AI answers from your own documents.
The difference
Generic AI vs. AI on your data.
An off-the-shelf chatbot knows the internet, but not your price lists and contracts. Only once it is connected to your data does a language model become a tool your team can rely on.
Generic
Half-knowledge from the internet
Answers are based on public text – not on what actually applies in your business.
Vague on expert questions
When it comes to your products, terms and internal rules, the model stays generic or guesses.
No verifiable sources
There is no way to check whether an answer is correct. That makes it risky in day-to-day work.
Yesterday's knowledge
Anything that changed since the model was trained – new prices, new processes – it simply doesn't know.
On your data
Answers from your documents
Manuals, contracts, quotes and tickets become the knowledge base. The AI quotes from them instead of guessing.
With sources cited
Every answer points to the document and passage it came from. Your team can verify at any time.
Your tone, your terminology
Through fine-tuning, the model learns your phrasing and technical terms. Answers then sound like your company, not like a textbook.
Data under your control
Your own servers, an EU cloud or an API with a DPA: you decide where your data lives and who has access.
How it comes together
From scattered documents to a reliable assistant.
We don't train a model from scratch; for most businesses that would be needlessly expensive. Instead, we connect a proven model to your knowledge, step by step.
Collect the data
Together we map out where your knowledge lives: document folders, wikis, emails, ticket systems, past quotes.
Prepare & index
The content is cleaned, structured and made searchable – the foundation for precise answers.
Connect: RAG & fine-tuning
The model accesses your knowledge base live. Where needed, we sharpen tone and terminology with fine-tuning.
Test & safeguard
We test with real questions from your daily work, set guardrails and define what the AI must not answer.
Operate & maintain
New documents flow in automatically, answer quality is monitored, and changes stay predictable.
Most of the work is not in the model but in cleaning up your data and in testing. That is where it is decided whether the answers are useful.
Examples
What businesses do with it.
Internal knowledge assistant
Data basis
Manuals, process descriptions and the internal wiki – knowledge that used to sit in folders and in people's heads.
Result
Employees ask in their own words and get the right answer, with a reference to the exact chapter. New colleagues find their footing faster, and questions to senior staff go down.
Quoting assistant
Data basis
Your quotes from recent years, price lists and service descriptions.
Result
A new inquiry turns into a draft quote based on comparable past quotes – in your tone and with your wording. Final approval stays with you.
Support answers with sources
Data basis
Your ticket history: thousands of already resolved customer requests, including the answers.
Result
For every new request, the system suggests a reply and shows the resolved cases it is based on. Your team reviews, adjusts and sends it off instead of starting from zero.
Your data, your rules
You decide where your data lives.
From a model on your own servers to an API with a data processing agreement: we follow your requirements for data protection and data sovereignty. For many businesses in the DACH region, that is the precondition for working with AI at all.
Frequent questions
What clients want to know before starting.
No. For a knowledge base built with RAG, a few hundred documents are often enough – manuals, quotes, resolved tickets. What matters is not the volume but that the content is correct and maintained. We will tell you upfront whether your data can support the intended use case.
That is your decision. Options include your own servers, an EU data centre or an API with a data processing agreement. Your documents are not used to train third-party models, and we document exactly which data flows where – as the basis for your GDPR assessment.
It depends on the model and how much it is used. With API solutions you pay per use, usually a low to mid three-figure amount per month; with your own servers there are fixed infrastructure costs instead. We calculate both options transparently before the start – no hidden items.
It cannot be prevented entirely. But we reduce the risk substantially: the AI answers only from your documents, cites the exact passage for every answer, and when it finds nothing, it says so. For critical topics, a human does the final check.
Next step
What knowledge is sitting in your folders and inboxes?
Tell us which questions keep coming up in your business. A short conversation is usually enough to see whether your data can support it – and which use case pays off first.