AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Six months ago, one of my e-commerce clients was drowning in the traditional SEO game. They had decent traffic—around 300 monthly visitors—but were fighting for scraps in an oversaturated market. Then something unexpected happened: their content started appearing in ChatGPT responses, even though we weren't optimizing for it.
That discovery changed everything. While everyone was still fighting over the same Google real estate, we pivoted to what I call GEO—Generative Engine Optimization. The result? We went from 300 to 5,000+ monthly visitors in three months, with most of our growth coming from AI-generated search responses.
Here's the uncomfortable truth: traditional SEO is becoming a red ocean. Everyone's fighting for the same keywords, using the same tactics, creating the same content. Meanwhile, language models like ChatGPT, Claude, and Perplexity are quietly reshaping how people discover content.
In this playbook, you'll learn:
Why optimizing for language model discoverability beats traditional SEO
The exact framework I used to get my client featured in AI responses
How to structure content for chunk-level retrieval
The metrics that actually matter in the GEO era
Why this approach works even if you have zero domain authority
This isn't about abandoning SEO—it's about getting ahead of the curve while everyone else is still playing the old game. Let me show you exactly how we did it.
Industry Reality
What the "experts" are still preaching
Walk into any SEO conference today, and you'll hear the same tired advice: "Create quality content," "build backlinks," "optimize for featured snippets." The SEO industry is stuck in 2019, treating Google like the only game in town.
Here's what conventional wisdom tells you to focus on:
Keyword research with traditional tools - Everyone's using Ahrefs and SEMrush to fight over the same oversaturated keywords
Domain authority building - Spending months chasing backlinks from "high-authority" sites that barely move the needle
Google-first optimization - Structuring content solely for Google's algorithm, ignoring how AI systems actually process information
Volume-based metrics - Obsessing over search volume numbers that don't account for AI-driven search behavior
Traditional content formats - Blog posts optimized for human readers, not AI comprehension
This approach made sense when Google was the only way people found content. But here's the reality check: 40% of Gen Z now uses TikTok and ChatGPT as their primary search engines. Yet most businesses are still optimizing like it's 2015.
The problem with this conventional approach? You're competing in an increasingly crowded space where the cost of entry keeps rising. Meanwhile, language model discoverability is still wide open—a blue ocean where smart businesses can dominate with the right strategy.
The SEO industry's biggest blind spot is assuming that Google's algorithm changes are the only thing that matters. But the real disruption isn't coming from another Google update—it's coming from an entirely different way people discover and consume information.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The breakthrough came while working with a B2C Shopify client who needed a complete SEO overhaul. They were in a traditional e-commerce niche—nothing fancy, just physical products—but something interesting was happening that nobody talks about in SEO circles.
Even in their traditional niche, we tracked a couple dozen LLM mentions per month. This wasn't something we initially optimized for—it happened naturally as a byproduct of solid content fundamentals. But when I dug deeper, I realized we were sitting on something huge.
The traditional SEO approach wasn't working. We'd tried the usual playbook: keyword research with expensive tools, building backlinks, optimizing for featured snippets. The results were mediocre at best. We were burning budget competing with established players who had years of domain authority.
That's when I started paying attention to where our traffic was actually coming from. Buried in the analytics, I found visitors arriving with search patterns that didn't match our targeted keywords. They were asking questions in natural language—complete sentences, conversational queries, exactly the type of input people use with ChatGPT.
I realized something the SEO industry was missing: Language models don't consume content the same way Google does. They break content into passages and synthesize answers from multiple sources. This meant our entire content strategy needed restructuring.
The "aha" moment came when I started testing content specifically designed for AI comprehension. Instead of optimizing for search engines, I optimized for how language models actually process and retrieve information. The results were immediate and dramatic.
While our competitors were still fighting over traditional keywords, we were building a completely different type of visibility—one that worked regardless of domain authority or backlink profile.
Here's my playbook
What I ended up doing and the results.
Instead of abandoning traditional SEO for shiny new GEO tactics, I developed a layered approach that builds on solid fundamentals while preparing for the AI-driven future.
Layer 1: Foundation-First Strategy
I started with traditional SEO best practices, but with a twist. Every piece of content needed to work for both search engines and language models. This meant creating genuinely useful content for humans while structuring it for AI comprehension.
Layer 2: Chunk-Level Optimization
Here's where it gets interesting. Language models don't process pages—they process chunks. I restructured all content so each section could stand alone as a valuable snippet. Every paragraph needed to be self-contained and contextually complete.
I implemented five key optimizations:
Chunk-level retrieval - Making each section self-contained and valuable independently
Answer synthesis readiness - Logical structure that AI can easily extract and combine
Citation-worthiness - Factual accuracy and clear attribution that language models trust
Topical breadth and depth - Covering all facets of topics in comprehensive ways
Multi-modal support - Integrating charts, tables, and visuals that enhance AI understanding
Layer 3: Natural Language Optimization
Instead of keyword stuffing, I focused on how people actually ask questions. I analyzed conversational search patterns and optimized for complete question-answer pairs. This approach naturally aligned with how language models understand and respond to queries.
Layer 4: Authority Building (The New Way)
Traditional backlink building wasn't working, so I focused on becoming the definitive source for specific topics. This meant creating comprehensive, factually accurate content that language models would naturally reference and cite.
The key insight? Language models reward depth and accuracy over domain authority. A well-structured, comprehensive article on a new domain can outrank established sites if it better serves the user's information need.
Chunk Structure
Each content section must work independently while contributing to the larger narrative flow
Natural Language
Optimize for how people actually ask questions, not traditional keyword targets
Authority Signals
Focus on factual accuracy and comprehensive coverage over traditional backlink metrics
Multi-Modal Content
Integrate visuals and structured data that enhance AI comprehension and citation
The results spoke for themselves. We went from around 300 monthly visitors to over 5,000 in just three months. But the numbers tell only part of the story.
Traffic Quality Transformation: The visitors coming through AI-driven discovery were more engaged and better qualified. Time on page increased by 40%, and bounce rate dropped significantly. These weren't just any visitors—they were people with specific intent who found exactly what they needed.
Competitive Advantage: While competitors were still fighting over traditional keywords, we owned entirely new search territories. We started ranking for long-tail, conversational queries that didn't even show up in traditional keyword tools.
Unexpected Discovery: The content optimized for language models also performed better in traditional search. Google's algorithm is moving toward natural language understanding, so our GEO optimization actually improved our traditional SEO performance.
The most surprising result? We achieved this without a single new backlink. Traditional SEO wisdom says you need months of link building to see results. But language models care more about content quality and structure than domain authority.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After implementing GEO across multiple client projects, here are the key lessons that will save you months of trial and error:
Foundation matters more than tactics - Don't abandon SEO fundamentals. Build your GEO strategy on top of solid basics, not instead of them
Content depth beats keyword density - Language models reward comprehensive, accurate information over keyword optimization
Structure for machines, write for humans - The best content serves both AI comprehension and human readability
Natural language is the new keyword research - Focus on how people actually ask questions, not search volume metrics
Authority comes from accuracy, not age - New sites can compete immediately if they provide better, more comprehensive information
Test everything - The landscape changes fast. What works today might not work tomorrow, so build testing into your process
Track the right metrics - Traditional SEO metrics don't capture GEO success. Focus on engagement, citation frequency, and answer inclusion rates
The biggest lesson? This isn't a replacement for traditional SEO—it's the evolution of it. The businesses that adapt first will dominate while others are still playing catch-up.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies looking to implement language model discoverability:
Create comprehensive help documentation that AI can easily parse and cite
Optimize feature explanations for natural language queries
Structure use case content as complete question-answer pairs
Focus on integration guides that work as standalone resources
For your Ecommerce store
For ecommerce stores wanting to leverage GEO strategies:
Optimize product descriptions for conversational search queries
Create buying guides structured as comprehensive, citable resources
Develop comparison content that AI can easily extract and synthesize
Structure FAQ sections to work as independent information chunks