The Challenge
MedTech Innovations struggled with siloed knowledge across R&D, regulatory, and manufacturing teams. Traditional search could not capture the complex relationships between research papers, regulatory filings, and product specifications.
Our Solution
We implemented a Graph RAG system that combines knowledge graphs with retrieval-augmented generation, creating a relational map of all organizational knowledge.
Architecture Highlights
- Neo4j knowledge graph modeling entity relationships
- Automated entity extraction using fine-tuned NER models
- Hybrid retrieval combining graph traversal + vector similarity
- Multi-hop reasoning for complex query answering
- Interactive visualization dashboard for knowledge exploration
Results
The Graph RAG system unlocked previously invisible connections in the knowledge base:
- 60% faster regulatory compliance checks
- 40% reduction in duplicated research efforts
- 95% recall rate for related documents across departments
- 78% of users reported finding information they never knew existed
Technologies Used
Python, Neo4j, LangChain, OpenAI, spaCy, React, D3.js, GraphQL, AWS