AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, I was helping an e-commerce client with what started as a traditional SEO project. Standard stuff - keyword research, content optimization, technical audits. But then something unexpected happened. We discovered their content was starting to appear in Claude responses, even though we'd never optimized for AI platforms.
This discovery led me down a rabbit hole of what's now called GEO (Generative Engine Optimization) - the practice of optimizing content for AI platforms like Claude, ChatGPT, and Perplexity. While everyone's still debating whether SEO is dead in the AI era, I decided to run real experiments.
Here's what I learned from tracking dozens of LLM mentions monthly and turning traditional content into AI-preferred answers. This isn't theory - it's what actually moved the needle when everyone else was still figuring out if this AI thing was just hype.
What you'll discover:
Why traditional SEO foundations matter more than new GEO tactics
The chunk-level thinking approach that got us mentioned consistently
How to structure content so AI platforms can actually use it
The attribution strategies that build long-term authority
Why quality beats manipulation in the AI era
Industry Reality
What everyone's saying about AI content optimization
If you've been following the marketing Twitter sphere lately, you've probably seen the flood of "GEO experts" promising to get your content featured in ChatGPT responses. The typical advice sounds something like this:
Optimize for conversational queries - Write content that answers questions the way people ask AI
Use natural language processing - Focus on semantic keywords and entity relationships
Create FAQ-style content - Structure everything as questions and answers
Target AI training data - Try to reverse-engineer what AI models were trained on
Abandon traditional SEO - Shift budget from search engines to AI optimization
This conventional wisdom exists because it feels logical. AI systems do process language differently than search engines. They do synthesize information from multiple sources. And yes, conversational queries are becoming more common.
But here's where the industry advice falls short: most of these tactics treat GEO like a completely separate discipline from SEO. The reality? After working with actual AI-generated mentions across multiple client projects, I discovered that strong SEO fundamentals aren't just helpful for GEO - they're absolutely critical.
The problem with abandoning traditional optimization is that AI systems still need to crawl, index, and understand your content before they can cite it. You can't shortcut your way to AI authority without building real authority first.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The GEO discovery happened by accident during what should have been a straightforward e-commerce SEO project. My client had a Shopify store with thousands of products, and we were implementing a comprehensive content strategy - the kind of AI-powered SEO workflow I'd developed for scaling content across multiple languages.
Three months into the project, the client mentioned something interesting: customers were finding them through "some AI chat thing" and specifically mentioning details that matched our recent content updates. This was in a traditional e-commerce niche where LLM usage wasn't common, so it caught my attention.
I started tracking this manually - searching for our client's brand and products across Claude, ChatGPT, and Perplexity. What I found was fascinating: we were getting a couple dozen LLM mentions per month, despite never specifically optimizing for AI platforms.
This wasn't some magic GEO strategy at work. It was happening naturally as a byproduct of solid content fundamentals. But it got me curious: if we were accidentally getting AI mentions, what would happen if we intentionally optimized for them?
The challenge was that nobody had a definitive playbook yet. Through conversations with teams at AI-first startups and other practitioners, I realized everyone was still figuring this out. There were theories and experiments, but no proven frameworks.
So I decided to treat our client's site as a testing ground. We had the perfect setup: solid SEO foundations, high-quality content, and the ability to track changes over time. The question was whether we could deliberately improve our AI platform visibility without sacrificing our traditional search performance.
Here's my playbook
What I ended up doing and the results.
Instead of abandoning SEO for shiny new GEO tactics, I took a layered approach. The foundation had to be rock-solid traditional optimization, then we'd add AI-specific enhancements on top.
Layer 1: Traditional SEO Excellence
First priority was ensuring our content met all the basics:
Comprehensive topic coverage with clear structure
High-quality, factually accurate information
Proper technical implementation and crawlability
Strong internal linking and topical authority
This wasn't optional. LLM robots still need to access and understand your content before they can cite it.
Layer 2: Chunk-Level Content Architecture
The breakthrough insight was that LLMs don't consume pages like traditional search engines. They break content into passages and synthesize answers from multiple sources. This meant restructuring content so each section could stand alone as a valuable snippet.
I implemented five key optimizations:
Self-contained sections: Each paragraph or section included enough context to be understood independently
Logical answer structure: Information was organized for easy extraction and synthesis
Citation-worthy accuracy: Every factual claim was verifiable and clearly attributed
Comprehensive coverage: We covered all facets of topics, not just surface-level information
Multi-modal integration: Charts, tables, and visuals were properly described and contextualized
Layer 3: Authority Signal Enhancement
AI systems seem to favor content from authoritative sources. We enhanced authority signals through:
Author expertise indicators and biographical context
Industry-specific credentials and experience markers
Citation of primary sources and original research
Cross-references to other authoritative content
The key was not trying to game the system, but making it easier for AI platforms to understand why our content was trustworthy and cite-worthy.
Chunk Strategy
Break content into self-contained sections that work independently - each paragraph should make sense without surrounding context
Authority Signals
Focus on demonstrating expertise through credentials and primary source citations rather than trying to game AI algorithms
Traditional Foundation
Never abandon SEO basics - AI systems still need to crawl and understand your content before they can cite it
Quality Over Tactics
Prioritize comprehensive accurate information over clever GEO tricks - authenticity wins long-term
The results weren't immediate, but they were measurable. Within 90 days of implementing the layered GEO approach, we tracked significant improvements:
AI Platform Mentions: Our monthly LLM mentions increased from around 24 to consistently above 60 across Claude, ChatGPT, and Perplexity. More importantly, the mentions were more comprehensive and accurate.
Traditional SEO Performance: Rather than hurting our search rankings, the chunk-level content restructuring actually improved our traditional SEO. Google seemed to favor the clearer, more comprehensive content structure.
Content Quality Metrics: Time on page increased by 23%, and our content started getting cited by industry publications more frequently. The emphasis on self-contained, authoritative sections made the content more valuable for human readers too.
The most interesting discovery was that the best GEO content was also the best human content. When we optimized for AI understanding and citation, we ended up creating more comprehensive, better-structured information that served everyone better.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After months of experimenting with GEO alongside traditional SEO, here are the key lessons that actually moved the needle:
Foundation matters more than tactics: Strong SEO fundamentals aren't just helpful for GEO - they're essential. You can't shortcut to AI authority.
Chunk-level thinking transforms content: Restructuring content so each section stands alone dramatically improved both AI mentions and human engagement.
Quality beats manipulation: Attempts to game AI algorithms backfire. Focus on comprehensive, accurate information instead.
Authority signals are crucial: AI platforms favor content from demonstrably expert sources. Invest in building real expertise markers.
Traditional and AI optimization align: The best GEO practices improve traditional SEO performance too. They're complementary, not competing strategies.
Patience is required: GEO results take 3-6 months to materialize. Don't expect immediate AI mentions from new content.
Tracking is essential: Without consistent monitoring across multiple AI platforms, you can't tell what's working. Manual tracking is still necessary.
The biggest mistake I see other practitioners making is treating GEO like a completely separate discipline. The future isn't SEO versus GEO - it's integrated optimization that serves both traditional search engines and AI platforms 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 improve Claude content visibility:
Build comprehensive feature documentation with self-contained sections
Create detailed integration guides that AI can easily parse and cite
Establish thought leadership through original research and industry insights
Optimize API documentation for both developers and AI understanding
For your Ecommerce store
For e-commerce stores targeting better AI platform mentions:
Develop detailed product guides that work as standalone resources
Create comprehensive buying guides with clear recommendations
Build industry expertise content around your product categories
Structure product information for easy AI extraction and synthesis