A production case study in building structured semantic understanding on top of a Retrieval-Augmented Generation (RAG) system — improving retrieval precision, response reliability, and enterprise governance for an AI-powered document intelligence platform.
A document intelligence platform relied on vector-based RAG retrieval to answer queries over large, heterogeneous document corpora. Retrieval quality was inconsistent: the system had no structured representation of the domain, so it missed context, classified poorly, and produced variable LLM responses driven by inconsistent terminology.
I designed and owned an ontology extraction and semantic intelligence pipeline that sits between raw documents and the retrieval layer. It performs layout-aware parsing, extracts a governed controlled vocabulary using a hybrid LLM-plus-rules pipeline, and injects that structured knowledge into query expansion, retrieval, reranking, and prompt construction.
Scale and surface area: multi-workspace document ingestion, thousands of extracted ontology terms under human-governed approval, and a phased architecture evolving from flat glossary to semantic knowledge graph to an operational intelligence layer.
Ownership: I owned the end-to-end design — parsing strategy, extraction pipeline, deduplication and conflict resolution, query-time matching, reranking integration, the human-in-the-loop governance workflow, and the multi-phase architectural roadmap.
Impact: Achieved 95%+ extraction and classification accuracy, materially improved RAG retrieval relevance and LLM response consistency, and delivered enterprise-grade governance (RBAC, audit trail, bulk review) that made the AI output trustworthy enough for production adoption.
The platform answered natural-language queries over large document datasets using RAG. Vector similarity alone proved insufficient for a domain-heavy corpus.
Why the system existed. Users needed reliable, explainable answers grounded in their own documents. Pure embedding retrieval surfaced semantically near but contextually wrong passages, and the LLM’s answers drifted because the same concept appeared under many surface forms across documents.
Why the problem was hard:
Constraints. Real-time retrieval latency budgets, multi-workspace isolation, compliance/traceability requirements, and the need to scale extraction across many document types without per-document hand-tuning.