AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
While most marketers were obsessing over Google rankings and backlink strategies, I discovered something fascinating: one of my e-commerce clients was getting dozens of mentions in AI-generated responses - despite being in a niche where LLM usage isn't common.
This wasn't something we initially optimized for. It happened naturally as a byproduct of solid content fundamentals. But it opened my eyes to a completely new frontier: Generative Engine Optimization (GEO).
Everyone's talking about how AI will kill SEO, but what if I told you that AI mentions are becoming the new backlinks? That getting featured in ChatGPT responses could drive more qualified traffic than ranking #1 on Google for some keywords?
Here's what you'll learn from my real-world experience optimizing for AI mentions:
Why chunk-level thinking beats page-level optimization for AI visibility
The content structure that makes LLMs choose your information over competitors
How to track and measure AI mentions (spoiler: it's not what you think)
The surprising content types that perform best in AI responses
Why traditional SEO fundamentals are still your foundation for GEO success
Let me show you exactly how we went from zero AI mentions to consistent featured snippets in ChatGPT, Claude, and Perplexity - and why this matters more than you think for future-proofing your content strategy. Check out our other AI-powered marketing strategies for more insights.
Industry Reality
What every content marketer thinks they know about AI optimization
If you've been following the SEO industry lately, you've probably heard the same tired advice about preparing for the "AI revolution" in search. Here's what most experts are telling you:
Create "AI-friendly" content by writing in Q&A format
Focus on E-A-T signals (Expertise, Authoritativeness, Trustworthiness)
Optimize for featured snippets since AI models pull from them
Use structured data extensively to help AI understand your content
Target "zero-click searches" with comprehensive answers
This conventional wisdom exists because everyone's trying to reverse-engineer how AI models work by looking at traditional search patterns. The thinking goes: "If Google's AI uses featured snippets, then optimizing for featured snippets will help with AI mentions."
But here's where this approach falls short in practice: AI models don't consume content the same way search engines do. They're not looking at your page authority, backlink profile, or even your featured snippet optimization. They're processing information at a much more granular level - breaking content into passages and synthesizing answers from multiple sources simultaneously.
Most SEO professionals are still thinking in terms of "pages" and "rankings" when they should be thinking about "chunks" and "mentions." They're optimizing for visibility in a system that fundamentally works differently than the one they understand.
The biggest gap? Traditional SEO focuses on getting found, while GEO focuses on getting cited. That's a completely different optimization challenge that requires a completely different approach.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Last year, while working on a complete SEO overhaul for a B2C Shopify client, something unexpected happened. We started tracking mentions across different AI platforms - not because we were optimizing for them, but because I was curious about this emerging trend.
The client was in a traditional e-commerce niche where you wouldn't expect much LLM usage. But we discovered a couple dozen LLM mentions per month happening naturally. Users were asking AI assistants about products in their category, and our content was being referenced in the responses.
This wasn't some massive viral moment - it was consistent, steady mentions that were driving qualified traffic. What fascinated me was that these mentions had nothing to do with our traditional SEO metrics. Pages that ranked poorly on Google were being cited by ChatGPT. Content with low backlink counts was appearing in Claude responses.
I started digging deeper, having conversations with teams at AI-first startups like Profound and Athena. What I learned was eye-opening: everyone is still figuring this out. There's no definitive playbook yet. The landscape is evolving too quickly for anyone to claim they've "cracked the code."
But what became clear was that our client's content was getting picked up because of something we'd done right with the fundamentals - not because of any specific "AI optimization" tactics. The question became: how could we systematically replicate and scale this?
That's when I started developing what I call the "chunk-level optimization" approach. Instead of thinking about pages, I began thinking about how each section of content could stand alone as a valuable, citable snippet.
Here's my playbook
What I ended up doing and the results.
Here's exactly how I restructured our entire content approach to optimize for AI mentions, step by step:
Step 1: Content Audit Through an AI Lens
First, I manually tested our existing content by asking ChatGPT, Claude, and Perplexity questions related to our client's niche. I tracked which pieces of our content got mentioned and analyzed the patterns. What I found was fascinating: AI models were pulling from our FAQ sections, detailed product descriptions, and "how-to" guides - not our optimized landing pages.
Step 2: Implementing Chunk-Level Architecture
I restructured content so each section could function independently. Instead of writing traditional blog posts, I created content where:
Each paragraph answered a specific question completely
Every section included context and background
Information was factual, specific, and citation-worthy
Technical details were explained simply but thoroughly
Step 3: The Five Key Optimizations
Based on conversations with AI startup teams and my own testing, I implemented five core strategies:
Chunk-level retrieval: Making each section self-contained and valuable
Answer synthesis readiness: Structuring information for easy extraction and combination
Citation-worthiness: Ensuring factual accuracy and clear attribution
Topical breadth and depth: Covering all facets of topics comprehensively
Multi-modal support: Integrating charts, tables, and visuals that AI could reference
Step 4: Testing and Tracking
Unlike traditional SEO, tracking AI mentions required manual monitoring. I set up a system to:
Test key queries across different AI platforms weekly
Screenshot and document mentions with timestamps
Track which content types performed best
Monitor changes in mention frequency over time
The key insight? This wasn't about abandoning traditional SEO for shiny new GEO tactics. It was about layering GEO strategies on top of strong SEO fundamentals. The content that performed best in AI responses was already well-structured, comprehensive, and valuable for humans.
Content Structure
Each section must work as a standalone, citable piece of information that AI can extract and use independently.
Testing Protocol
Weekly manual queries across ChatGPT, Claude, and Perplexity to track mentions and document performance patterns.
Factual Foundation
AI models prioritize accurate, specific information with clear attribution over promotional or vague content.
SEO Integration
GEO works best when layered on top of solid traditional SEO fundamentals, not as a replacement strategy.
The results weren't dramatic overnight changes, but consistent, measurable improvements:
Mention Frequency: We went from sporadic, accidental mentions to consistent weekly citations across multiple AI platforms. Our client's content started appearing in responses for industry-specific queries where we hadn't even ranked well on Google.
Traffic Quality: The traffic coming from AI-influenced searches was notably higher quality. Users who found the client through AI recommendations had already been "pre-qualified" by the AI's understanding of their needs.
Content Performance: Specific content types emerged as clear winners. Detailed comparison guides, step-by-step tutorials, and comprehensive FAQ sections consistently outperformed promotional content for AI mentions.
Competitive Advantage: While competitors focused on traditional SEO, we were building visibility in an emerging channel. When potential customers asked AI assistants for recommendations, our client's content was increasingly being referenced.
What surprised me most was that some of our best-performing AI content had mediocre traditional SEO metrics. This reinforced that we were optimizing for a fundamentally different system with different ranking factors.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons learned from six months of GEO experimentation:
Foundation First: Don't abandon traditional SEO. The best AI-mentioned content was already well-structured and valuable for humans.
Patience Required: Unlike paid ads, GEO results compound slowly. Consistent mention building takes months, not weeks.
Manual Tracking Essential: There are no automated tools yet for comprehensive AI mention tracking. Manual monitoring is currently necessary.
Quality Over Quantity: AI models prioritize helpful, accurate information over content volume or keyword density.
Context Matters: Content that provides complete context in each section performs better than content requiring external links for understanding.
Multi-Platform Approach: Different AI models have different preferences. What works for ChatGPT may not work for Claude or Perplexity.
Future-Proofing Strategy: As AI search becomes more prevalent, early optimization gives a significant competitive advantage.
The biggest mistake I see companies making is treating GEO as a separate strategy from their overall content marketing. The most successful approach integrates AI optimization into existing content workflows rather than creating entirely new processes.
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 GEO:
Focus on comprehensive feature documentation and use case examples that AI can easily reference and cite
Create detailed integration guides that work as standalone resources
Develop comparison content that positions your solution contextually against alternatives
For your Ecommerce store
For e-commerce stores optimizing for AI mentions:
Build detailed product guides and buying advice that provide complete purchase context
Create comprehensive category explanations that help AI understand product relationships
Develop troubleshooting and support content that answers complete customer questions