Semantic Knowledge Engineering
Enterprise RAG & Knowledge Systems
Unlock the proprietary knowledge buried across your company's PDFs, Notion wikis, SQL databases, and customer records. We architect advanced Retrieval-Augmented Generation (RAG) systems that enable accurate, citation-backed AI search and reasoning.
Technical Depth
Advanced RAG Architecture Pipeline
01
Semantic Chunking
Hierarchical chunking preserving tables, headers, and code snippets rather than naive character splitting.
02
Hybrid Search & Vectors
Combining dense vector embeddings with BM25 keyword search across pgvector or Pinecone for maximum recall.
03
Cross-Encoder Re-Ranking
Re-ranking retrieved passages with Cohere or local cross-encoders to ensure only top relevant context reaches the LLM.
04
Grounded Generation
Synthesis of the final answer with clickable source document citations and exact line number references.
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