RAG and knowledge-base search with verifiable answers
We build RAG systems when a team needs search across a knowledge base, documents or data with source references. An answer must not look convincing because it guessed: we define approved materials, access rules and abstention when evidence is insufficient.
What we need to start
We need the search objective, document owners, example questions, role requirements and a reviewed set of expected answers. Before launch, we separately test extraction, index freshness and a user's access to passages.
What we deliver
The agreed outcome includes a data model, indexing, retrieval, interface or API, access roles, administration flows and verification criteria. Integrations and document volume are fixed within the stage boundary.
Limits and a common question
RAG does not make every document true and does not replace a knowledge owner. Models, storage, external APIs and their charges are scoped separately; before release we define which actions require human review.