site content, and our own MCP server (webse.at/mcp) makes the same content queryable for AI assistants.
Ask your bot for an exact number from your price list. Does it actually come back?
Frequent questions
What companies ask us about this
- Does our data leave the building?
- No. The knowledge system runs EU-hosted (Hetzner) or self-hosted on your own infrastructure. Your documents stay under your control, and no public models are trained on your data.
- How do you prevent invented answers?
- The system answers exclusively from your own documents and backs every statement with its source (Retrieval-Augmented Generation, or RAG). If it finds no evidence, it says so openly.
- Can you connect this to our existing tools?
- Yes. We connect your data sources and assistants in a standardised way via MCP (Model Context Protocol). Where standard interfaces end, we build on in a custom way.
- Does it work with scans, tables and plans?
- Yes. For visually complex documents we use Visual RAG: the system reads the page as an image and captures layout, tables and image content too, not just plain text.
- How do we measure whether it works?
- With concrete metrics: recall and answer accuracy on a test set of your real questions. You see exactly which questions the system answers correctly and which it doesn't.
Ready to put your knowledge to work?
Let's build your company memory.
Get in touch, no strings attached. We'll look at where a knowledge system gives you the biggest lever, and recommend the right size.
Discuss your project