i
Connect. Gather the sources.
We collect the knowledge where it is created, without duplicate maintenance.
SharePointConfluenceFile serverDMSOutlookJiraZammad
Pixelschnitzel · RAG
Your knowledge.
With sources.
Reliably queryable.
Contracts, manuals, tickets, emails: your knowledge is scattered everywhere. Our RAG system makes it queryable, with a dependable answer and a traceable source.
· Context ·
Full-text search
Full-text search
finds words.
We find answers.
Search “Which rule applies to complaints over €1,000?” and you get PDFs; a standard chatbot makes up an answer. A RAG system retrieves the relevant documents from your sources before every answer and lets the language model answer only on that basis, with a citation.
· Structure ·
Four steps.
Four steps.
From source
to answer.
ii
Understand. Structure & vectorise.
Documents are split up semantically, enriched with permissions, validity period and source, and moved into a vector store.
QdrantWeaviateEmbeddingsOCRMetadata
iii
Answer. Question → source → answer.
Relevant sources from the vector store are handed to a language model; it answers only on that basis and names them.
Llama 3.3Qwen 2.5Guardrails“Don’t know”Citations
iv
Govern. Permissions, audit, feedback.
Whoever asks only sees what they are allowed to see. Every request is audited; feedback improves source selection and prompt strategy.
RBACAudit logFeedback loopMonitoring
· What’s different ·
Answers with a
Answers with a
citation,
not from the gut.
Every answer shows where it comes from. Hallucinations become rarer, and the system may say: “I don’t know that.”
Contract § 4.3
Source 1
Policy 12
Source 2
Manual 7.1
Source 3
Answer
→ based on S1 + S2
· Application ·
Six use cases
Six use cases
that take the load off right away.
i.
Sales. What did we agree on?
“What terms apply to customer X?”, answered in seconds, with a contract reference.
ii.
Support. Solution knowledge from tickets.
Thousands of old tickets become a queryable knowledge trove.
iii.
Compliance. Policies within reach.
Terms and conditions, data protection, compliance rules, answered with the source passage instead of a 50-page PDF.
iv.
Clinic. Guidelines & SOPs.
Hygiene regulations, training materials, guidelines, queryable on the ward.
v.
Administration. Case & procedural knowledge.
Procedural manuals, decrees, legacy cases, answered concretely, with a source reference.
vi.
Plant. Maintenance & engineering.
Engineering drawings, maintenance logs, instructions, answers right at the machine.
Knowledge that sleeps in a file
is knowledge that does not exist.
is knowledge that does not exist.
· Frequently asked ·
Answers,
Answers,
even before
you ask.
What sets RAG apart from a normal chatbot?
A normal chatbot answers from the model’s knowledge, often wrongly. RAG retrieves the relevant documents from your sources and lets the model answer only on that basis, with a source.
What happens to permissions from SharePoint or DMS?
Every request is checked against the permission model; staff only see what they are allowed to access in the source system.
Can the system also say “I don’t know”?
Yes. If the sources provide no dependable answer, the system says exactly that instead of making something up.
Where does the system run?
The standard is a controlled environment in Germany, on request fully on-premise, see On-premise AI for business.
How long does it take to get started?
First dependable answers on a defined source typically after 2–4 weeks, broader integration in 6–12 weeks.
What does it cost?
Setup in the low five figures plus ongoing operation; a concrete figure after the first conversation.
How is the system kept up to date?
Sources are re-indexed regularly: changed documents flow in automatically, superseded ones are removed.
· First conversation ·
Give us
Give us
one source.
We’ll show you answers.
One defined source set is enough. In a single meeting you’ll see what answers and sources look like, on your real content.