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Generative AI & Architecture

On-Premise RAG Architecture & Automated Clinical Document Analysis

Enterprise Generative AI Project
LangChainn8nPostgreSQLC# / .NET CorePython

Problem statement

Hospitals sit on large volumes of Electronic Medical Records (EMR) and billing documents, but the highly confidential nature of that data makes it impossible to send to public cloud AI services. They needed automated summarization and billing analysis that never exposes patient information to external networks.

My contribution

Secure RAG Pipeline

Engineered a Retrieval-Augmented Generation pipeline with LangChain that retrieves and reasons over clinical documents entirely within the hospital's own infrastructure.

Workflow Automation

Used n8n to orchestrate the ingestion, processing, and routing of documents, connecting the AI layer to the existing C# / .NET Core management system.

Data Layer

Implemented a PostgreSQL-backed store for document context and retrieval, tuned for on-premise performance.

Technical stack

LangChainn8nPostgreSQLC# / .NET CorePythonRAG

Results & impact

  • Enabled 100% secure, fully on-premise processing of confidential hospital documents.
  • Automated EMR summarization and billing analysis that previously required manual review.
  • Integrated cleanly into the hospital's existing corporate management system.

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