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AI / ML

RAG Pipeline

Production
LangChainChromaDBPythonDockerTransformers
RAG Pipeline preview

Designed and deployed a RAG system inside an air-gapped corporate network with zero internet access. The system serves internal engineering teams, answering questions about manufacturing processes, documentation, and SOPs. Every component — the LLM, vector store, embedding model, and ingestion pipeline — runs entirely on local infrastructure with no cloud dependencies.

Fully air-gapped deployment with zero external dependencies

Semantic chunking with overlap for context-preserving retrieval

Custom evaluation framework using domain expert annotations

Organic adoption across engineering teams without top-down mandate

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Joey Schnepel — Phoenix, AZ — 2026