Enterprise RAG systems built on your own knowledge

Retrieval-augmented generation consulting: when RAG is the right choice

A language model knows nothing about your company. RAG fixes that by retrieving relevant documents at runtime and handing them to the model as context — no fine-tuning, always current data, traceable sources. It is the right choice when answers must be grounded in your knowledge and that knowledge keeps changing.

In the consulting phase we first clarify whether RAG is needed at all: sometimes a large context window is enough, sometimes you need an enterprise knowledge base with permissions, and sometimes RAG is just one building block in a larger agent system.

RAG architecture best practices: custom RAG pipelines over off-the-shelf

The quality of a RAG system is decided not by the model but by the pipeline in front of it. We build custom RAG pipelines along proven architecture principles:

• Clean preprocessing and chunking of documents — matched to format and question type
• Hybrid search: vector database plus full-text and metadata filters
• Permissions from your systems are respected at retrieval time
• Re-ranking and source citations so answers stay verifiable
• Evaluation with real questions from your team — measured, not guessed

Agentic RAG: integrating RAG and AI agents

Simple RAG systems answer one question with one search. Agentic RAG goes further: an agent decomposes the question, searches several sources multiple times, checks for contradictions and calls tools when needed — for example to fetch live data from your ERP. Integrating RAG and AI agents turns a knowledge base into an assistant that gets tasks done.

Vector database, hosting and data protection

Whether pgvector in your existing Postgres, a dedicated vector database or a managed service: we choose the store based on data volume, stack and operational effort. Sensitive company data can stay entirely in the EU or Switzerland — including the embedding model, if the use case requires it.