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New course by Beatrice Gamba: AI Search & LLMs: Entity SEO and Knowledge Graph Strategies for Brands now live -> Start learning today ✨
AI Search & LLMs: Entity SEO and Knowledge Graph Strategies for Brands
Course Creator:
Guest Instructor:
In this course, Beatrice Gamba, Head of Innovation at Wordlift, will guide you through how entity-based search redefines the way brands are understood in the LLM era.
What truly matters is meaning: the ability to clearly communicate who you are, what you stand for, and how your brand connects to the larger knowledge ecosystem. Machines and people alike respond to consistency and clarity, and ensuring your values are digitally represented with precision is essential to being visible and trusted by the machines.
sneak peak of what you’ll learn
What you’ll take away
We’ll explore how to translate the values of your brand into structured, data-driven signals that search engines and AI systems can interpret.
From strengthening entity recognition to weaving your presence into knowledge graphs, this course is about making your brand not just discoverable, but meaningful in a digital environment where authority is earned through accuracy, trust, and relevance.
Entity Meaning in the LLM Era
Why meaning is central to entity-based search in the age of LLMs.
Brand Representation and Brand Entity Development
How to represent your brand’s values consistently in digital form.
Reinforcing entity value
Practical semantic SEO methods for reinforcing entity recognition.
Knowledge graph integration
How to integrate with knowledge graphs to enhance trust and visibility.
Multi-platform brand entity optimisation
Concrete steps to build lasting brand authority in a data-driven ecosystem.
Practical work at scale
Working with the Google NLP and Knowledge Graph APIs
COURSE AUTHOR & INSTRUCTOR
Beatrice Gamba
Beatrice Gamba is an expert in semantic technologies and the future of search. She specializes in helping businesses navigate the transition from traditional SEO to agent-driven discovery, combining technical expertise with practical implementation strategies.
Beatrice leads the development of knowledge graph solutions that make content accessible to intelligent agents and large language models. Her work focuses on the intersection of SEO, semantic web technologies, and digital transformation, enabling businesses to build sustainable competitive advantages in such a dynamic industry as Search has become.
A recognized thought leader in the semantic SEO space, Beatrice is a frequent speaker at industry conferences including The Knowledge Graph Conference in New York and Connected Data London, where she shares insights on how knowledge graphs and intelligent agents are reshaping content discovery. Her expertise spans entity-based optimization, structured data implementation, and automated SEO workflows.
With a background spanning Fortune 500 companies across various industries, Beatrice has helped organizations leverage cutting-edge semantic technologies to drive organic growth and enhance digital visibility. She is passionate about making advanced technologies practical and accessible, bridging the gap between innovation and real-world business application.
Beatrice’s approach combines strategic thinking with hands-on technical implementation, helping digital leaders prepare for a future where search and content discovery are increasingly dialogical, personalized and agent-mediated. Her work at the forefront of agentic search positioning makes her uniquely qualified to guide businesses through this critical transformation.
Beatrice currently serves as Head of Innovation at WordLift.
COURSE GUEST CONTRIBUTOR
Lazarina Stoy.
Lazarina Stoy is a recognized expert in the intersection of SEO, data science, and machine learning. With a background in digital marketing and technology, Lazarina has worked with top-tier enterprise-level companies like AWS, Skyscanner, and Extreme Networks, leveraging machine learning to enhance SEO strategies, implement process automation, and drive organic growth. She is known for her ability to simplify complex technical concepts, making them accessible and exciting for both beginners and professionals.
Lazarina has led numerous projects implementing machine learning for SEO tasks, before turning her passion for technology into the training platform MLforSEO, where she helps organic search marketers get onboarded into the world of AI. Additionally, she has developed training resources, contributed to major industry publications like Search Engine Land and Moz, and spoken at leading digital marketing conferences worldwide.
Let’s get started
Introduction & Overview
This course is designed as a practical, systems-focused journey from understanding entities to building and optimizing knowledge graphs and finally engineering brand authority for AI search and LLMs.You’ll move through a set of core modules, each broken down into short, focused lessons with concrete examples, mini case studies, and tool-driven exercises (e.g. interrogating Google’s Knowledge Graph, doing entity audits, and working with the Knowledge Graph API).Two optional knowledge primers sit alongside the main pathway – one on entities/NER and one on knowledge graphs – so you can quickly catch up or deepen specific technical foundations without slowing down the strategic narrative of the core course. Explore the course structure and what you’ll learn.
Lessons
How this course is structured What you'll learnIntroduction to Entities and Entity Extraction (Knowledge Primer)
In this module, Lazarina will give you a knowledge primer on Named Entity Recognition and Entity extraction, as well as an explainer of crucial concepts like entities (and how they differ from keywords) and EAV Model. There are also two practical lessons in this module on doing entity extraction with the Google NLP API, as well as a comparative analysis of different NLP APIs versus generative AI on the task of extracting entities. Although this module is not required for the completion for the course, is is highly recommended for those students, that need a detailed introduction to Entities, NER (Entity Extraction), Google’s NLP API, and get introduced to other APIs like Amazon Comprehend or NLP Watson NLU.
Lessons
What is Named Entity Recognition (Entity Extraction) in Semantic Analysis? Introduction to Entities and the EAV Model Working with the Google NLP API (Practical) Bonus Lesson – Comparative Analysis of NLP APIs versus generative AI on entity extraction PreviewUnderstanding Entity-Based Search in the LLM Era
We will cover five critical components, we’ll define what entities actually are and why they differ fundamentally from keywords, how Google systems, process entities, looking through the lens of their knowledge graph architecture. We’ll explore the NLP features that serve as training inputs for LLMS. We’ll analyze how LLMS process entities through their unique pipeline architecture. And finally, we work through a comprehensive case study showing an entity-based approach in action with specific implementation strategies that you can apply to your own work.
Lessons
Module 1 Introduction Entity Importance to Traditional and AI Search – Brief Introduction to Entities and the Knowledge Graph Interrogating Google's Knowledge Graph (Manual Practice Example) Entity Audit Toolkit How to Audit Entity Structure and What are Appropriate Correction Strategies Entities in Query-Fan Out Systems, AI Search Systems and LLMs Entity Website Analysis – Practice Walk Through based on fictional Case Study Module Outro – Key Takeaways and ResourcesBrand Entity Development and Recognition
In this module, we learn how to build a comprehensive entity framework that makes your brand discoverable and authoritative in AI powered search. We’ll cover six critical areas: how to discover entity recognition gaps that prevent LLM discovery; the bridge framework systematic approach to entity development; entity architecture design principles; advanced schema markup for multi-entity relationships; content strategies that amplify entity recognition across platforms; and finally, how to leverage AI agents for automated entity management and optimization.
Lessons
Module 2 Introduction How to discover entity recognition gaps that prevent LLM discovery – BRIDGE Framework Entity Blueprint Analysis – Building your entity catalogue Entity Relationship Mapping Advanced Schema Markup for multi-entity relationships Content distribution strategies that amplify entity recognition across platforms How to grow your entity network by leveraging AI agents Entity Evaluation and Measurement Module Outro – Common pitfalls to avoid, Key Takeaways and ResourcesIntroduction to Knowledge Graphs (Knowledge Primer)
In this module, Lazarina discusses Knowledge Graphs – how they work, their key elements, as well as goes into depth about how Google’s Knowledge Graph works, with detailed patent analysis, covering some questions like how to add entities to the knowledge graph, how Google adds facts about entities to the knowledge graph, and so on. We also have a practical lesson, working with the Google’s Knowledge Graph API, as an introduction to analysis at scale. This module is not required for the completion for the course, but is a primer for those students, that need a detailed introduction to Knowledge Graphs and the Knowledge Graph API.
Lessons
Introduction to Knowledge Graphs and Key Elements How Google's Knowledge Graph works Working with the Knowledge Graph API (Practical) Module Outro – Key TakeawaysKnowledge Graph Integration and Optimization
Lessons
Module 3 Introduction Knowledge Graph Fundamentans Building Your Knowledge Graph How to Implement your Knowledge Graph – Approaches and Considerations How to Deploy the Semantic Infrastructure on your Website Multi-platform Knowledge Graph Alignment How to maintain your Knowledge Graph Module Outro – Key Takeaways and ResourcesBuilding Brand Authority for LLM Success
In this module, we’ll cover four critical areas. First, we’ll look at competitive positioning, how to engineer your brand’s, position in LLMs responses related to your competitors. Cross-platform amplification- how to leverage forums, Reddit postcard podcast, alternative channels for LLM training data. Ai agent orchestration, how to deploy autonomous agents for acquiring citations, monitoring and optimizing.Reputation engineering -how to control the narrative, how to manage the negative association, optimizing sentiment in LLM responses. And these four pillars work together to create a comprehensive authority building system that scales.
Lessons
Module 4 Introduction and the Key Mistake Teams Make Competitive LLM Intelligence How to turn your Knowledge Graph into LLM Citations Cross-platform entity distribution What is Co-citation in the context of LLM Reputation Management and How to Approach it Strategically Monitoring and Maintenance Module Outro – Key Takeaways and ResourcesCongratulations
You’re done! Find out what’s next, take an optional quiz, or share your project and success with the MLforSEO Community!