Please log in to access your purchased courses.

AI Search & LLMs: Entity SEO and Knowledge Graph Strategies for Brands

Q: What is this course about, and what will I be able to do after it?

Learn entity-based search and brand knowledge graphs for modern discovery across Google, AI Overviews, and LLM answer engines. You will model key entities, implement structured data, and ship experiments that improve AI search visibility.

Q: Who teaches the course?

Course creator: Beatrice Gamba (Head of Innovation at WordLift). Guest instructor: Lazarina Stoy.

Q: How do LLMs change search and brand visibility?

LLMs ground answers on entities, not only keywords. Brands that publish consistent, linked entity data are easier for LLMs to retrieve and summarize.

Q: What is a brand knowledge graph and why does it matter?

A knowledge graph connects your core entities and relationships so search engines and LLMs can resolve what your pages are about and attribute facts to your brand.

Q: Which schemas and identifiers should I use?

Use practical schema.org patterns for brands, products, and identity with persistent IDs and sameAs links, mapped to page templates and locales.

Q: What AI workflows will I build?

Entity extraction and reconciliation, schema implementation and validation, and RAG-style grounding concepts for brand-safe answers.

Q: How do I measure impact?

Track entity coverage, identity consistency, rich result eligibility, answer-engine visibility, and assisted conversions.

Q: Do I need developer skills or a data-science background?

No advanced background required—implementation is taught with examples, templates, and checklists for brand, content, and SEO teams.

Course Creator:

Beatrice Gamba

Guest Instructor:

Lazarina Stoy.

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.

Curriculum

What’s Included

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 learn

Introduction 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 Preview

Understanding 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 Resources

Brand 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 Practical: How to use the Schema Entity Audit Template Practical: How to use the Entity Relationship Planning Workbook Module Outro – Common pitfalls to avoid, Key Takeaways and Resources

Introduction 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 Takeaways

Knowledge 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 Resources Practical: How to use the Knowledge Graph Foundation Workbook

Building 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 LLM Citations Monitoring and Maintenance Module Outro – Key Takeaways and Resources

Applied Brand Entity SEO Masterclass: From Audit to Execution

Move beyond theory and step into the workflow of Beatrice Gamba, implementing the core concepts of this course to the auditing process of a live website. In this immersive, three-part masterclass, you will witness the end-to-end application of Brand Entity SEO and Knowledge Graph strategies on a live, existing website.Designed to bridge the gap between abstract concepts and real-world execution, this module provides an “over-the-shoulder” view of the actual tools and workflows used to analyze, plan, and optimize for AI search. You will learn how to translate raw data into a strategic roadmap and watch the immediate implementation of content and schema tactics that drive knowledge graph understanding. (1) In lesson 1, watch a real-time deep dive into an existing website using advanced entity analysis tools. This session identifies exactly what the site is doing right and where it is failing in terms of entity recognition, semantic relationships, and schema markup, setting the stage for strategic improvement. (2) In lesson 2, see how it all comes together with a complete optimization lifecycle. We take one specific piece of content from the audited site and optimize it live—covering everything from content enhancement and semantic enrichment to internal linking and technical schema implementation. (3) In lesson 3, you put this into practice with the provided templates and prioritisation framework.

Lessons

Live Entity Audit: Diagnosing Gaps and Opportunities Live Building of Structured data with Entity Relationship Mapping Putting it all into practice: Workbooks and prioritisation framework walk-through

Congratulations

You’re done! Find out what’s next, take an optional quiz, or share your project and success with the MLforSEO Community!

Lessons

Quiz Preparation (Core Insights Recap) – Flashcards (Optional) Final Exam (Optional) Your certificate and what's next

testimonials from fellow marketers, who completed the course

What people are saying

AI Search & LLMs: Entity SEO and Knowledge Graph Strategies for Brands – FAQs

1) What is this course about, and what will I be able to do after it?

This course shows how entity-based search and brand knowledge graphs power modern discovery across Google, AI Overviews, and LLM-driven answer engines. You’ll learn how to model your brand’s entities, implement structured data, and make your content machine-readable so LLMs can retrieve and generate accurate answers about you.

By the end, you’ll be able to design an entity-first SEO strategy, stand up a practical knowledge graph, and ship experiments that improve visibility in AI search experiences.

2) Who teaches the course?

Course creator: Beatrice Gamba (Head of Innovation at WordLift). Guest instructor: Lazarina Stoy. Sessions combine strategy and hands-on workflows tailored for brand teams.

3) How do LLMs change search and brand visibility?

LLMs synthesize answers by grounding on entities, not just keywords. Brands that publish consistent, linked, and verifiable entity data are easier for LLMs to retrieve and summarize. You’ll learn how to structure your brand’s knowledge so AI Overviews and assistants can surface you with confidence.

4) What is a brand knowledge graph and why does it matter?

A knowledge graph is a network of your most important entities (brand, products, people, places, offers) and their relationships. It aligns content, metadata, and identifiers so search engines and LLMs can resolve who/what your pages are about and confidently attribute facts to you.

5) Which schemas and identifiers should I use?

We cover practical schema.org patterns for brands and products, organization/author identity, and how to use persistent IDs and sameAs links. You’ll map site sections to entity types and learn QA checks that prevent drift across locales.

6) What AI workflows will I build in the course?

You’ll practice:

Entity extraction & reconciliation from your content and sources.
Schema implementation and validation for high-impact templates.
RAG-style grounding concepts for brand-safe answers and summaries.

7) How do I measure impact from entity SEO and knowledge graphs?

Track entity coverage, brand/identity consistency, rich result eligibility, non-brand discovery, answer-engine visibility, and assisted conversions. We show a pragmatic roadmap for testing hypotheses and attributing wins to graph-backed changes.

8) Do I need developer skills or a data-science background?

No advanced background is required. We keep implementation practical with clear examples, templates, and checklists that brand, content, and SEO teams can adopt quickly.

Who this course is for

For brand, SEO, and content teams who want to operationalize entity-first strategy and build a practical knowledge graph that improves AI search visibility.

Brand & SEO Leads

Define a brand graph and entity taxonomy aligned to business goals.
Prioritize schemas and page types that influence AI answers.

Content & Editorial

Make content machine-readable with consistent entities and IDs.
Plan formats LLMs can retrieve, ground, and summarize.

Data & Analytics

Measure entity coverage, answer-engine visibility, and assisted impact.
Build QA checks that keep the graph accurate across locales.

Product & Knowledge Ops

Operationalize identifiers, sameAs links, and governance.
Connect internal sources so LLMs can verify brand facts.

Agencies & Consultants

Package entity-first roadmaps and schema rollouts for clients.
Differentiate with graph-backed deliverables and reporting.

Course Purchase FAQs

Can you issue an invoice?

Yes. Complete checkout and enter billing details (name, company, address, and—if applicable—VAT/Tax ID). An invoice/receipt is issued automatically and emailed to you. If anything needs correcting, reply to the order email and we’ll re-issue.

What payment methods do you accept?

Major debit/credit cards are supported, with additional local options shown at checkout depending on your location. For company POs or bank transfers for multiple seats, reply to your order email for assistance.

How soon do I get access after purchasing?

Access is provisioned as soon as payment completes. You’ll receive a confirmation email and can log into your Academy dashboard to start right away.

How long do I have access to the course?

The course is on-demand and self-paced—no time limits for completion.

Do you charge VAT or sales tax?

Taxes are calculated based on your billing country and shown at checkout. If you have a valid VAT/Tax ID (where applicable), enter it during checkout for the correct treatment.

Can I buy multiple seats or a team license?

Yes. For teams, we can organise bulk checkout and centralised invoicing. Reply to your order email with the number of seats you need and we’ll set it up.

Can you update my invoice details after purchase?

Yes. If you need a correction (e.g., company name, address, VAT number), reply to the invoice or order email with the exact details and we’ll re-issue the document.

Do you provide a certificate of completion?

MLforSEO Academy provides a certificate of completion for each course you finish.

Can I change the seat to a different person after purchase?

If you purchased with the wrong email or need to reassign a seat, reply to your order email. We can move access to the correct learner’s account.

Can I expense the course to my company?

Absolutely—use your company billing details at checkout to receive a formal invoice. If your finance team needs specific wording or a PO reference, include it in the “Company” or “Notes” field.