AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, I found myself staring at something that made no sense. My client's content was appearing in AI-generated responses - ChatGPT, Claude, Perplexity - despite being in a niche where LLM usage isn't common. We hadn't optimized for it. We hadn't even considered it. Yet there it was.
This discovery sent me down a rabbit hole that consumed the next six months. Will AI really replace traditional search ranking? Everyone's asking this question, but most are answering it with speculation rather than actual experimentation.
Here's what I learned after implementing both traditional SEO and GEO (Generative Engine Optimization) strategies across multiple client projects, including an e-commerce site that went from virtually no AI mentions to dozens monthly.
You'll discover:
Why most people are asking the wrong question about AI search
The real relationship between traditional SEO and AI ranking
My actual testing results across different industries
The chunk-level optimization strategy that actually works
When to prioritize GEO over SEO (and when not to)
Current Thinking
What the SEO world is saying about AI search
The SEO community is split into two camps right now. The first camp is panicking, convinced that ChatGPT and similar tools will kill Google overnight. They're frantically trying to optimize for AI while abandoning proven SEO fundamentals.
The second camp is dismissive, treating AI search as a passing fad that won't affect "real" search behavior. They're doubling down on traditional ranking factors and ignoring the shift entirely.
Here's what both camps typically recommend:
Focus on featured snippets - The theory being that if you rank for snippets, AI will pick up your content
Optimize for question-based queries - Since AI excels at answering questions
Create "AI-friendly" content - Whatever that means (nobody really knows)
Either abandon SEO completely or ignore AI completely - No middle ground
Wait and see what happens - The classic procrastination strategy
This conventional wisdom exists because we're in uncharted territory. When Google launched, we had decades to figure out ranking factors. With AI search, we're making educated guesses based on limited data.
But here's where this approach falls short: it treats AI search and traditional search as completely separate ecosystems. My testing revealed they're more interconnected than anyone realizes. The foundations that make content rank well in Google also make it citation-worthy for AI systems.
The real question isn't "Will AI replace traditional search ranking?" It's "How do these systems work together, and how can we optimize for both?"
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The project that changed my perspective started as a traditional SEO overhaul for a B2C e-commerce client. We needed to scale from virtually no organic traffic to something meaningful, and fast. The client had thousands of products but zero content strategy.
During our initial content audit, I noticed something unexpected in our traffic analytics. A couple dozen visits per month were coming from what looked like direct traffic, but with unusual user behavior patterns. Digging deeper, I discovered these visitors had actually found the site through AI-generated responses - despite us never optimizing for AI.
This was my "aha" moment. Even in a traditional e-commerce niche where people aren't typically asking ChatGPT for product recommendations, our content was still being surfaced by AI systems. We were getting GEO results without even trying.
That's when I decided to run a parallel experiment. Instead of choosing between traditional SEO and GEO optimization, I would implement both strategies simultaneously and track the results. My hypothesis: the foundations of good SEO would actually strengthen our AI visibility, not compete with it.
The client was perfect for this test - they needed massive content scaling anyway, and they were open to experimental approaches. We had 3,000+ products across 8 languages, which meant thousands of pages to optimize. If there was a relationship between SEO and GEO performance, we'd see it at scale.
But first, I had to figure out what "GEO optimization" actually meant. Nobody had a playbook for this.
Here's my playbook
What I ended up doing and the results.
After conversations with teams at AI-first startups and months of testing, I developed what I call the "foundation-first" approach to GEO. Instead of treating AI optimization as a separate discipline, I built it on top of solid SEO fundamentals.
Here's the system I implemented:
Layer 1: Traditional SEO Foundation
I started with proven SEO basics because AI systems still need to crawl and index content just like traditional search engines. Quality, relevant content remains the cornerstone whether you're optimizing for Google or ChatGPT.
For the e-commerce client, this meant:
Comprehensive keyword research and content mapping
Technical SEO optimization across all 20,000+ pages
Structured data implementation for better content understanding
Internal linking strategy that creates clear content relationships
Layer 2: Chunk-Level Content Architecture
This is where GEO diverges from traditional SEO. AI systems don't consume pages like search engines - they break content into passages and synthesize answers from multiple sources. I restructured our content so each section could stand alone as a valuable snippet.
Every piece of content followed this structure:
Self-contained sections - Each heading and paragraph combo answered a specific question completely
Citation-worthy facts - Specific, verifiable information that AI systems could confidently reference
Logical progression - Content flowed naturally for humans while being modular for AI extraction
Layer 3: Multi-Modal Content Integration
AI systems increasingly process not just text, but images, charts, and structured data. I integrated visual elements that both enhanced user experience and provided additional context for AI understanding.
Layer 4: Continuous Testing and Iteration
I tracked mentions across multiple AI platforms using a combination of manual checking and automated monitoring. This wasn't about gaming the system - it was about understanding which content formats and topics naturally aligned with how AI systems synthesize information.
The key insight: AI systems favor content that already demonstrates expertise, authority, and trustworthiness - the same E-A-T factors that Google values. The optimization techniques that worked weren't revolutionary; they were fundamentals executed at a higher level.
Content Architecture
Restructuring for both human readers and AI systems
Multi-Modal Approach
Integrating visuals and structured data for comprehensive optimization
Testing Framework
Systematic tracking across multiple AI platforms and traditional search
Performance Metrics
Measuring success in both traditional rankings and AI citations
The results weren't what I expected - they were better. Within three months, we achieved a 10x increase in organic traffic through traditional SEO while simultaneously seeing our AI mentions grow from a couple dozen to over 100 monthly citations across various platforms.
Here's what actually moved the needle:
Traditional SEO Performance:
The site went from 300 monthly visitors to over 5,000, with pages ranking on page one for hundreds of long-tail keywords. The foundation-first approach meant our traditional SEO didn't suffer from GEO optimization - it actually improved.
AI Visibility Growth:
More importantly, our content started appearing naturally in AI responses without us specifically prompting for it. The chunk-level architecture made our content easy for AI systems to extract and cite accurately.
Unexpected Synergies:
The biggest surprise was how optimizing for AI improved our traditional SEO. Content structured for easy AI extraction also had better user engagement metrics, lower bounce rates, and higher time on page - all ranking factors Google values.
The layered approach proved that you don't have to choose between optimizing for traditional search and AI search. They're not competing systems; they're complementary ones that reward similar content qualities.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After six months of parallel testing, here are the key insights that changed how I think about search optimization:
AI doesn't replace SEO foundations, it amplifies them - The best-performing content in AI citations was also ranking well in traditional search
Chunk-level thinking is the future - Content that works in sections performs better across all search types
Quality over AI-gaming - Attempts to "trick" AI systems failed, while genuinely helpful content succeeded naturally
Multi-platform approach is essential - Different AI systems prefer different content formats, requiring diverse optimization
User intent remains constant - Whether someone asks Google or ChatGPT, they want accurate, helpful information
Technical SEO still matters - AI systems need to access and understand your content just like traditional crawlers
The timeline is longer than expected - AI search adoption is happening gradually, not overnight
What I'd do differently: I would have started tracking AI mentions from day one instead of discovering them accidentally. Early measurement would have revealed optimization opportunities sooner.
When this approach works best: For businesses creating substantial content volumes where you can implement systematic testing. When it doesn't work: For quick-win scenarios or businesses that can't commit to long-term content strategy.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups implementing this playbook:
Start with solid SEO fundamentals - they'll serve both traditional and AI search
Structure product documentation and help content in self-contained sections
Track mentions across multiple AI platforms from the beginning
Focus on educational content that demonstrates expertise naturally
For your Ecommerce store
For ecommerce stores implementing this playbook:
Optimize product descriptions and category pages with chunk-level thinking
Create buying guides that work as standalone sections
Implement comprehensive structured data across all product pages
Build educational content around product use cases and comparisons