Search engines and language models don’t rank strings — they work with entities: your organization, your founders, your products, and how they connect. When those aren’t defined clearly and consistently, Google’s Knowledge Graph and AI assistants guess, and they guess wrong. AI Search & LLMs: Entity SEO and Knowledge Graph Strategies for Brands shows you how to audit, build and connect your brand’s entities into a knowledge graph — and turn it into citations in ChatGPT, Gemini and Perplexity.
What’s inside
Twenty-nine chapters in seven parts, each ending with an exercise worked through for one fictional brand, Fernhill Outdoor.
- Entity foundations (primer by Lazarina Stoy) — what entities are, the EAV model, and extracting entities with Google’s Natural Language API vs. generative AI.
- Entity-based search in the LLM era — interrogating Google’s Knowledge Graph, auditing entity structure, entities in query fan-out, and a full case study.
- Brand entity development — the BRIDGE framework: entity catalogue, relationship mapping, multi-entity schema with
@id, distribution and measurement. - Knowledge graph primer (by Lazarina Stoy) — how knowledge graphs and Google’s Knowledge Graph work, and the Knowledge Graph Search API in practice.
- Knowledge graph integration — building, implementing, deploying and maintaining your own brand knowledge graph.
- Brand authority for LLMs — shaping how LLMs describe you, turning your graph into citations, cross-platform distribution, co-citation and citation monitoring.
- Masterclasses — companion chapters to Beatrice’s three live sessions, which come with the book on video.
Eleven companion resources — Beatrice’s Schema Entity Audit Template, Entity Relationship Planning Workbook, Knowledge Graph Foundation template, Implementation Guide and LLM Citation Tracking guide, plus Lazarina’s entity-extraction templates and notebooks — and the three masterclass videos: a live entity audit, building enterprise article schema, and the workbook & prioritization walk-through. You’ll be enrolled in a Resource Library in your MLforSEO Academy account with direct links to all of them.
Built for reference
- Slide diagrams recreated for print, plus JSON-LD examples wiring an Organization, its founder and its products together with
@id. - A master checklist of all 29 exercises, a tools list, a 41-term glossary and sources in the appendices.
- Fact-checked in October 2026 —
resultScore, knowledge panels, Wikidata notability, schema and AI features — with dated update notes.
Who it’s for
SEOs, technical SEOs and brand marketers who want their organization, people and products understood correctly by Google and by AI assistants — and who want a system they can run and maintain, not one-off schema fixes. The primers make it accessible if entities and knowledge graphs are new to you.
Beatrice has worked at WordLift since 2016 on semantic SEO, structured data, knowledge graphs and AI search, helping Fortune 500 companies, major editorial groups and large e-commerce platforms structure their information so search engines and AI systems can understand, trust and surface it.
Founder of MLforSEO; marketing and machine learning consultant, trainer and speaker. She wrote the primers on entity extraction and knowledge graphs. Lazarina’s profile →
Format: PDF digital download (instant access). Adapted from the MLforSEO Academy course with the help of generative AI; the frameworks, arguments and research are Beatrice Gamba’s, and the primers Lazarina Stoy’s. © 2026 ML Marketing Consulting, Ltd. (MLforSEO & MLforSEO Academy) and Beatrice Gamba.
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