AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last month, I was working with an e-commerce client who had a solid SEO foundation - their blog was performing well, ranking for the right keywords, and bringing in organic traffic. But we discovered something interesting: their content was starting to appear in AI-generated responses, despite being in a niche where LLM usage isn't common.
This discovery led me down the rabbit hole of GEO (Generative Engine Optimization) - basically, optimizing for AI chatbots like ChatGPT, Claude, and Perplexity. While everyone's still figuring this out, I've learned some hard truths about why most content gets ignored by AI systems.
The reality? Your content might be invisible to AI chatbots for reasons that have nothing to do with traditional SEO. And that's a problem, because AI-driven search is already changing how people find information.
Here's what you'll learn from my real-world experiments:
Why traditional SEO tactics fail for AI visibility
The "chunk-level thinking" approach that actually works
My 3-step framework for getting mentioned by AI chatbots
Real tactics that moved the needle for my clients
Why GEO is different from traditional SEO optimization
This isn't about abandoning SEO - it's about building on top of what already works.
Industry Reality
What the ""experts"" are getting wrong about AI optimization
If you've been following the AI optimization conversation, you've probably heard the usual advice. Most SEO experts are approaching AI chatbot optimization like it's just another search engine to game.
Here's what the industry typically recommends:
Focus on featured snippets: The logic is that if Google features your content, AI will too
Optimize for question-based queries: Since people ask AI chatbots questions
Add more schema markup: Hoping structured data helps AI systems understand content
Create FAQ sections: Assuming AI prefers Q&A format
Increase content depth: Writing longer, more comprehensive pieces
This conventional wisdom exists because it's the natural extension of what worked for traditional search. SEO professionals are applying their existing toolkit to a fundamentally different problem.
But here's where it falls short: AI systems don't consume content like search engines do. They break content into passages and synthesize answers from multiple sources. They're not looking for the "best page" - they're looking for the best chunks of information across many pages.
Traditional SEO focuses on page-level optimization. AI optimization requires chunk-level thinking. That's a completely different game, and most content creators are still playing by the old rules.
The result? Tons of well-optimized content that AI systems completely ignore.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Working with my e-commerce client, we discovered their content was getting a couple dozen LLM mentions per month - not because we optimized for it, but as a natural byproduct of solid content fundamentals. This wasn't something we initially targeted, but it opened my eyes to how AI systems actually work.
The client was in a traditional e-commerce niche where you wouldn't expect much AI interaction. Yet we were tracking mentions in ChatGPT, Claude, and Perplexity responses. The content that got picked up wasn't necessarily our highest-ranking pages or our most comprehensive guides.
That's when I realized we were looking at this all wrong. Through conversations with teams at AI-first startups and my own experiments, I discovered that everyone is still figuring this out. There's no definitive playbook yet, but there are patterns emerging.
The breakthrough came when I stopped thinking about pages and started thinking about passages. AI systems don't care about your beautiful homepage or your comprehensive pillar content. They care about specific chunks of information that can stand alone and answer user questions directly.
This led me to restructure how we approach content entirely - not abandoning traditional SEO, but layering on a new approach designed specifically for how AI systems process and reference information.
Here's my playbook
What I ended up doing and the results.
Instead of trying to game AI systems, I developed a layered approach that builds on strong SEO fundamentals. Here's the exact framework I use:
Step 1: Foundation First
Don't abandon traditional SEO for shiny new GEO tactics. Your content still needs to be crawlable, indexable, and valuable to humans. AI systems need to find your content before they can reference it. I always start with:
Quality, relevant content that serves real user needs
Proper technical SEO implementation
Clear site structure and internal linking
Regular content updates and maintenance
Step 2: Chunk-Level Optimization
This is where GEO differs from traditional SEO. I restructure content so each section can stand alone as a valuable snippet. Here's how:
Self-contained sections: Each heading and paragraph combo should answer a complete question
Logical structure: Information flows in a way that makes sense when extracted
Clear attribution: Facts are stated clearly with obvious sources
Context inclusion: Each chunk includes enough context to be understood independently
Step 3: Signal Amplification
Once the content is structured correctly, I focus on amplifying the right signals:
Topical breadth and depth: Covering all facets of topics comprehensively
Citation-worthiness: Ensuring factual accuracy and clear attribution
Multi-modal support: Integrating charts, tables, and visuals that support the text
Answer synthesis readiness: Structuring information for easy extraction and combination
The key insight: AI systems are pattern recognition machines. They look for content that consistently provides accurate, well-structured information that can be confidently referenced and combined with other sources.
This approach worked because it aligns with how modern AI systems actually process information, rather than trying to reverse-engineer what we think they want.
Foundation Setup
Start with solid SEO fundamentals before adding GEO tactics. AI systems need to find your content first.
Chunk Structure
Break content into self-contained sections that can stand alone as complete answers.
Signal Quality
Focus on factual accuracy and clear attribution to build citation-worthiness.
Testing Method
Track mentions across multiple AI platforms to measure real-world performance.
The results from this approach have been encouraging, though I want to be realistic about the current state of AI optimization. For my e-commerce client, we went from occasional random mentions to consistent references across multiple AI platforms.
More importantly, the mentions we achieved weren't from aggressive GEO tactics - they came from content that naturally aligned with how AI systems process information. The couple dozen monthly mentions we tracked represented real value because they were in response to genuine user queries in our niche.
What moved the needle wasn't any single optimization, but the systematic approach to structuring content for both human readers and AI consumption. The content that got picked up shared common characteristics: clear structure, factual accuracy, and logical flow.
The timeline was longer than traditional SEO - we started seeing consistent mentions after about 3-4 months of implementing the new approach. But the mentions we did get were high-quality and contextually relevant.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons from my GEO experiments:
Foundation matters most: Strong traditional SEO is still the starting point for AI visibility
Structure trumps length: Well-organized shorter content often outperforms comprehensive but poorly structured pieces
Context is king: Each section needs enough context to be understood independently
Accuracy over authority: AI systems seem to prioritize factual accuracy over domain authority
Patience required: GEO results take longer to materialize than traditional SEO
Multi-platform thinking: Different AI systems prefer different types of content structure
Evolution is constant: AI systems are changing rapidly, so strategies need to adapt
The biggest lesson? Don't abandon what works. Build your GEO strategy on top of strong SEO fundamentals, not instead of them. The landscape is evolving too quickly to bet everything on optimization tactics that might be obsolete in six months.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies looking to improve AI chatbot visibility:
Focus on use-case specific content that answers direct questions
Structure feature explanations as self-contained chunks
Create integration guides with clear step-by-step instructions
Build comprehensive but modular documentation
For your Ecommerce store
For e-commerce stores targeting AI visibility:
Create buying guides with clear product comparisons
Structure product information for easy extraction
Build category pages that answer common shopping questions
Focus on problem-solution content formats