GRAPH RAG
Entity-aware retrieval for complex documents
When standard RAG isn't enough — LightRAG and Neo4j-powered graph retrieval for documents with complex cross-references, entity relationships, and hierarchical structure. Built for legal, insurance, and medical document corpora.
Key Features
LightRAG entity extraction and graph construction
Neo4j graph database integration
Cross-reference traversal for complex queries
Hybrid graph + vector retrieval
How We Work Together
A proven methodology that delivers results
Discovery
We start with understanding your business, challenges, and goals through workshops and interviews.
Design
Together we design the solution architecture and create a detailed implementation plan.
Deliver
Iterative implementation with regular demos and feedback loops to ensure alignment.
Support
Post-launch support, knowledge transfer, and ongoing optimization recommendations.
Use Cases
- Complex multi-document corpora with cross-references
- Document sets with extensive cross-references
- Entity relationship extraction and traversal
- Legal, insurance, and medical document retrieval
Ideal For
- Hierarchical or heavily cross-referenced documents
- Insurance, legal, or medical organizations
- Teams needing knowledge graph capabilities
Not Ideal For
- Simple, flat document collections
- Documents without entity relationships
- Low-complexity retrieval requirements
Related Case Studies
RAG Document Processing System
At Insly, I led development of a RAG (Retrieval-Augmented Generation) system that gives insurance brokers fast, context-aware answers about policy details. The system combines traditional search with vector embeddings to handle complex queries across 23 different insurance providers.
Challenge
Insurance brokers needed to quickly find relevant information across thousands of policy documents from 23 different insurers, each with unique formats and terminology.
Microservices Migration
CloudAcademy needed to migrate their content authorization service from Kotlin to Go as part of a broader standardization effort. I led this migration while ensuring zero downtime and creating new microservices following DDD patterns.
Challenge
Legacy Kotlin service had performance bottlenecks and was difficult to maintain. Team needed to standardize on Go for better consistency across microservices.
Ready to Transform Your Business?
Let's discuss how I can help you achieve your goals. The first consultation is free.