Building Business-Ready Generative AI Systems

Supercharge your business with context-aware AI controllers, adaptive agents, multimodal reasoning functionality, neuroscientific memory systems, and flexible handler mechanisms that integrate the emerging generative AI models.

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Key Features

• Build an adaptive, context-aware AI controller with advanced memory strategies
• Enhance GenAISys with multi-domain, multimodal reasoning capabilities and Chain of Thought (CoT)
• Seamlessly integrate cutting-edge OpenAI and DeepSeek models as you see fit

Book Description

Standalone LLMs no longer deliver sufficient business value on their own. This guide moves beyond basic chatbots, showing you how to build agentic, ChatGPT-grade systems capable of sophisticated semantic and sentiment analysis, powered by context engineering.

You'll design AI controller architectures with multi-user memory retention to dynamically adapt your system to diverse user and system inputs. You'll architect a Retrieval-Augmented Generation system with Pinecone to combine instruction-driven scenarios. Through context engineering, you’ll minimize token usage, maximize response quality, and create systems that reason across complex tasks with precision. You'll enhance your system’s intelligence with multimodal capabilities—image generation, voice interactions, and machine-driven reasoning—leveraging Chain-of-Thought and context chaining to address cross-domain automation challenges. You'll also integrate OpenAI’s suite and DeepSeek-R1 without disrupting your existing GenAISys ecosystem.

With context engineering as the backbone, every step becomes a deliberate act of shaping model behavior. Your GenAISys will apply neuroscience-inspired insights to marketing strategies, predict human mobility, integrate smoothly into human workflows, and connect to live external data, all wrapped in a polished, investor-ready interface.

What you will learn

• Implement an AI controller with a conversation AI agent and orchestrator at its core
• Build contextual awareness with short-term, long-term, and cross-session memory
• Design cross-domain automation with multimodal reasoning, image generation, and voice features
• Expand a CoT agent by integrating consumer-memory understanding
• Integrate cutting-edge models of your choice without disrupting your existing GenAISys
• Connect to real-time external data while blocking security breaches

Who this book is for

This book is for AI and Machine Learning Engineers seeking to enhance their understanding of Generative AI and its enterprise applications. It will particularly benefit those interested in building AI agents, creating advanced orchestration systems, and leveraging AI for automation in marketing, production, and logistics. Software architects and enterprise developers looking to build scalable AI-driven systems will also find immense value in this guide. No prior superintelligence experience is necessary, but familiarity with AI concepts is recommended.

Table of Contents

1. Defining a Business-Ready Generative AI System
2. Building the Generative AI Controller
3. Integrating Dynamic RAG into the GenAISys
4. Building the AI Controller Orchestration Interface
5. Adding Multimodal, Multifunctional Reasoning with Chain of Thought
6. Reasoning E-Marketing AI Agents
7. Enhancing the GenAISys with DeepSeek
8. GenAISys for Trajectory Simulation and Prediction
9. Upgrading the GenAISys with Data Security and Moderation for Customer Service
10. Presenting Your Business-Ready Generative AI System

Denis Rothman graduated from Sorbonne University and Paris-Diderot University, designing one of the very first word2matrix patented embedding and patented AI conversational agents. He began his career authoring one of the first AI cognitive Natural Language Processing (NLP) chatbots applied as an automated language teacher for Moet et Chandon and other companies. He authored an AI resource optimizer for IBM and apparel producers. He then authored an Advanced Planning and Scheduling (APS) solution used worldwide.