- Permission-aware retrieval
- Cited answers
- Measured retrieval quality
- A maintainable ingestion path
SERVICE / 05
RAG Systems
A retrieval system designed around source quality, permissions, citations, evaluation, and the questions people actually ask.
The fit
Built for a real decision or delivery constraint.
Teams with internal documents, product knowledge, or support content that must be searchable without inventing answers.
- Source-level access
- Grounded response rules
- Evaluation datasets
- Freshness and deletion paths
- Ingestion and retrieval pipeline
- Evaluation suite
- Answer interface or API
- Operations documentation
- Production deployment
Scope is agreed before implementation. Material changes are estimated separately. Third-party infrastructure and model usage remain visible and are normally billed directly to the client.
What this is not
No black-box demo. No indefinite discovery.
Every engagement begins with explicit acceptance criteria and ends with source, tests, deployment configuration, and operational documentation appropriate to the scope.
If the use case does not justify an AI system, the recommendation may be normal software, simpler automation, or no build at all.
Paid entry engagement · €1,500
Start the rag systems with a clear boundary.
The Architecture Review turns a use case, prototype, or delivery opportunity into a defensible implementation plan in five business days.