Engineering AI Knowledge Systems - by Ivan Rodriguez (Paperback)

Book Synopsis

In most organizations, valuable knowledge is scattered across documents--manuals, emails, spreadsheets, and reports--often locked away in formats that are difficult to search, reuse, or scale. This book takes a refreshingly practical view of this challenge, showing how enterprise information can be transformed into structured, reliable systems that power intelligent assistants. Rather than treating AI as a standalone solution, the book positions it as one piece of a larger engineering puzzle.

The book walks the reader through a clear, disciplined approach to building enterprise knowledge platforms. Beginning with why organizations lose knowledge and struggle to find answers, it gradually builds toward a complete system architecture. The early chapters establish the importance of structured documentation, metadata, and retrieval as the foundation for accurate AI responses. From there, the focus shifts to implementation--covering ingestion pipelines, document normalization, chunking strategies, indexing, and hybrid retrieval models that combine traditional search with vector similarity. What makes this book particularly valuable is its emphasis on real-world engineering concerns. Readers learn how to design scalable and secure systems, manage conversational agents with memory and context, and handle governance, permissions, and operational complexity. Along the way, the author shares practical patterns, design principles, and lessons learned from building production systems--including what fails and why.

By the end of the book, readers will have a clear roadmap for turning fragmented enterprise data into a dependable AI knowledge platform. Grounded in decades of hands-on experience, this guide goes beyond theory to offer a realistic, engineering-driven path to building intelligent systems that truly work in practice.

What you will learn:

• Design enterprise AI systems using structured documentation, metadata, retrieval engineering, and hybrid search architectures.
• Convert enterprise documents into AI-ready knowledge platforms capable of powering intelligent assistants.
• Build practical RAG systems using indexing, chunking, vector similarity, and conversational AI workflows.
• Implement governance, security, and memory management to ensure reliable and controlled AI operations.
• Architect scalable, maintainable, and trustworthy enterprise AI platforms grounded in real-world practices.

Who this book is for:

This book is intended for software developers, systems architects, enterprise architects, AI engineers, database professionals, IT managers, knowledge-management professionals, and digital transformation leaders interested in building enterprise AI systems based on structured organizational knowledge.

From the Back Cover

In most organizations, valuable knowledge is scattered across documents--manuals, emails, spreadsheets, and reports--often locked away in formats that are difficult to search, reuse, or scale. This book takes a refreshingly practical view of this challenge, showing how enterprise information can be transformed into structured, reliable systems that power intelligent assistants. Rather than treating AI as a standalone solution, the book positions it as one piece of a larger engineering puzzle.

The book walks the reader through a clear, disciplined approach to building enterprise knowledge platforms. Beginning with why organizations lose knowledge and struggle to find answers, it gradually builds toward a complete system architecture. The early chapters establish the importance of structured documentation, metadata, and retrieval as the foundation for accurate AI responses. From there, the focus shifts to implementation--covering ingestion pipelines, document normalization, chunking strategies, indexing, and hybrid retrieval models that combine traditional search with vector similarity. What makes this book particularly valuable is its emphasis on real-world engineering concerns. Readers learn how to design scalable and secure systems, manage conversational agents with memory and context, and handle governance, permissions, and operational complexity. Along the way, the author shares practical patterns, design principles, and lessons learned from building production systems--including what fails and why.

By the end of the book, readers will have a clear roadmap for turning fragmented enterprise data into a dependable AI knowledge platform. Grounded in decades of hands-on experience, this guide goes beyond theory to offer a realistic, engineering-driven path to building intelligent systems that truly work in practice.

What you will learn:

Design enterprise AI architectures using structured retrieval engineering. Engineer enterprise retrieval beyond vector search. Transform enterprise documents into intelligent AI knowledge platforms. Build secure, scalable AI assistants with governance and conversational memory. Apply proven enterprise architecture and production design patterns.

About the Author

Ivan Rodriguez is a systems architect, software developer, and former government technology executive with more than fifty years of real-world experience designing, operating, and modernizing enterprise information systems. His background includes software development, database systems, telecommunications, emergency communications, infrastructure operations, and large-scale enterprise modernisation initiatives for organisations with more than 25,000 employees and technology budgets exceeding $100 million.

Throughout his career, he has worked across multiple generations of computing technologies, from IBM mainframe environments to modern enterprise AI platforms. His work has focused on building practical, scalable, and operationally reliable systems designed for real business environments.