Architecting a Custom GenAI Pipeline for Enterprise Data
How we built a secure, privacy-first RAG system for a healthcare data company to unlock $5M in new revenue.
01The Challenge
A healthcare data company wanted to leverage Large Language Models (LLMs) to allow clients to query patient records naturally. However, they faced strict HIPAA compliance requirements. They couldn't send sensitive data to public APIs like OpenAI, and they needed to guarantee zero hallucinations when retrieving medical facts.
- Strict HIPAA Compliance
- Zero Tolerance for Hallucinations
02The Strategy
We designed a "Local-First" RAG (Retrieval-Augmented Generation) architecture. Instead of relying on external model knowledge, we built a system that retrieves relevant, verified documents from a secure vector database and uses the LLM only for summarization and formatting, ensuring data never leaves their secure VPC.
Key Interventions
Secure Vector Database
Implemented Pinecone with strict access controls to index millions of medical records.
RAG Pipeline
Built a Python-based pipeline using LangChain to orchestrate retrieval and generation.
Evaluation Framework
Created an automated testing suite (Ragas) to measure answer accuracy and faithfulness.
03The Results
The system enabled secure, natural language querying of patient records, a feature that competitors couldn't match. This innovation opened a new enterprise revenue stream worth $5M annually and positioned the company as a leader in AI-driven healthcare analytics.