RAG implementation Europe

RAG systems that make company knowledge usable by AI.

Ferre Torres B.V. designs and builds retrieval systems for company documents, dashboards, tickets, databases, policies, and workflows so assistants can answer with context and act inside governed processes.

Discuss RAG implementation RAG and agents

Production RAG

Retrieval is only useful when it respects quality, permissions, and workflow context.

Document ingestion, chunking, metadata design, and hybrid retrieval over company data.
Permissions, role-based access, auditability, and privacy-aware deployment patterns.
Evaluations for retrieval quality, answer accuracy, latency, cost, and task success.
Integration with agents, dashboards, internal tools, and Company Brain infrastructure.

RAG readiness

Assess data sources, access rules, document quality, business questions, and evaluation criteria.

Retrieval MVP

Build the first grounded assistant around one workflow, team, or knowledge domain.

Operational scale

Add monitoring, permissions, evaluation loops, tool use, and governance for production use.