Question set
Which business questions should the RAG system answer correctly?
RAG consultant Netherlands
RAG consulting for reliable AI over company knowledge.
Ferre Torres B.V. helps companies design, test, and deploy RAG systems with source attribution, permissions, evaluation, monitoring, and a practical path from proof-of-concept to production.
RAG work
Retrieval quality matters more than a polished chat interface.
- Data source analysis across documents, knowledge bases, tickets, policies, and internal systems.
- Chunking, metadata, hybrid search, reranking, source citation, and retrieval evaluation.
- Permission-aware deployment for internal users, regulated workflows, and sensitive company knowledge.
- Production path through monitoring, feedback, ownership, and continuous improvement.
Sources
Which documents are authoritative, duplicated, outdated, or restricted?
Evaluation
How will expert reviewers judge quality before rollout?
Interface
Should RAG appear as chat, dashboard, workflow assistant, or API?
Next step
Bring the documents and the questions users actually ask.
That is enough to scope a narrow RAG MVP and define the first evaluation set.