What document intelligence and RAG are
Document intelligence turns files into structured, searchable knowledge. Retrieval-augmented generation, or RAG, selects relevant source material and gives it to a model so an answer or document can be grounded in evidence rather than memory alone.
When you actually need it
This approach fits legal files, grant documentation, policies, technical manuals, research, and other collections where information is valuable but difficult to locate or compare. It is especially useful when answers must point back to the source.
How it differs from adjacent solutions
RAG works primarily with unstructured or semi-structured knowledge. A semantic layer governs metrics and structured business data. A knowledge graph makes concepts and relationships explicit. An agentic system may use RAG, but retrieval remains a separate engineering problem with its own quality measures.
How we build document systems
We design ingestion, parsing, metadata, chunking, retrieval, reranking, permissions, citations, and evaluation as one pipeline. The architecture reflects document structure and user questions instead of relying on a generic vector-search recipe.
Common mistakes
Typical failures include poor source parsing, lost document hierarchy, indiscriminate chunking, no access control, and judging quality from a few attractive answers. A useful system needs representative questions, retrieval metrics, answer checks, and explicit behavior when evidence is missing.