Agentic Knowledge Assistant & Automated Document RAG
Building a secure internal AI search and reasoning agent over thousands of technical documentation files and compliance standards.
Deterministic Multi-Stage Retrieval & Citation Flow
Engineering and legal personnel spent hours every week manually sifting through multi-thousand-page technical compliance PDFs and internal wikis to find critical specifications.
Implemented a hybrid vector search pipeline with contextual document chunking, semantic re-ranking, and dynamic citation overlays ensuring every AI-generated response is backed by exact source references.
Staff inquiry resolution times dropped from 45 minutes to under 30 seconds, with an audited hallucination rate below 0.5% and zero confidential data leakage.
Technical Architecture
To eliminate hallucinations in high-stakes technical domains, standard naive vector search was replaced with a multi-stage retrieval pipeline:
- Semantic Chunking: Documents were parsed preserving headers, markdown tables, and code snippets rather than arbitrary token boundaries.
- Hybrid Retrieval: Combined dense vector embeddings with BM25 sparse keyword search to guarantee exact code/model number matches.
- Cross-Encoder Re-ranking: Top candidate chunks were scored for relevance before passing strictly grounded context to the language model.
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