Standard RAG implementations chunk documents into 512-token pieces, embed them, and retrieve by cosine similarity. This works well for general knowledge bases. It works poorly when your knowledge base is 40,000 classified documents with complex inter-dependencies, proprietary terminology, and queries that require synthesis across multiple sources simultaneously.
Contextual Chunking
We developed a technique called "Semantic Partitioning" which respects the logical structure of institutional documents — legal clauses, technical specifications, and historical context — rather than just arbitrary token counts.
Hybrid Retrieval
Our pipeline uses a combination of dense vector search and symbolic reasoning. This ensures that we don't just find "similar" documents, but the *correct* documents based on the institutional logic required for the query.