Cognitive RAG: High-Precision Enterprise Document AI
Vector-hybrid retrieval engine with semantic chunking and automated citation verification.
System Architecture & Overview
A production-grade Retrieval-Augmented Generation system designed for complex technical manuals, contracts, and legal corpora. Implements reciprocal rank fusion (BM25 + Dense embeddings) and citation backtracking to eliminate false claims.
The Challenge / Problem
Standard RAG setups fail when dealing with tables, dense clauses, and domain-specific terminology, resulting in distorted answers.
Engineering Solution
Built a hybrid indexing pipeline with semantic markdown chunking, cross-encoder reranking, and dynamic threshold confidence filtering.
Technical Hardening & Challenges
Parsing embedded PDF tables cleanly, balancing dense semantic vs keyword lexical recall, and maintaining sub-150ms retrieval latencies.
Commercial & Production Result
96.7% retrieval precision, 100% citation verification rate, and 120ms average query latency.
Pipeline & Architecture Flow
Document Parser -> Chunking & Metadata Injection -> Vector Store + BM25 -> Reranker -> Gemini Pro -> Verified Output.
Applied AI & Engineering Capabilities
System Interface & Analytics
