Successful enterprises critically depend on information. Whether it's deciding to invest (through financial reports), engage in a business agreement (via contracts), maintain machinery (with an engineering manual), discover new opportunities (through research papers), or meet regulatory requirements (with a written policy) - information is the key. The problem is that while the information is largely "online," it's also often buried within tens, thousands or even hundreds of thousands of files both digital and scanned, which are hard to find and utilize optimally.
This is where Document Intelligence comes in.
The recent advances in Generative AI make it possible to design solutions that go significantly beyond simple document analysis by allowing you to extract relevant information, analyze context, identify various structured data, and respond to queries in natural language. By combining the power of Python and Generative AI, it becomes possible to turn vast amounts of unstructured data into useful information that facilitates faster and better decisions.
This book takes a practical, hands-on approach to building such systems.
Apart from artificial intelligence theory, this book walks you through building a full document intelligence solution with Gemini and Python. It starts with digital PDFs, then moves step by step into scanned documents, table extraction, metadata organisation, finding useful information with Retrieval-Augmented Generation (RAG), writing better prompts, and creating structured outputs that fit databases and business tools. Each chapter builds on the one before it. You do not only learn what each part does by itself, you also see how everything connects into one document intelligence system. That matters, honestly, because the whole flow is what makes it useful.
The techniques presented in this book can be applied across many domains. Whether you are analysing annual reports, processing engineering documents, reviewing contracts, organising research papers or developing enterprise knowledge systems, the same principles remain applicable.
By the end of the book you will have built a set of tools that will unlock the information in a document, and that will make this information accessible, searchable, and available for decision making purposes.
What You Will Learn
After completing this book, you will be able to:
• Build complete document intelligence systems using Gemini and Python.
• Extract text from both digital and scanned PDF documents.
• Detect and extract tables from complex documents.
• Design prompts that produce accurate and structured AI responses.
• Generate reliable JSON outputs for downstream applications.
• Organise documents using metadata to improve retrieval.
• Build Retrieval-Augmented Generation (RAG) pipelines for document search.
• Validate, verify and store extracted information.
• Integrate Generative AI into practical business workflows.
• Extend the framework to support additional document types and AI models.
Who This Book Is For
This book is intended for all software developers, Python developers, data professionals, business analysts, educators, and students who wish to build usable document intelligence applications using Generative AI. We assume some programming experience with Python. Previous knowledge of document intelligence or Generative AI are not required.
This is essential to understanding the concepts, but readers who already know a lot about document intelligence will also find material that provides new insights.
This book proceeds from small experiments with Generative AI to building complete systems in order to reveal the mechanisms of different tools, along with their combination for information extraction from any of your documents.