Michael Patrick Cortez Person
Digital marketing strategist with nearly two decades at the intersection of search, brand, and AI. Director of Growth & Marketing at Webfor, board member at SEMpdx, and a leading practitioner of GEO, Semantic SEO, and AI-native marketing workflows.
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Nearly 20 years at the intersection of search, brand, and strategy. I help businesses win in organic search — and in every AI system that’s replacing it.
Two decades across sales leadership, digital marketing, SEO, brand strategy, and agency partnership.
Using Claude Code, MCP integrations, and AI-native systems to do in minutes what used to take days.
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Webfor Organization
A digital marketing agency where Michael Patrick Cortez serves as Director of Growth & Marketing, delivering SEO, GEO, PPC, and AI-native marketing services.
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Director of Growth & Marketing at Webfor.
SEO Concept
also: Search Engine Optimization
The practice of improving a website’s visibility in organic search results — encompassing technical optimization, semantic content strategy, and authority building — increasingly practiced alongside AI system optimization.
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I help businesses win in organic search — and in every AI system that’s replacing it.
Entity-based SEO, site architecture, crawlability, and structured data — the deep technical work that separates brands that rank from brands that drift.
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Generative Engine Optimization Concept
also: GEO
The discipline of optimizing content, entity signals, and brand presence so that AI systems — including ChatGPT, Claude, Perplexity, and Gemini — discover, cite, and recommend a brand in generated answers. Runs parallel to traditional SEO.
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Getting brands mentioned in ChatGPT, Claude, and Perplexity. The new visibility game runs parallel to SEO — and most brands aren’t playing it yet.
Google AI Overviews now appear in 25–48% of all queries. When they do, organic CTR on that result drops by an average of 61%.
Only 11% of domains get cited by both ChatGPT and Perplexity.
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AI Search Visibility Concept
The rate at which a brand, content, or product appears in AI-generated responses from ChatGPT, Perplexity, Google AI Overviews, Claude, and Gemini. Unlike fixed search rankings, AI visibility operates probabilistically across platforms with distinct citation behaviors.
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AI search visibility is the rate at which your brand, content, or products appear in AI-generated responses from ChatGPT, Perplexity, Google AI Overviews, Claude, and Gemini.
88% of URLs cited by ChatGPT come directly from Bing search results.
44.2% of LLM citations reference content from the first 30% of an article.
Answer Engine Optimization Concept
also: AEO
The practice of structuring content so that AI systems can extract, cite, and surface it directly inside generated answers — using self-contained answer blocks, question-format headings, schema markup, and E-E-A-T authority signals.
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AEO is the practice of structuring your content so that AI systems can extract, cite, and surface it directly inside generated answers.
Cited sites in AI Overviews see 35% CTR boost versus uncited competitors.
Content scoring 8.5/10 for semantic completeness is 4.2x more likely to be cited.
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Technical SEO Concept
The discipline of optimizing a website’s technical foundation — site architecture, crawlability, structured data, indexation, Core Web Vitals, and JavaScript rendering — to ensure search engines and AI crawlers can fully access and understand its content.
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Entity-based SEO, site architecture, crawlability, and structured data — the deep technical work that separates brands that rank from brands that drift.
The Screaming Frog MCP server lets Claude run crawls, generate reports, execute custom Node.js scripts against crawl data, and write findings to project files.
Semantic SEO Concept
An approach to search optimization that builds topical authority through entity relationships, meaning-based content structures, and internal linking — enabling search engines to deeply understand a site’s expertise rather than matching keywords.
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Websites with complete entity schema markup receive 3.2x more citations in AI-generated answers than sites without it.
It does not look at what keywords competitors rank for. It looks at what concepts competitors consistently discuss together.
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Entity SEO Concept
An SEO practice centered on entity recognition, knowledge graph optimization, and structured data — building the machine-readable signals that allow Google and AI systems to confidently identify, understand, and cite a brand.
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Pages with 15 or more recognized entities in their content show 4.8x higher AI Overview selection rates.
Google’s Knowledge Graph contains over 500 billion facts about 5 billion entities.
Server-side JSON-LD is not optional. If you’re using a tag manager or JavaScript to inject your structured data, you’re invisible to AI crawlers.
Entity Salience Concept
Google’s measure of how central an entity is to a document, expressed as a score from 0.0 to 1.0. All entity scores in a document sum to exactly 1.0, meaning adding unfocused topics dilutes the salience of the primary entity.
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Entity salience is Google’s measure of how central an entity is to a document, expressed as a score from 0.0 to 1.0.
Every entity score in a document sums to exactly 1.0. That normalization is the mechanic most SEOs miss.
Entity Co-occurrence Concept
The pattern of which named entities appear together within and across documents covering the same topic. Google uses these co-occurrence patterns to build its understanding of how real-world concepts relate to each other.
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Entity co-occurrence represents the pattern of which named entities appear together within and across documents covering the same topic.
Google uses these patterns to build its understanding of how concepts relate to each other in the real world.
Information Gain Concept
Google’s measure of how much new, original knowledge a piece of content adds to the existing web. Scored across five dimensions: proprietary data, first-hand evidence, original framework, expert attribution, and freshness hook.
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Information gain is Google’s measure of how much new, original knowledge a piece of content adds to the existing web.
Content with direct quotations received a 42.6% citation lift in AI responses.
Semantic Gap Analysis Methodology
A structured workflow that identifies which entity attributes, subtopics, and co-occurring concepts are missing from a page’s content compared to what top-ranking competitor pages collectively address — going beyond keyword gaps to the semantic layer.
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A semantic gap exists when your content is missing specific entity attributes that the top-ranking pages collectively address.
Semantic gap analysis identifies which entity attributes, subtopics, and co-occurring concepts are missing from your content, not which keywords you forgot to include.
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EAV Framework Methodology
also: Entity-Attribute-Value Framework
An Entity-Attribute-Value content structuring approach that organizes page information the way Google reads knowledge graph entries — where every entity has attributes and each attribute has a specific value — allowing Google to assess whether a page is a reliable source for that entity type.
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Attribute completeness is what Google uses to decide whether your page is a reliable source about that entity type.
an entity (the subject, like ‘Moz’), an attribute (a characteristic, like ‘founding year’), and a value (the specific data, like ‘2004’).
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Topical Authority Mapping Methodology
A five-component framework for building and sequencing content clusters — Source Context, Central Entity, Central Search Intent, Core Section, and Outer Section — to signal deep topical expertise to search engines through structured coverage breadth and depth.
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a keyword list and a topical map answer different questions.
Topical authority involves processing connected topics and entailed search queries with accurate, unique, and expert information.
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Knowledge Panel Audit Methodology
A structured 20-minute workflow that compares an Entity Home, Wikidata record, on-page schema, and Google’s Knowledge Panel side by side to identify and close the five corroboration gaps that suppress or destabilize a brand’s Knowledge Panel.
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A Knowledge Panel audit compares four signals: your Entity Home, Wikidata record, on-page schema, and the Knowledge Panel Google displays.
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Claude Code Skills Methodology
The practice of building persistent slash-command workflow files — saved as Markdown at ~/.claude/skills/ — that run complete, context-aware SEO and marketing tasks against live data in a single command, replacing one-off prompts with reusable automation.
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A prompt is something you type once and forget. A skill is something you build once and run forever.
Creating a skill takes about 60 seconds.
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- TARGETS → SEO (confidence: declared)
Claude Code SoftwareProduct
Anthropic’s CLI-based AI coding environment used to run agentic SEO workflows, MCP data integrations, parallel subagent analysis, and automated marketing tasks — giving marketers a full development environment for AI-native workflows.
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The actual value of Claude for SEO has almost nothing to do with content generation. It’s in the analysis layer – the ability to process an entire site’s GSC data in seconds, cross-reference keyword cannibalization across hundreds of pages, reverse-engineer competitor content strategies, and build technical audit workflows that run on command.
Using Claude Code, MCP integrations, and AI-native systems to do in minutes what used to take days.
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Model Context Protocol Standard
also: MCP
An open protocol that connects Claude directly to external tools — including Google Search Console, Ahrefs, Semrush, Screaming Frog, and Notion — enabling live data retrieval inside a session without manual CSV exports or copy-pasting.
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MCP (Model Context Protocol) lets Claude connect directly to Google Search Console, Ahrefs, Semrush, Notion, Google Sheets, and dozens of other services.
Claude is only as useful as the data it can see. MCP fixes that.
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SEMpdx Organization
The premier digital marketing professional community in the Pacific Northwest, where Michael Patrick Cortez serves as a board member and moderator.
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Board Member & Moderator at SEMpdx.
Google Ads Platform
Google’s paid advertising platform — spanning search, display, and AI Max campaigns — audited, built, and managed through Claude Code and MCP integrations using Google Ads Query Language (GAQL) for campaign data retrieval and analysis.
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Performance Marketing: Paid search, conversion strategy, and measurement frameworks grounded in 20 years of driving real business outcomes.
monetary values in GAQL are returned in micros (millionths of the currency unit).