AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
When I was working with an e-commerce Shopify client last year, something unexpected happened. Despite being in a traditional niche where you wouldn't expect AI usage, we started tracking mentions in LLM responses. This wasn't part of our initial SEO strategy – it happened naturally as a byproduct of solid content fundamentals.
But here's the thing: while traditional SEO is getting more competitive by the day, most businesses are completely ignoring the next frontier – Generative Engine Optimization (GEO). ChatGPT, Claude, Perplexity, and other AI systems are becoming primary information sources, yet 99% of companies haven't even thought about optimizing for them.
Through my client work and conversations with teams at AI-first startups like Profound and Athena, I've learned that everyone is still figuring this out. There's no definitive playbook yet. But that's exactly why this is such a massive opportunity.
In this playbook, you'll discover:
Why AI systems mention certain content and ignore others
The five key optimizations that actually move the needle for LLM visibility
How to build on traditional SEO foundations without starting from scratch
Real examples of content structures that AI systems prefer
The measurement framework for tracking your GEO performance
If you're still only thinking about Google rankings while your competitors start getting mentioned in ChatGPT conversations, you're already behind. Let me show you how to get ahead.
Industry Reality
What most SEO experts are still recommending
Walk into any SEO conference or browse through the latest "SEO trends for 2025" articles, and you'll hear the same tired advice. Everyone's still obsessing over the same traditional metrics – keyword rankings, backlink profiles, domain authority, and Core Web Vitals.
The industry standard approach focuses on five main areas:
Keyword research and targeting: Find high-volume, low-competition keywords and build content around them
Technical SEO optimization: Improve site speed, mobile responsiveness, and crawlability
Content depth and authority: Create comprehensive, long-form content that covers topics exhaustively
Link building campaigns: Acquire high-authority backlinks through outreach and content promotion
User experience signals: Optimize for click-through rates, dwell time, and bounce rates
This conventional wisdom exists because it worked incredibly well for the past decade. Google's algorithm rewarded these signals, and businesses saw predictable returns on their SEO investments. The frameworks are proven, the tools are mature, and there's extensive case study data supporting these approaches.
But here's where this traditional thinking falls short: it assumes search behavior isn't changing. While SEO experts debate whether to target "best project management software" or "project management tools comparison," millions of users are already asking ChatGPT, "What's the best project management tool for a 10-person startup with remote workers?"
The fundamental shift isn't just about new platforms – it's about intent. AI systems answer specific, contextual questions that people would never type into Google. They provide synthesized answers from multiple sources, not just lists of links. And most importantly, they're becoming the first stop for research, not the last.
Traditional SEO is still valuable, but it's becoming table stakes. The real opportunity lies in understanding how AI systems process, synthesize, and cite information – and optimizing for that entirely different paradigm.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Let me tell you about the moment I realized everything was changing. I was working with this e-commerce Shopify client on what seemed like a straightforward SEO project. Traditional niche, older demographic, nothing fancy. We were focused on the usual suspects – improving product page optimization, building out collection pages, creating buying guides.
About three months into the project, I decided to do something different. Instead of just tracking Google rankings, I started monitoring whether our content was showing up in AI responses. I'd search for topics related to their products in ChatGPT, Claude, and Perplexity, just to see what happened.
That's when I found something surprising. Even in this traditional e-commerce niche where you wouldn't expect heavy LLM usage, we were getting mentioned in AI responses. Not consistently, and not for every topic, but it was happening. A couple dozen mentions per month across different AI platforms.
The weird part? This wasn't something we'd optimized for. It was happening naturally as a byproduct of the solid content fundamentals we'd implemented. But it got me thinking – if we were accidentally getting mentioned, what would happen if we did this intentionally?
I started reaching out to teams at AI-first startups like Profound and Athena, people who were already thinking about this problem. The universal response was the same: "We're all figuring this out as we go." There was no playbook, no established best practices, no clear framework.
That's when I realized we were looking at the biggest opportunity in content marketing since the early days of SEO. While everyone else was fighting over the same Google keywords, there was this entirely new frontier that barely anyone was thinking about.
The challenge was clear: how do you optimize for systems that work completely differently from traditional search engines? Google crawls pages and ranks them based on authority and relevance. But AI systems break content into passages, synthesize information from multiple sources, and create original responses. It's a fundamentally different game.
Here's my playbook
What I ended up doing and the results.
Rather than abandoning traditional SEO for shiny new tactics, I developed what I call a "layered GEO approach" – building AI optimization on top of strong SEO fundamentals, not instead of them.
Here's the step-by-step system I implemented:
Step 1: Foundation Assessment
First, I audited what we already had. LLM robots still need to crawl and index your content, so traditional SEO isn't dead – it's the foundation. I made sure we had solid technical SEO, quality content, and proper site structure before adding the GEO layer.
Step 2: Chunk-Level Content Restructuring
This was the game-changer. AI systems 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.
Instead of writing flowing narratives, I created modular content blocks:
Self-contained sections with clear headings
Each paragraph answering a specific sub-question
Key information summarized at the beginning of each section
Context provided within each chunk so it makes sense independently
Step 3: Answer Synthesis Readiness
I analyzed how AI systems structure their responses and optimized our content to match those patterns. This meant organizing information logically, using clear cause-and-effect relationships, and providing specific, actionable details rather than vague concepts.
Step 4: Citation-Worthiness Implementation
AI systems are more likely to cite sources that are factually accurate and clearly attributed. I focused on:
Including specific data points and statistics
Citing original research and studies
Providing step-by-step methodologies
Using precise language rather than marketing fluff
Step 5: Multi-Modal Content Integration
I expanded beyond text to include structured data that AI systems could easily parse: comparison tables, step-by-step processes, numbered lists, and clear categorization systems.
The key insight was this: don't abandon what works, build on it. The fundamentals that made content valuable for human readers and search engines also made it valuable for AI systems. The difference was in the structure and presentation, not the underlying quality.
Chunk Strategy
Breaking content into self-contained, AI-digestible sections that can stand alone while providing complete context and value.
Citation Framework
Building factual accuracy and clear attribution into every piece of content to increase the likelihood of being referenced by AI systems.
Synthesis Readiness
Structuring information with logical flow and clear relationships that AI can easily parse and reorganize into coherent responses.
Multi-Modal Integration
Combining text with structured data formats (tables, lists, processes) that AI systems prefer for generating comprehensive answers.
The results were more encouraging than I expected, though I'll be honest – this isn't about overnight transformations. GEO is a long-term strategy that compounds over time.
Within the first month of implementing our chunk-level restructuring, I noticed our content starting to appear more frequently in AI responses. What used to be a couple dozen mentions per month grew to consistent visibility across multiple AI platforms.
The most significant change was in the quality of mentions. Instead of just getting our brand name dropped in passing, AI systems were actually referencing our specific methodologies and citing our step-by-step processes. When someone asked about our niche topics, our content was being synthesized into comprehensive answers.
But here's what really convinced me this approach was working: I started seeing traffic from sources I couldn't easily attribute. People were finding our website after AI conversations, even though there's no direct referral data from ChatGPT or Claude. The content was doing its job as a discovery mechanism.
The compound effect became clear after three months. Our traditional SEO metrics improved too – the chunk-level content structure actually made our pages more valuable for human readers, which Google rewarded with better rankings. We weren't just optimizing for AI; we were creating better content across the board.
By month six, we'd established a consistent presence in AI responses for our target topics. More importantly, we'd built a framework for creating new content that worked for both traditional search and AI systems from day one.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After implementing this approach across multiple client projects, here are the seven critical lessons I've learned about GEO:
Foundation first, optimization second. You can't skip traditional SEO fundamentals. AI systems still need to find and index your content through traditional crawling mechanisms.
Context is everything in chunks. Each section needs to be self-contained because AI systems extract passages independently. Don't assume readers have context from earlier sections.
Factual accuracy beats marketing speak. AI systems heavily weight authoritative, fact-based content. Save the promotional language for your sales pages.
Structure matters more than length. A well-organized 800-word article often outperforms a rambling 3,000-word piece in AI citations.
Test across multiple AI platforms. Each system has different preferences. What works for ChatGPT might not work for Claude or Perplexity.
Measurement requires creativity. Traditional analytics don't capture AI-driven traffic. You need to track brand mentions, topic association, and indirect attribution.
The landscape evolves quickly. What works today might change in six months. Build flexible content systems, not rigid optimization tactics.
The biggest mistake I see teams making is treating GEO like a completely separate strategy from SEO. The smartest approach is integration – using AI optimization to make your existing content more valuable for all audiences, not just AI systems.
This is still the early days. Most businesses haven't even started thinking about AI optimization, which means there's a massive first-mover advantage for companies that get this right now.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups looking to implement this approach:
Focus on feature explanation and use case documentation that AI can easily reference
Create comparison content structured as clear data points rather than marketing claims
Build integration guides and API documentation with step-by-step, contextual sections
For your Ecommerce store
For e-commerce stores implementing GEO strategies:
Structure product information as answerable questions (sizing, compatibility, use cases)
Create buying guides with modular sections that work as standalone advice
Develop comparison content that includes specific, factual product differentiators