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Use Case
Your company's knowledge is scattered across wikis, documents, tickets, and legacy systems. A RAG search answers questions in natural language, with source citations and full respect for access permissions.
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RAG (Retrieval-Augmented Generation) combines a search across your own content with a language model. The answer is based on sources found in your data, so every statement is backed by a source. That keeps it verifiable and current.
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Sources
One search across what you already have.
Approach
How we build a knowledge search that gives dependable answers.
We connect your wikis, file storage, and legacy systems, clean up the content, and prepare it for search.
For every question, the system pulls the relevant passages from your sources, and only the ones the user is cleared for.
The language model formulates the answer from the passages it found and points to the source, so every statement can be verified.
We measure answer quality against real questions, tune it, and keep the search current in operation.
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For internal knowledge search, RAG is almost always the right choice: cheaper, easy to keep current, and with source citations. Fine-tuning pays off more for style and format, not for factual knowledge.
Experience
The appDev team has been building backend and integration software for over 15 years, including for health insurers, banks, and industry. Bringing together data from many systems, mapping permissions cleanly, and working in a way that stays traceable is everyday business there.
That is exactly what a knowledge search needs to earn trust inside a company. You speak directly with the permanently employed senior engineers who build your solution.
Processing and storage within the EU.
Data minimization and access control, built in from the first draft.
A conformity assessment for your use case is part of it.
FAQ
RAG connects a search across your own documents with a language model. The answer draws on the sources it finds, which makes it backed up and current.
For factual knowledge and internal search, RAG is usually better: cheaper, easy to keep current, and with source citations. Fine-tuning is more suited to a fixed style or format.
No. Access control makes sure users only see sources they are cleared for.
Every answer draws on documented passages from your sources and cites them, so you can check them. If the source coverage is too thin, the system makes that clear. That keeps the search dependable.
Wikis, SharePoint, file storage, manuals, tickets, and legacy systems. We clarify which ones matter for you at the start.
A first, useful knowledge search is ready in the pilot within six to eight weeks. We scope the exact extent precisely up front.
Getting started
You start with a Discovery for €1,900, creditable. We look at your sources and sharpen the use case. You get a binding fixed-price quote. The pilot then brings the search into production in six to eight weeks, from €39,000.
Keep reading
Get in touch
Tell us briefly where your team looks for answers today. We'll tell you whether a RAG search pays off as a fixed-price pilot — and if it doesn't, we'll tell you that too.
Pick an open slot in the calendar directly. Free and without obligation.Briefly describe what it’s about. A reply the same working day.
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