GraphRAG in Production: A Technical Deep Dive into Retrieval, Knowledge Graphs, and Enterprise AI Infrastructure

Retrieval internals, graph data modeling, extraction economics, community detection, query-time algorithms, permission-safe summarization, incremental indexing, and evaluation. With code.

This is a long one. It assumes you have built at least one RAG system, know roughly what an embedding is, and have run into the wall where adding more chunks to the context stops helping. The goal is to go past architecture diagrams and into the mechanics: why dense retrieval fails on certain query classes, how GraphRAG’s indexing and query algorithms actually work, where the costs come from, and what breaks when you put any of this behind an enterprise permission model.

The examples use Neo4j and Cypher because the vector index, full-text index, graph algorithms, and traversal live in one engine, which keeps the code short. The patterns port to other stacks.

Part 1: Why dense retrieval fails structurally

Full article with detailed steps and instruction available to my paid subscribers on substack: https://substack.com/@brianjamescurry

About the Author

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Brian Curry is a Kansas City-based machine learning and AI strategist, marketing economist, and applied researcher. He leads Vector1, an independent research studio working on cognitive AI, causal inference, marketing economics, and knowledge engineering, with a focus on simulation-driven intelligence systems.

Over more than twenty years, Brian has led data science and analytics initiatives at enterprises ranging from $4B to $125B in revenue, including Koch Industries, Tractor Supply, Vail Resorts, Hallmark, Garmin, and AT&T. His work applies economics and causal inference to connect marketing investment to structural outcomes, operational decisions to economic value, and AI adoption to durable competitive advantage.

He is also the founder of KC AI Lab, a 1,500-member AI and ML education community in the Midwest.

Research and Open-Source Projects

The modeling approach in this paper draws on PyCausalSim and Papilon.

Causal Inference and Economics

• PyCausalSim: Simulation-driven causal discovery and evaluation, using counterfactual scenarios to isolate structural drivers.
• Papilon: Complex-system simulation and optimization, with applications in media mix modeling and adaptive architectures.
• Daedalus: Causal analytics for money-flow mapping and macroeconomic tracking.
• The Autonomy Economy: A macroeconomic framework for the combined effects of rapid productivity growth and structural price deflation in an AI-automated economy.

Forecasting and Applied Modeling

• Regimecast: Time-series regime detection, structural breaks, and economic forecasting.
• CarryNet: Financial modeling for commodity carry trade analysis.
• Foodmesh-ml: Machine learning, optimization, and simulation for distributed food systems.

Cognitive AI and Semantic Architecture

• Memory-Node Encapsulation (MNE): A cognitive AI architecture and data structure for artificial episodic memory (NeoCortex-M).
• MeaningFlow: Semantic content modeling, embeddings, clustering, and coverage gap analysis.

Contact: brian@vector1.ai | GitHub @Bodhi8 | Substack

The text of this article may be shared with attribution under Creative Commons Attribution 4.0 International (CC BY 4.0). Code and models referenced here are available separately to paid subscribers.