Mike King is the CEO of iPullRank and one of the few SEO leaders working seriously with artificial intelligence and machine learning. His background is computer science, and it shows. He thinks about content strategy through a technical lens: what can be automated, what patterns can be discovered through data analysis, what can be scaled without sacrificing quality.
He's coined the term "Content Engineering": the idea that content production should be treated as an engineering discipline, not just a creative one. Engineers build systems that are reliable, scalable, and maintainable. They don't rely on inspiration or hope. They rely on process, data, and iteration. King applies this thinking to content strategy. If you want to produce 100 pages of quality content, you need a system, not just talented writers. That system is Content Engineering.
Who They Are
King's computer science background sets him apart in the SEO world. Most SEO professionals come from marketing, journalism, or communications. King comes from code. This shapes his entire approach. He's interested in using technology to solve content problems: semantic analysis to find gaps, vector embeddings to understand topic relationships, machine learning to predict what content will perform well, AI-assisted workflows to scale production. He sees content strategy through a technical lens without losing sight of the human elements.
He's not recklessly chasing new technology. He's thoughtful about where AI and automation actually create value in content strategy. He's critical of using AI for content generation without understanding what you're optimising for. But he's also clear-eyed about what technology enables: scaling expertise, finding patterns humans can't see, automating routine tasks so humans can focus on strategic decisions. This balanced perspective separates King from hype-driven practitioners.
His reputation is built on integrating AI and semantic analysis into practical SEO workflows. iPullRank is known for tools that use machine learning and semantic analysis. King publishes research on how Google evaluates content at the semantic and passage level. He's presented at major conferences on content engineering and the role of AI in SEO. His contributions have shaped how the industry thinks about the intersection of AI and content strategy.
King's particular strength is making emerging technology practical. When vector embeddings or RAG systems emerge, he asks immediately: how does this solve real content problems? How do you implement this in a way that works? He avoids theoretical discussions and focuses on application.
What They Teach
"Content Engineering" is the framework for treating content production systematically. This means defining content requirements upfront: who is the audience, what problem does this content solve, what will success look like. It means building templates and systems so you can produce multiple pieces without reinventing the process each time. It means integrating data throughout: audience research to inform strategy, competitor analysis to identify gaps, performance data to optimise, semantic analysis to ensure comprehensiveness.
AI and machine learning integration is the second pillar of his teaching. Specifically, he's focused on how to use technology appropriately. Vector embeddings for semantic analysis: understanding which topics are closely related at a semantic level, identifying content gaps by finding related topics you don't cover. Retrieval-Augmented Generation (RAG): using language models in ways that ground output in actual data rather than relying on the model's training data alone. This produces more accurate, more specific content.
Passage-level optimisation is another focus area. Google doesn't just evaluate pages. It evaluates passages within pages. Understanding how Google breaks pages into passages and evaluates them at a passage level changes how you structure content. A single page might contain multiple passages that answer different queries. King's work on passage evaluation helps you optimise at that level: understanding where to put key information, how to structure content so passages are identifiable and evaluable, how to target multiple intent variations within a single page.
Technical content optimisation through semantic analysis is also central to his teaching. Semantic analysis tools can identify what topics you're covering and what related topics you're missing. This reveals content gaps that keyword research alone wouldn't catch. It shows you where your topic coverage has holes. It helps you build more comprehensive content that addresses related questions and needs.
Persona-driven SEO is another component of his methodology. Audience understanding should drive technical execution. Not all audiences are the same. Different personas have different needs, different vocabularies, different levels of expertise. Content Engineering starts with clear persona definitions and ensures that content is built to serve those personas effectively. This connects audience understanding to technical execution.
How It Maps to Opportunity and Authority
King's work is balanced between Opportunity and Authority (approximately 50 per cent each). The Opportunity side is substantial. Content Engineering at scale is about identifying and pursuing Opportunities that might be otherwise invisible. Semantic analysis reveals content gaps that traditional keyword research misses. Vector embedding analysis finds topics you're not covering that are closely related to what you do cover. Programmatic SEO at scale lets you pursue dozens or hundreds of micro-Opportunities efficiently.
His work on passage-level optimisation has Opportunity implications too. If a single page can effectively target multiple intent variations, you're capturing more Opportunity from the same piece of content. Semantic analysis helps identify these multi-intent opportunities so you can design content to capture them.
The Authority side of King's work is equally important. Content Engineering processes, when done well, ensure quality at scale. You're not sacrificing quality to produce volume. You're building systems that maintain quality standards while expanding output. Semantic depth signals expertise. Pages that comprehensively cover a topic at multiple levels and from multiple angles signal deep knowledge. Technical excellence in content structure, information architecture, and semantic coherence is itself a credibility signal. Well-engineered content is perceived as authoritative content.
Persona-driven content engineering ensures that you're building content that actually serves the audience you're trying to reach. This builds trust and authority with those audiences. You're not writing for the algorithm. You're writing for the person, using technical systems to ensure you're doing it well.
When to Learn From Them
Learn from King if your diagnostic shows you need to scale content production without losing quality. If you have the expertise to produce two outstanding pages but you need 50 pages at that quality level, Content Engineering is how you do it. King's methodology makes quality scalable. This is a common challenge. Many organisations have expertise but lack the capacity to document and share that expertise at scale. Content Engineering solves this.
Learn from him if you're technically minded and want to understand how Google evaluates content at a semantic level. His work on passage evaluation, semantic analysis, and vector embeddings goes deep into how Google understands content. This knowledge helps you build content that aligns with how Google evaluates it. Understanding passage-level evaluation is increasingly important as Google moves toward AI Overviews and more granular content understanding.
Learn from him if you want to integrate AI appropriately into your SEO and content workflow. He's not a cheerleader for AI for its own sake. He's clear about what AI does well and what it doesn't. He teaches practical patterns for using AI in ways that produce better results. This grounded perspective is valuable when AI is generating hype and confusion in equal measure.
Also learn from him if you suspect you have content gaps that traditional keyword research isn't catching. Semantic analysis reveals these gaps. If you're covering topic A comprehensively but missing related topics that could attract traffic or build authority, his methodology will find and help you address those gaps. This is particularly valuable in technical or complex topics where related subtopics might not show up in keyword volume data.
Learn from him if you're competing with larger sites that have bigger content budgets. Content Engineering lets you be more efficient with resources. You can produce more content faster without sacrificing quality. You can identify and pursue high-value Opportunities that others might miss. This efficiency advantage is sustainable.
Learn from him if you believe content and technical SEO should work as one integrated discipline. King's entire framework is predicated on this. Technical understanding informs content strategy. Data informs decisions. Automation handles routine tasks. Humans focus on strategy and creativity.
Where to Start
Start with the iPullRank blog. Look for articles on Content Engineering, semantic analysis, and AI integration in SEO. These foundational pieces will establish his framework and show how the different components fit together. King's writing is technical but accessible. He explains complex concepts clearly without oversimplifying them.
Read his book "The Science of SEO" if you want a comprehensive treatment of his methodology. This covers Content Engineering, technical SEO, semantic analysis, and the role of data in decision-making. It's the most complete statement of his philosophy and approach.
Look for his presentations at industry conferences, particularly Mozcon. He typically speaks on content engineering, semantic analysis, and AI applications in SEO. These presentations include real case studies and go deeper into methodology than blog posts alone. Conference recordings are valuable resources you can return to multiple times.
Watch iPullRank tool demonstrations. The company's tools are built on these principles. Seeing how semantic analysis tools work in practice gives you concrete understanding of how to apply these concepts to your own content. Tool walkthroughs demystify complex concepts and show practical application.
Start your own content audit using semantic analysis. Identify a topic you cover comprehensively. Use semantic analysis tools to identify related topics. Which of those do you cover? Which are gaps? This shows you how semantic analysis reveals Opportunity that keyword research might miss. This hands-on experience is where King's teaching becomes practical.
Then consider your content production process. Is it systematic or ad hoc? Do you have templates and standards? Is it repeatable? If not, this is where Content Engineering starts. Define your process, standardise your templates, integrate data collection, and build systems that ensure quality at scale. This process improvement is where meaningful efficiency gains emerge.
Part of the Expert Series. Back to the framework or the diagnostic. Part of the Marketing Universe. Explore Traffic Plus Offer : The Trust Algorithm : 4-Quadrant AI. Read the book: Marketing Curious: Working the Noise.