AI / RAGInternal build
Medical Knowledge Assistant
This assistant turns a corpus of medical literature and clinical guidelines into a conversational knowledge base. Healthcare professionals retrieve drug information, diagnostic criteria, and treatment protocols by simply asking — with a local-first stack that keeps sensitive data in-house.
RAGLLaMA 3FAISSFastAPI
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The Challenge
- Clinicians needed fast answers from vast, dense medical literature during time-critical work.
- Patient and clinical data privacy meant sensitive content couldn't leave controlled infrastructure.
- Answers had to be grounded in trusted guidelines, not a model's general training.
- Retrieval needed to be fast and accurate across a large document corpus.
Our Solution
- Built a RAG system using LLaMA 3 so inference can run privately, on-premises if required.
- Indexed the medical corpus with FAISS for fast, high-quality similarity search.
- Grounded every conversational answer in retrieved guideline passages.
- Served the assistant through a FastAPI backend for clean integration.
Key Features
Conversational Retrieval
Ask for drug info, diagnostic criteria, or protocols in natural language.
Private by Design
LLaMA 3 enables local inference so sensitive data stays in-house.
Fast Vector Search
FAISS delivers rapid, accurate retrieval over large medical corpora.
Guideline-Grounded
Answers anchored in trusted clinical literature, not guesswork.
What It Does
Private
On-prem capable inference
Fast
FAISS-powered retrieval
Grounded
Answers from real guidelines