RAG Systems
Your company knowledge. Securely accessible and traceable.
Employees should receive authoritative answers from approved sources — not from a general language model.
RAG is not a chatbot showcase. First we clarify whether controlled access is the right lever at all.
BUSINESS PROBLEM
Knowledge is scattered. Answers take too long.
When teams search PDFs, folders, CRM and ERP, they lose time, duplicate work and risk giving customers inconsistent answers. A general chatbot does not solve this because it does not know which company information is approved.
Only approved sources may form the basis.
Answers to recurring questions should be easy to find without copying information between tools.
If the source is missing, the system escalates instead of guessing.
FIT
When RAG helps — and when it does not
RAG is often useful
- Many authoritative documents that come up regularly
- Answers must be grounded in approved sources and constrained by access rules
- Roles decide who may see which knowledge
A simpler solution may be better
- A few stable FAQs or a clear process handbook
- Rare one-off cases without a recurring pattern
- Decisions that always need a person for legal or financial reasons
FLOW
From source to a controlled answer
The flow stays deliberately simple. Technology follows the required protection level, not the other way around.
Only content you have explicitly released.
The system finds the relevant passages for the question.
The answer stays bound to the retrieved context.
It is clear where the statement comes from — or the system refuses.
CONTROL
Roles, refusal and human escalation
Public, internal and sensitive knowledge are considered separately.
If a suitable source is missing, the system says it does not know the answer.
Sensitive or unclear cases go to a responsible person.
Only necessary information is processed or stored.
QUALITY ASSURANCE
A plausible answer is not enough.
A RAG system is useful only when it retrieves relevant knowledge, keeps claims grounded in approved sources, respects access rules, and refuses or escalates when confidence is insufficient.
Retrieval quality
Does the system find the relevant, approved passages for the question?
Source grounding
Can the answer’s claims be supported by the retrieved context?
Access and boundaries
Do roles, permissions, refusal behaviour and human escalation work as intended?
Operations and security
Are data flows, sensitive information, prompt-injection risks, logging and deletion handled transparently?
Automated metrics provide signals, not a final quality verdict. Critical cases are checked against subject-matter-approved reference questions and reviewed by people.
DATA FLOW
Documented decisions instead of a blanket assurance
Both are conditional architecture choices based on the required protection level — not a default promise.
Which sources, which processing, which provider: named before work starts.
Only where the provider and agreement support it. No blanket no-training guarantee.
We work with privacy in mind. This is not legal advice.
WHAT YOU RECEIVE
What the introductory call clarifies
Suitability and scope are clarified in the introductory call.
Whether RAG, a simpler structure or visibility work first makes more sense.
Which sources may be used and where processing happens.
Test questions, refusal rules and the escalation path.
Only if the usefulness justifies the control effort.
FAQ
Definition and limits
What is a RAG system?
RAG first retrieves approved company information and then forms an answer. That makes answers more traceable and easier to constrain. AI output remains probabilistic and needs human review for important decisions.
When is RAG useful?
When employees repeatedly search documents, folders, CRM or ERP for authoritative answers, and those answers must be grounded in approved sources and constrained by access rules.
When is a simpler solution better?
When the knowledge base is small, questions are infrequent, or the answers already exist in a clear FAQ, intranet or process handbook. In those cases, better structure is often enough — without a retrieval system.
What happens if the system does not know the answer?
It should refuse or say “I do not know” and escalate to a responsible person. The system must not guess in unclear or sensitive cases.
Does all data stay in Switzerland automatically?
No. Swiss hosting or on-premise are architecture choices based on the required protection level. Data flows are documented. This is not a residency or compliance guarantee.
Does this automatically mean no-training?
No. No-training language applies only where the selected provider and agreement actually support it. We review that before use and do not invent a blanket assurance.
What do we receive from the introductory call?
An initial assessment of whether RAG is suitable, which sources can be used and which data flows must be documented first. Suitability and scope are clarified in the introductory call.
NEXT STEP
An introductory call or a RAG suitability discussion
We clarify whether a controlled knowledge system is the right next step.