# RAG systems for company knowledge

> Production-ready RAG systems for your company knowledge: data pipeline, retrieval quality, evaluation and operation on your own infrastructure.

Quelle: https://kontrollfeld.de/en/services/rag/
Sprache: en

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We build retrieval-augmented generation systems that get past the prototype: with a clean ingestion pipeline, measurable retrieval quality and an operating model on your own infrastructure.

## When RAG is worth it

Knowledge sits scattered across wikis, drives, mailboxes and ticketing systems. The question is rarely whether a language model can answer from it — it is whether the answer can be backed up. That is what RAG does: the model receives retrieved passages as context and answers from them rather than from memory. Every statement can be traced back to its source.

Not every knowledge problem needs this. For a bounded, well-structured corpus a faceted search is faster, cheaper and easier to explain. RAG earns its keep where the question is asked in free text and the answer has to be assembled from several documents.

## Decisions that come up

**Which sources belong in it.** Access rights have to travel with them. Otherwise the system answers questions the asker was never allowed to ask — and does so in a convincing tone, which makes it worse.

**How documents are split.** A document is not retrieved as a whole but in sections. Where the cuts fall and how much overlap remains decides more about answer quality than the choice of model.

**Which vector store.** If Postgres is already running, pgvector is usually the answer with the least operational overhead; for large corpora with heavy filtering a specialised database shows its strengths. We wrote the criteria up separately: [Qdrant vs. pgvector](/en/articles/qdrant-oder-pgvector-self-hosted-rag/).

**How quality is measured.** Without an evaluation harness every change to the pipeline is a bet. With a set of reference questions it becomes a number that can be compared before and after.

**Where the model runs.** Vector store, pipeline and documents stay on your infrastructure by default; model calls can be pointed at EU endpoints or at a locally operated model.

## What you get

- Data preparation and a chunking strategy for your source systems
- Embedding and vector store selection, self-hosted and GDPR-compliant
- Evaluation harness: retrieval and answer quality as a regression test
- Role-based access that inherits the permissions of the source systems
- Operation, monitoring and cost control

## How you know it holds

Every answer carries its sources, and the citations check out. A change to the pipeline shows up as a number in the evaluation run rather than as a gut feeling. And a new source system can be added without rebuilding the whole thing.