AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
OK, so last year I was working with an e-commerce client on their SEO strategy when something weird happened. We started noticing their content appearing in Claude AI responses - and this was in a traditional niche where you wouldn't expect much LLM usage.
This got me thinking: if our content was showing up in AI responses naturally, what if we could optimize for this intentionally? Most people are still obsessing over traditional SEO while a whole new search landscape is emerging right under our noses.
Here's the thing - while everyone's debating whether "SEO is dead" because of AI, I decided to actually test what makes content rank in Claude AI. Not theoretically, but with real experiments across multiple client projects.
What I discovered completely changed how I think about content optimization in 2025. The factors that make Claude surface your content aren't what you'd expect from traditional SEO.
In this playbook, you'll learn:
Why traditional SEO signals matter less for AI search ranking
The 3 content structure patterns that consistently get Claude mentions
How to optimize for "chunk-level thinking" instead of page-level ranking
My framework for building citation-worthy content that AI actually wants to reference
Real metrics from testing GEO (Generative Engine Optimization) tactics
This isn't about replacing your traditional SEO strategy - it's about adding a new layer that future-proofs your content strategy.
New Reality
What most people get wrong about AI search
Most SEO experts are approaching AI search optimization completely backwards. They're trying to apply the same old tactics that worked for Google to Claude AI and other LLMs.
Here's what the industry typically recommends for "AI SEO":
Focus on featured snippets - optimize for position zero thinking it'll translate to AI mentions
Stuff more keywords - believing LLMs work like traditional search engines
Create FAQ sections - assuming AI will pull from these structured formats
Optimize for voice search - thinking conversational queries are the same as AI interactions
Build more backlinks - applying traditional authority signals to AI ranking
The problem? LLMs don't consume content like Google does. They break everything down into passages and synthesize answers from multiple sources. They're not looking at your page authority or keyword density.
What matters is whether your content can stand alone as a valuable snippet and provide clear, actionable information that an AI can confidently cite. It's about chunk-level optimization, not page-level optimization.
The conventional wisdom exists because it's easier to apply old frameworks to new problems. But Claude AI operates on completely different principles than traditional search engines. While Google ranks pages, Claude evaluates and synthesizes information passages.
This fundamental misunderstanding is why most "AI SEO" advice falls flat in practice.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
OK, so let me tell you about the project that opened my eyes to this whole AI search thing. I was working with this e-commerce client - let's call them a specialty tools retailer - on a complete SEO overhaul. Traditional niche, nothing fancy about AI or tech.
We'd built this solid content foundation using AI-powered content generation across multiple languages. Everything was performing well in Google, but then I started tracking something unusual.
A couple dozen times per month, we were getting LLM mentions - people would ask Claude or other AI models about topics in their niche, and our content was being cited. This wasn't something we'd optimized for. It was just happening naturally.
That's when I realized: if this is happening by accident, what could we achieve if we did it intentionally?
My first approach was completely wrong. I tried applying traditional SEO thinking - optimizing for featured snippets, adding more structured data, creating FAQ sections. The results? Basically nothing. Our AI mentions stayed roughly the same.
I was treating AI search like it was just "Google with a chatbot interface." But through conversations with teams at AI-first startups and digging into how LLMs actually process content, I discovered something crucial:
LLMs don't care about your page structure, your domain authority, or your keyword optimization. They care about whether each section of your content can stand alone as a trustworthy, complete answer to a specific question.
This insight completely changed my approach. Instead of optimizing pages, I started optimizing individual content chunks for what I call "citation-worthiness." The difference was immediate and dramatic.
Here's my playbook
What I ended up doing and the results.
After months of testing and dozens of failed experiments, I developed what I call the GEO Framework - Generative Engine Optimization. This isn't about gaming the system; it's about understanding how AI models actually consume and cite content.
Here's the step-by-step process that consistently gets content mentioned by Claude AI:
Step 1: Chunk-Level Content Structure
Instead of writing traditional blog posts, I restructure content so each section can stand alone. Every 2-3 paragraphs need to be self-contained and answer a specific question completely. No relying on context from other parts of the page.
For my e-commerce client, instead of writing "The Ultimate Guide to X," I created content like "How to solve Y problem" with each solution explained independently. Claude can pull any section and it makes perfect sense on its own.
Step 2: Citation-Worthy Formatting
I discovered that Claude heavily favors content that's already formatted for easy extraction:
Clear, factual statements without fluff
Numbered steps that work independently
Specific examples with measurable outcomes
Attribution-ready statements ("According to [source]")
Step 3: Answer Synthesis Readiness
Claude doesn't just copy-paste content - it synthesizes information from multiple sources. I optimize for this by:
Writing in a tone that blends well with other authoritative sources
Using consistent terminology that matches industry standards
Providing context that helps AI understand when to cite this information
Step 4: Topical Authority Clustering
Instead of random blog posts, I create content clusters around specific expertise areas. For the e-commerce client, we built comprehensive coverage of their specialty - not just surface-level content, but deep, interconnected expertise that establishes us as the go-to source for that topic.
Step 5: Multi-Modal Integration
Claude can process various content types, so I integrate:
Tables with clear data
Step-by-step processes with visual cues
Code examples and technical specifications
Charts and infographics with descriptive alt text
The key insight? Don't write for humans then hope AI can understand it. Write for AI synthesis while keeping it valuable for humans.
Chunk Optimization
Each content section must work independently and provide complete value without requiring context from the rest of the page.
Synthesis Ready
Format content in a way that blends naturally with other authoritative sources when AI models create comprehensive answers.
Authority Clustering
Build interconnected content around specific expertise areas rather than scattered blog posts on random topics.
Multi-Modal Approach
Integrate tables, processes, and visual elements that AI can process and reference effectively in responses.
The results from implementing this GEO framework were more significant than I expected. Within three months of restructuring our content approach:
AI Mention Growth: Our LLM citations increased from a couple dozen per month to over 200 tracked mentions across Claude, Perplexity, and other AI platforms. But here's the thing - traditional metrics showed parallel improvement too.
Traditional SEO Boost: Our Google rankings actually improved as well. Turns out, content optimized for AI synthesis is also really good for traditional search. Our organic traffic increased by 40% during the same period.
Content Efficiency: Instead of creating 20 mediocre blog posts, we focused on 8 comprehensive, deeply optimized pieces. Less content, but each piece worked harder and got more visibility.
Authority Recognition: The most unexpected result? Our content started getting cited not just by AI, but by human researchers and industry publications. When you optimize for citation-worthiness, you create genuinely valuable content.
The timeline was faster than traditional SEO. While normal content can take 6-12 months to rank, our AI-optimized content started getting mentions within 2-4 weeks of publication.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After testing GEO tactics across multiple client projects, here are the most important lessons I learned:
Quality beats quantity every time. One well-structured, comprehensive piece gets more AI citations than 10 shallow articles.
Context independence is crucial. If your content requires reading other sections to make sense, AI won't cite it confidently.
Specific examples outperform general advice. Claude heavily favors content with concrete, measurable examples over theoretical concepts.
Traditional SEO still matters. GEO works best as a layer on top of solid SEO fundamentals, not a replacement.
Industry terminology consistency is critical. Use the same terms that authoritative sources use to increase synthesis compatibility.
Update frequency impacts citations. Fresh, current information gets prioritized by AI models over outdated content.
Attribution-ready format increases confidence. When your content is already formatted for easy citation, AI models are more likely to reference it.
What I'd do differently: Start with GEO principles from day one instead of retrofitting existing content. It's much easier to build with these principles than to restructure later.
This approach works best for expertise-driven businesses with deep knowledge in specific areas. It's less effective for thin content or topics without clear expertise demonstration.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups:
Focus on use-case documentation that works independently
Create integration guides with step-by-step instructions
Build comprehensive feature comparisons with specific examples
Document real customer success metrics and outcomes
For your Ecommerce store
For e-commerce stores:
Create product category guides with specific use cases
Build buying guides with clear decision frameworks
Document product specifications in AI-readable formats
Create comparison content with measurable criteria