AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, while working on a complete SEO overhaul for an e-commerce client, something unexpected happened. Despite being in a traditional industry where LLM usage isn't common, we started tracking mentions in AI-generated responses from Claude, ChatGPT, and Perplexity. This wasn't something we initially optimized for—it happened naturally as a byproduct of solid content fundamentals.
This discovery led me down the rabbit hole of GEO (Generative Engine Optimization), and what I learned challenges everything we think we know about SEO in the AI era. While everyone's debating whether SEO is dead, I was quietly running experiments to understand how content actually gets surfaced in AI responses.
The reality? The foundation hasn't changed as much as the gurus want you to believe. But there's a crucial new layer that most businesses are completely missing. Here's what you'll discover in this playbook:
Why traditional SEO principles still matter more than GEO tactics
The real difference between optimizing for search engines vs. AI responses
My actual experiments with chunk-level content optimization
Specific techniques that got our content featured in Claude and ChatGPT
Why building for humans first beats gaming AI algorithms
This isn't another theoretical guide about the future of search—it's a hands-on report from the trenches of actually implementing AI-driven strategies that work today.
Industry Reality
What everyone's saying about SEO and AI
The SEO industry is in full panic mode about AI. Every conference, every LinkedIn post, every "expert" is screaming the same message: SEO is dead, traditional search is over, and you need to completely rebuild your content strategy for AI.
Here's what the conventional wisdom looks like right now:
Abandon traditional SEO: Focus entirely on optimizing for AI responses instead of search rankings
Restructure everything: Break all your content into "chunks" that AI can easily digest
Target conversational queries: Optimize for how people talk to AI, not how they search
Focus on featured snippets: Since AI pulls from these, make them your primary target
Use AI-first content: Write specifically for machine consumption, not human readers
This advice exists because there's genuine fear in the industry. Search volumes are shifting, users are getting answers directly from AI without clicking through to websites, and traditional metrics are becoming less reliable. The panic is real, and frankly, some of it is justified.
But here's where the conventional wisdom falls short: it assumes you have to choose between traditional SEO and GEO optimization. It treats them as competing strategies when they're actually complementary. Even worse, most of the "experts" pushing this advice haven't actually run experiments—they're just repeating what sounds logical.
The truth is more nuanced, and from my experience working across different industries, the businesses that succeed will be those that understand how to layer GEO on top of strong SEO fundamentals, not replace them entirely.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The discovery happened completely by accident. I was working with an e-commerce client on a traditional SEO overhaul—nothing fancy, just solid content fundamentals, technical optimization, and a focus on user intent. Their industry wasn't particularly tech-forward, and we weren't even thinking about AI optimization.
But about three months into the project, something interesting started showing up in our analytics. We began tracking a couple dozen LLM mentions per month. Our content was appearing in Claude responses, ChatGPT answers, and Perplexity summaries, despite being in a niche where LLM usage wasn't common.
This wasn't planned. We hadn't optimized for it. It was happening naturally as a byproduct of creating comprehensive, well-structured content that genuinely served user needs. That's when I realized the SEO industry might be overcomplicating the AI shift.
Curious about this unexpected result, I started reaching out to teams at AI-first startups like Profound and Athena. What I discovered was eye-opening: everyone is still figuring this out. There's no definitive playbook yet. The companies actually working on AI products were being much more cautious about dramatic claims than the SEO consultants selling "AI optimization" services.
This led me to run deliberate experiments. Instead of abandoning traditional SEO practices, I decided to test a layered approach—keeping the fundamentals that were already working and adding specific GEO optimizations on top. The goal was to understand what actually moves the needle versus what just sounds good in theory.
What I found challenged most of the conventional wisdom being pushed by the industry. The foundation principles that make content good for humans and search engines also make it good for AI consumption. The difference isn't in replacing one approach with another—it's in understanding how to optimize for both simultaneously.
Here's my playbook
What I ended up doing and the results.
Rather than following the industry's rush to abandon traditional SEO, I took a systematic approach to understanding how AI systems actually consume and surface content. Here's the exact framework I developed and tested:
Layer 1: Strengthen Traditional SEO First
The biggest mistake I see businesses making is trying to optimize for AI before they've mastered basic SEO fundamentals. AI systems like Claude still need to crawl and index your content—they're not magic. I started by ensuring our foundation was rock-solid:
Quality, relevant content that genuinely serves user intent
Proper technical SEO structure and site architecture
Clear, logical content hierarchy with appropriate heading tags
Fast loading times and mobile optimization
Layer 2: Implement Chunk-Level Optimization
Here's where it gets interesting. LLMs don't consume pages like traditional 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:
Each paragraph addresses a complete thought or answers a specific question
Key information is front-loaded in each section
Context is provided within each chunk, not just referenced from earlier sections
Important facts include clear attribution and sources
Layer 3: Optimize for Answer Synthesis
AI systems excel at combining information from multiple sources to create comprehensive responses. I focused on making our content "synthesis-ready" by:
Using clear, factual statements that can be easily extracted
Including specific data points, statistics, and concrete examples
Structuring information in logical, hierarchical formats
Ensuring accuracy and avoiding speculation or opinions presented as facts
Layer 4: Test and Measure AI Visibility
This is where most guides fail—they don't tell you how to actually track your progress. I developed a simple system for monitoring AI mentions:
Regular testing of key topics in Claude, ChatGPT, and Perplexity
Tracking which content gets cited and which doesn't
Analyzing the context in which our content appears in AI responses
Correlating traditional SEO metrics with AI visibility
The key insight from this approach: you don't abandon what works; you layer new optimizations on top. The content that performed well in traditional search also tended to get picked up by AI systems, but the GEO optimizations made it more likely to be featured prominently and accurately.
Foundation First
Start with solid SEO fundamentals before adding GEO tactics - AI systems still crawl and index like traditional search engines
Chunk Strategy
Structure content so each section can stand alone as a complete answer to a specific question
Testing Protocol
Regularly test key topics across Claude ChatGPT and Perplexity to track which content gets featured
Synthesis Ready
Make content easy for AI to extract and combine with other sources by using clear factual statements
The results from this layered approach were more impressive than I expected, though they reinforced my hypothesis that traditional SEO and GEO work together rather than against each other.
Quantitative Results:
Within six months of implementing this framework, we saw a significant increase in AI mentions. What started as a couple dozen LLM references per month grew substantially, but more importantly, the quality of citations improved. Our content wasn't just being mentioned—it was being featured as authoritative sources for specific topics.
Traditional SEO metrics remained strong throughout this process. Organic traffic continued to grow, and our search rankings for target keywords actually improved. This directly contradicts the narrative that you have to sacrifice traditional performance for AI optimization.
Unexpected Discoveries:
The most surprising result was how AI systems handled our chunk-optimized content. Rather than just pulling isolated snippets, AI responses often combined multiple sections from our content to create more comprehensive answers. This suggested that the synthesis-ready optimization was working exactly as intended.
We also noticed that certain types of content performed dramatically better in AI responses than others. Practical, how-to content with clear steps and specific examples got featured far more often than theoretical or opinion-based pieces. This insight shaped our content strategy going forward.
Perhaps most importantly, we discovered that AI citations often led to increased direct traffic to our website. Unlike the fear that AI would replace search entirely, we found that quality AI mentions actually drove more qualified visitors to our site.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After running these experiments across multiple client projects, here are the seven most important lessons that will save you time and prevent costly mistakes:
Don't abandon SEO fundamentals: Every business that tried to go "AI-first" without solid SEO foundations struggled. The fundamentals still matter more than fancy GEO tactics.
Focus on accuracy over optimization: AI systems are incredibly good at detecting and avoiding unreliable sources. Being factually correct beats being "optimized" every time.
Chunk-level thinking requires practice: Restructuring content to work both for humans and AI synthesis takes time to master. Start with your most important pages and learn the approach before scaling.
Test regularly but don't over-optimize: AI responses can vary significantly between sessions. Don't make dramatic content changes based on single tests.
Quality beats quantity for AI visibility: One piece of comprehensive, well-sourced content will get more AI citations than ten shallow pieces targeting the same topic.
Traditional metrics still predict AI success: Content that ranks well in traditional search is more likely to get featured in AI responses. Don't ignore your existing analytics.
This is a marathon not a sprint: Building AI visibility takes months not weeks. The businesses succeeding are those taking a systematic long-term approach rather than chasing quick wins.
The biggest mistake I see companies making is treating GEO as a replacement for SEO rather than an enhancement. The future belongs to businesses that can excel at both simultaneously.
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 this approach:
Focus on comprehensive feature documentation that can stand alone
Create use-case content that addresses complete workflows
Include integration guides with clear step-by-step instructions
Optimize API documentation for both developers and AI consumption
For your Ecommerce store
For e-commerce stores implementing this strategy:
Create detailed buying guides that can be easily extracted
Optimize product descriptions with complete specifications
Build category pages that thoroughly address buyer intent
Focus on local SEO that AI can reference for location-based queries