What do these categories mean?
1) AI Search & Generative SERP Optimization
Retrieval tuning, simulating LLM answers/attribution, entity/topic coverage, and adding the evidence LLMs need to cite you.
2) Technical SEO Automation
Log/crawl anomaly detection, internal-link graphs, predictive crawl budget, duplicate/canonical classifiers & CI/CD autofixes.
3) Entity & Programmatic SEO
Knowledge graphs & entity resolution, automated schema.org, ML mapping of product/category feeds & attribute extraction.
4) AI Content Engineering
RAG pipelines, fact-checking & safety, brand/style classifiers, content quality scoring to prioritise edits.
5) Conversational Experiences & On-Site Assistants
Grounded chat/agents, query rewriting & guardrails, evaluation harnesses tied to funnel impact.
6) Personalization, CRO & Recommendations
Embeddings for recs & next-best-action, uplift/bandits, ML-generated copy/layout variants with guardrails.
7) Creative & Media Optimization
GenAI assets with brand safety, fatigue detection, message clustering, and budget allocation from response curves.
8) Measurement, Experimentation & Attribution
MMM/MTA, incrementality & geo-experiments, causal inference for SEO changes, forecasting under volatility.
9) Data Platform & MLOps
Warehousing/feature stores & vector DBs, eval/monitoring for drift & cost, lineage/quality checks for marketing data.
Produces AEO/AI visibility analyses and community resources translating AI search shifts into action.
Publishes practical AI search playbooks, from AI Overviews risk assessment to inclusion/impact tracking, that help teams adapt SEO to generative results.
Leads adoption of knowledge graphs and AI-powered SEO that surfaces brands in generative search.
Leads WordLift innovation on topical maps, knowledge graphs, and AI discovery strategies.
Long-time educator on machine learning for SEO (entities, logs), and practical/ethical GenAI for marketers; teaches an accelarator program in AI for marketers
Documents concrete LLM-driven workflows for content strategy and SEO ops.
Writes and contributes practical pieces on GEO/AEO and AI-assisted content systems for SEO practitioners.
Builds and open-sources practical Python/Streamlit apps that apply NLP/LLMs to real SEO problems.
Publishes deep, hands-on research about embeddings, RAG and AI-first SEO architecture.
Shares enterprise GEO/SEO tactics and studies on AI Overviews inclusion with actionable frameworks
Trains content teams in successful implementation of AI systems, and develops AI tools for content marketers.
Builds and shares practical AI workflows that blend content strategy, SEO, and automation to shape the future of marketing.
Publishes technical analyses of Google’s AI Mode (snippet selection, indexing behavior, grounding data) and builds measurement frameworks/tools for tracking brand visibility in LLMs; long track record of SEO and ML experimentation and tool building
Clarifies concepts like AgenticSEO and advises enterprises on AI-era technical and governance shifts.
Builds AI-driven tools for GEO/SEO and shares lessons from automating technical workflows.
Bridges IR research and SEO to explain AI Overviews/AI Mode and how to adapt technical strategy to generative IR.
Champions how agentic AI will reshape SEO roles and measurement, offering executive-level guidance.
Builds the tools platform advertools and shows how to use Python/LLMs to scale SEO/SEM analysis with reproducible workflows.
Merges deep AI/ML expertise with practical marketing strategy to build data-driven, entity/graph-powered approaches that transform how businesses tell their stories and grow.
Advocate for the responsible use of LLMs with hands-on experience applying AI across full-stack marketing and user research; an active speaker and contributor to industry publications.
Teaches AI search Optimization and brings rigorous growth modeling to evolving AI-driven discovery.
Offers sober, operator-level guidance on AI strategy, risk and measurement for leaders navigating LLM adoption.
Designs strategic frameworks for AI Search (AI Overviews/AI Mode, agentic search, multimodal outputs) and uses knowledge graphs and search intelligence to align content, entities, and measurement with LLM-driven discovery and ecommerce experiences.
Consistently ships Python tutorials and automation to operationalize SEO data, ML and API workflows.
Provides no marketing fluff, but scientific research and testing backed up content for the community.
Publishes prolific Python/ML tutorials that automate SEO workflows and measurement.
Combines MLOps and technical SEO to demystify LLMs, ethics, and practical internal tool-building.
Builds no-code and low-code AI SEO applications
Shaping AEO playbooks and platform measurement for how brands get cited in AI Overviews and LLM answers.
Runs large-scale studies comparing brand visibility in LLMs vs. Google and shares Python/data-science approaches to SEO.
Blends generative AI, data journalism and PR to create link-earning content and AI-ready brand assets and operates a killer library of genAI-based tools for marketers
Trains marketers to embed ML into SEO workflows and ships practical automation guides and talks.
Builds and open-sources Python-based SEO automation (semantic keyword clustering with embeddings, GSC/crawl utilities, URL audit scripts) that turn large SEO datasets into entity-level insights and technical fixes at scale.
Director at Candour and creator of AlsoAsked, he turns query and SERP data into tools and experiments that help teams understand AI Overviews/AI Mode, test SEO changes, and monitor how AI-driven SERPs impact clicks and demand.
Reverse-engineers generative search systems and shares hands-on experiments for GEO/LLM attribution.
Teaches AI SEO workflow automations and creates technical resources for working with AI/ML APIs
Coined the concept relevance engineering and publishes deep, practical playbooks on AI Overviews, RAG, and mechanics of AI search.
Explains how Google’s AI systems work and applies data science to strategy in content systems
Leads technical SEO and generative AI strategy at Dejan, applying machine learning (re-ranking algorithms, internal-link ML, semantic site structures) and “grounded” RAG-style approaches to make brands more visible and measurable in AI search.
Pioneers Generative Engine Optimization (GEO) and Large Language Model Optimization (LLMO), publishing deep analyses of AI search mechanics (AI Overviews/AI Mode, query fan-out, MIPS, patents) and running the SEO Research Suite to operationalize GEO research and measurement.
Publishes rigorous research on AI bot crawling and practical levers that affect AI visibility.
Co-founder of Similar.ai and long-time expert in AI’s application and impact on SEO, content, and search; shares thoughtful perspectives on AI through a philosophical lens—exploring consciousness, perception, reasoning, and adoption.
Runs data-backed experiments on AI Overviews impact and shares pragmatic AI-era content strategy.
Champions content transformation and distribution for marketers and SEOs
Analyzes AI crawlers and rendering implications to keep sites visible to LLMs and AI search.
Shares robust workflows on BigQuery, JS SEO and diagnostic frameworks for AI-era search.
Has a course for Python for SEOs that also covers in part applications of AI/ML in SEO
Advocates human-centered, data-rich marketing and shows how to compete in AI search while calling out low-value AI content.
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