AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last month, a client came to me frustrated. "ChatGPT never mentions our company when users ask about our industry," they said. "We have hundreds of blog posts, case studies, and resources, but it's like we don't exist in the AI world."
Sound familiar? You're not alone. While everyone's obsessing over traditional SEO, there's a new game in town: Generative Engine Optimization (GEO). And most businesses are losing without even knowing they're playing.
Through my work with an e-commerce client implementing AI-native content strategies, I discovered why LLMs overlook most business content—and more importantly, how to fix it. The problem isn't your content quality; it's how you structure information for AI consumption.
Here's what you'll learn from my real-world GEO implementation:
Why traditional SEO content fails with AI systems
The chunk-level optimization strategy that worked
How we restructured 20,000+ pages for LLM visibility
Specific techniques to make your content citation-worthy
Why breadth beats depth in the AI era
This isn't theory—it's a playbook from the trenches of implementing AI optimization strategies that actually move the needle.
Industry Reality
What the experts are getting wrong about LLM indexing
If you've been following the SEO community lately, you've probably heard the standard advice about GEO: "Focus on E-A-T," "Write longer content," "Add more schema markup." The problem? Most of this advice treats LLMs like fancy search engines.
Here's what the industry typically recommends:
Write authoritative, long-form content - The assumption being that LLMs prefer comprehensive articles
Optimize for featured snippets - Since that's what worked for Google
Focus on brand mentions and citations - Believing that authority signals matter most
Create FAQ sections - Thinking LLMs consume Q&A format better
Build more backlinks - Assuming traditional ranking factors apply
This conventional wisdom exists because marketers are applying old SEO frameworks to new technology. It makes sense—we understand how Google works, so we assume ChatGPT and Claude work similarly.
But here's where it falls short: LLMs don't crawl and rank like search engines. They consume information during training, break it into chunks, and synthesize answers from multiple sources. They don't care about your domain authority or how many backlinks you have. They care about information density, context, and how well your content answers specific questions.
The biggest misconception? That you can optimize for LLMs the same way you optimize for Google. You can't. LLMs need content structured for chunk-level retrieval, not page-level ranking. Most businesses are optimizing for the wrong system entirely.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When I started working with this e-commerce client, they had a massive content problem. Despite having comprehensive SEO strategies and thousands of indexed pages, they were invisible in AI-generated responses. Users would ask ChatGPT about their industry, and competitors would get mentioned—but never them.
The client operated in a traditional e-commerce niche with over 3,000 products across 8 languages. They'd invested heavily in traditional SEO and had decent organic traffic. But when we tracked LLM mentions, we found something telling: they were getting mentioned a couple dozen times per month, despite being leaders in their space.
My first instinct was to apply traditional SEO thinking. I looked at their content through the lens of authority, backlinks, and featured snippets. Everything looked solid on paper. Their domain authority was strong, content was comprehensive, and they had industry recognition.
But after diving deeper into how LLMs actually process information, I realized we were solving the wrong problem. The issue wasn't content quality or authority—it was information architecture. Their content was structured for human readers and search engines, not for AI consumption.
Here's what I discovered: LLMs don't read your content the way humans do. They break information into passages, analyze context at the chunk level, and synthesize answers from multiple sources. Our client's content was optimized for page-level SEO, but LLMs needed chunk-level optimization.
This led to a fundamental realization: we needed to rebuild their content strategy from the ground up, focusing on how AI systems consume and process information rather than how search engines rank pages.
Here's my playbook
What I ended up doing and the results.
Instead of tweaking existing content, I implemented a complete AI-native content strategy. This wasn't about adding a few tweaks—it was about restructuring how we created and organized information for AI consumption.
The Foundation: Chunk-Level Thinking
First, I restructured all content so each section could stand alone as a valuable snippet. Instead of writing long articles that built arguments progressively, we created self-contained chunks that could answer specific questions independently. Each section had clear context, relevant data, and actionable insights.
Answer Synthesis Readiness
I redesigned our content structure to make it easy for LLMs to extract and synthesize information. This meant:
Leading with direct answers before explaining context
Using consistent formatting for data points and statistics
Creating logical information hierarchies within each chunk
Adding clear attribution and source context
Citation-Worthiness Over Authority
Instead of focusing on domain authority, I concentrated on making our content citation-worthy. This meant ensuring factual accuracy, providing clear attribution, and structuring information so LLMs could easily reference it in responses.
Topical Breadth and Depth
Rather than creating a few comprehensive guides, we covered all facets of topics relevant to our industry. LLMs favor content that addresses multiple angles of a subject, so we created extensive topic coverage rather than just authoritative deep-dives.
Multi-Modal Integration
I integrated charts, tables, and visual data directly into our content structure. LLMs can reference structured data more easily than pure text, so we made our information more accessible through multiple formats.
The key insight: Traditional SEO optimizes for ranking; GEO optimizes for citation. We shifted from asking "How do we rank higher?" to "How do we become the source LLMs reference?"
Chunk Optimization
Each content section designed to stand alone as valuable, referenceable information
Citation Structure
Clear attribution and context to make content easily referenceable by AI systems
Topic Coverage
Comprehensive breadth across all industry facets rather than just deep authority pieces
Data Integration
Structured information through tables, charts, and multi-modal content formats
Within three months of implementing this AI-native content strategy, we started seeing significant changes in LLM visibility. The client went from occasional mentions to becoming a regular reference point for industry-related queries.
Most importantly, this wasn't just vanity metrics. We tracked actual business impact: users who discovered the client through AI-generated responses showed higher engagement rates and conversion potential than traditional organic traffic.
The approach proved that focusing on chunk-level optimization and citation-worthiness created more sustainable AI visibility than trying to game traditional ranking factors. Our content became genuinely useful to AI systems because it was structured for their consumption patterns.
An unexpected outcome: the content improvements also enhanced traditional SEO performance. By creating self-contained, valuable chunks, we improved user experience and reduced bounce rates across the board.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons from implementing GEO in a real business context:
Chunk-level beats page-level - Structure content so each section provides standalone value
Citation-worthiness trumps authority - Focus on being referenceable, not just authoritative
Breadth beats depth - Cover all angles of topics rather than just creating comprehensive guides
Context is crucial - Each content chunk needs clear context for AI understanding
Structure matters more than style - How you organize information affects AI consumption
Multi-modal content performs better - Integrate structured data, not just text
Traditional SEO principles still apply - GEO enhances rather than replaces good SEO
What I'd do differently: Start with GEO principles from day one rather than retrofitting existing content. The architectural changes required significant effort that could have been avoided with AI-native thinking from the beginning.
This approach works best for businesses with substantial content needs and the resources to implement systematic changes. It's less effective for companies with limited content or those in highly regulated industries where information structure is constrained.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies implementing GEO strategies:
Structure feature documentation as standalone, referenceable chunks
Create comprehensive use-case libraries covering all customer scenarios
Optimize integration guides for AI citation and reference
For your Ecommerce store
For e-commerce stores optimizing for LLM visibility:
Structure product information for AI consumption and comparison
Create comprehensive buying guides covering all customer questions
Optimize category pages as referenceable information sources