Building Graph-RAG Pipelines: Real-World Integration, Continuous Updates, and Scalable Maintenance for Enterprise AI Systems

By Dwayne Daniel

Your data knows more than your models are saying. What if your AI could connect regulations to subsidiaries, guidelines to patients, and facts to sources-instantly and with full traceability? If you've ever asked, "How do we make AI both accurate and accountable?", this book is for you.

This practical guide shows architects, data engineers, MLOps teams, and AI leaders how to build Graph-RAG systems that marry knowledge graphs with retrieval-augmented generation. You'll learn how to design pipelines that integrate with legacy databases and APIs, keep knowledge fresh with continuous updates, and scale reliably in cloud environments. The payoff is enterprise AI that's explainable, compliant, and production-ready-so stakeholders trust the answers and you can show your work.

What sets this book apart?
It's an end-to-end playbook grounded in real enterprise constraints-security, governance, and cost.

Foundations of Graph-RAG: Core patterns that blend graph traversal with vector search for precise, evidence-backed responses.

Knowledge Graphs as the Backbone: Modeling entities, relationships, and provenance to make reasoning explicit.

Data Ingestion & Preparation: Multi-source pipelines, schema alignment, and cleaning strategies that hold up in production.

Core Pipeline Build: Orchestrating graph queries with embeddings and hybrid search that balances recall and precision.

Real-World Integration: Connecting to legacy systems, event streams, and middleware without disrupting operations.

Continuous Updates: Incremental indexing, CDC, and automation to keep answers current.

Scaling & Optimization: Load balancing, distributed architectures, cost control, and performance monitoring.

Explainability & Governance: Access control, auditability, and ethical safeguards that satisfy risk and compliance.

Maintenance & Lifecycle: Observability, self-healing, and technical-debt mitigation for long-term reliability.

Case Studies & Future Directions: Healthcare and finance examples, plus where graph-augmented enterprise AI is heading.

Build AI your executives, regulators, and users can trust. Add this book to your toolkit today and start delivering Graph-RAG pipelines that are accurate, explainable, and built for scale-across real enterprise data, real systems, and real constraints.

• Format: Paperback
• Language: English
• ISBN: B0FQ485VH3
• ISBN13: 9798264175169
• Release Date: September 2025
• Publisher: Independently Published
• Length: 128 Pages
• Weight: 0.52 lbs.
• Dimensions: 0.3" x 7.0" x 10.0"