Source quality
Which documents are authoritative, outdated, duplicated, or sensitive?
RAG implementation Netherlands
Reliable AI retrieval over private company data.
Ferre Torres B.V. implements RAG systems for companies that need AI answers grounded in documents, policies, reports, tickets, knowledge bases, and operational data with permissions and evaluation from the start.
RAG scope
A good RAG system is an evaluated retrieval product, not a prompt around a document dump.
- Ingest documents and metadata from the systems where company knowledge already lives.
- Design chunking, ranking, source attribution, permissions, and feedback loops around real questions.
- Evaluate retrieval quality before scaling to larger user groups or sensitive workflows.
- Expose the system through assistants, dashboards, APIs, or workflow-specific interfaces.
Answer quality
What makes an answer correct, useful, and acceptable to a domain expert?
Permissions
Which users, teams, or roles should see different subsets of the knowledge base?
Evaluation
Which test questions prove the system is ready for production use?
Next step
Start with one retrieval workflow and a test set of business questions.
Define the documents, user group, expected answers, and deployment constraints.