Custom RAG development
13Floors builds enterprise RAG systems in Germany: document search, internal knowledge assistants and answers with citations instead of hallucinations.
Help teams and customers reach trustworthy answers faster—with citations instead of unsupported output.
Common starting point
- Knowledge is trapped in PDFs, wikis and silos
- Generic models do not know internal facts
- Answers without evidence are difficult to trust
What we deliver
- Ingestion, chunking and embeddings
- Semantic or hybrid retrieval
- Citations and permission-aware access
- Retrieval and answer evaluation
Approach
Audit the knowledge
We examine formats, freshness, permissions and representative questions.
Engineer retrieval
Chunking, search and ranking are tuned to the information users need.
Evaluate answers
Test sets measure grounding, coverage and failure behaviour before rollout.
Concrete solutions
Frequently asked questions
What does RAG mean?
Retrieval-Augmented Generation first finds relevant information in approved sources and supplies it to the language model as context.
What is Agentic RAG, and do companies need it?
Agentic RAG adds planning, tools and multi-step retrieval. Most companies first need a dependable RAG system with citations, permissions and evaluation. Agents are added when the workflow requires them, not as a research demo.
Can a RAG system provide citations?
Yes. We build responses with document and page references so users can verify important claims.