AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
So I was working with this e-commerce client who had spent months perfecting their SEO strategy - beautiful content, perfect keywords, backlinks from all the right places. Their Google rankings were decent, but here's the thing that caught my attention: their content was starting to show up in ChatGPT responses even though they were in a super traditional industry where you wouldn't expect AI usage.
This discovery led me down a rabbit hole that completely changed how I think about content distribution. While everyone's still obsessing over Google's algorithm updates, there's a whole new content discovery system emerging that most businesses are completely ignoring.
The uncomfortable truth? Traditional SEO might get you found on Google, but if your content isn't optimized for AI systems like ChatGPT, you're missing out on an entirely new channel of discovery. And here's the kicker - the rules are completely different.
In this playbook, you'll discover:
Why traditional SEO tactics actually hurt your ChatGPT visibility
The content structure that AI systems favor (it's not what you think)
How to create "chunk-worthy" content that gets cited by AI
My framework for tracking AI mentions without expensive tools
Real examples from client work showing what actually works
Ready to future-proof your content strategy? Let's dive into the world of AI-optimized content that actually gets discovered.
Industry Reality
What most content creators are still getting wrong
If you've been in the content game for a while, you've probably heard the standard advice about optimizing for AI discovery. The typical recommendations go something like this:
Create comprehensive, authoritative content - Write long-form articles that cover every angle of a topic
Use structured data and schema markup - Help AI systems understand your content hierarchy
Focus on factual accuracy - Ensure all information is verifiable and well-sourced
Build topical authority - Create content clusters around your expertise areas
Optimize for featured snippets - Structure content to answer specific questions
This conventional wisdom exists because it's based on how traditional search engines work. Google rewards comprehensive content, authoritative sources, and clear information hierarchy. So naturally, everyone assumes AI systems like ChatGPT work the same way.
But here's where it gets interesting - and where most businesses are getting it completely wrong. AI systems don't consume content the same way search engines do. They don't crawl pages looking for keywords or analyzing backlink profiles. Instead, they break content into digestible chunks and synthesize information from multiple sources to generate responses.
The problem with following traditional SEO advice for AI optimization is that you end up creating content that's perfect for ranking on Google but terrible for being referenced by AI systems. Those 3,000-word comprehensive guides? They often get ignored because AI systems struggle to extract specific, quotable insights from walls of text.
Most content creators are still thinking in terms of pages and rankings when they should be thinking in terms of chunks and citations.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
This discovery happened while working on a complete SEO overhaul for a Shopify client - not exactly the type of business you'd expect to see mentioned in AI conversations. They were selling traditional products in a very non-tech industry, and their content strategy was pretty standard: product descriptions, buying guides, comparison articles.
What caught my attention was when the client mentioned that customers were asking about specific product details that seemed oddly specific - details that weren't prominently featured on their main product pages but were buried in their blog content. When I dug deeper, I discovered these customers were getting information from ChatGPT, and somehow our client's content was being referenced.
This was fascinating because according to everything I knew about SEO, this content shouldn't have been discoverable. It wasn't ranking on page one for any major keywords, didn't have significant backlinks, and was relatively new. Yet ChatGPT was pulling information from it and presenting it to users as authoritative.
I started tracking this more systematically and found that we were getting about two dozen LLM mentions per month - not huge numbers, but consistent enough to be meaningful. The weird part? The content that was getting referenced by AI wasn't necessarily the same content that was ranking well on Google.
This led me to a crucial realization: AI systems have their own discovery and indexing methods that operate independently of traditional search rankings. While Google looks at authority signals like backlinks and domain strength, AI systems seem to prioritize different factors entirely.
The content that was getting picked up by ChatGPT had specific characteristics - it was factual, well-structured, and broken into clear, standalone sections. Each paragraph could almost function as a complete answer on its own. This was completely different from our Google-optimized content, which was designed to keep users on the page and follow a narrative flow.
Here's my playbook
What I ended up doing and the results.
Once I understood that AI systems work differently, I had to completely rethink our content approach. Instead of optimizing for traditional search engines, I developed what I call "chunk-level optimization" - creating content specifically designed to be easily extracted and cited by AI systems.
The key insight was this: AI systems don't read pages, they consume chunks. Each section of your content needs to be self-contained and valuable on its own, because that's how AI systems will use it - as individual pieces of information to synthesize into responses.
Here's the exact framework I developed:
Step 1: Chunk-Ready Structure
I restructured all content so that each section could stand alone as a complete answer. Instead of writing flowing narratives, I created modular information blocks. Each paragraph had a clear topic sentence, supporting details, and a conclusion that could be quoted independently.
For example, instead of writing "When considering product features, you should think about..." I would write "The three critical features for X product are: durability, compatibility, and price. Durability matters because..." This gives AI systems a clear, quotable statement that includes context.
Step 2: Answer Synthesis Readiness
I optimized content for how AI systems actually use information - to synthesize answers from multiple sources. This meant including logical connectors, clear attribution, and structured reasoning that AI could easily follow and combine with other sources.
The content needed to play nicely with other information sources. Instead of trying to be the definitive answer, each piece of content was designed to be a valuable component that could be combined with other sources to create comprehensive responses.
Step 3: Citation-Worthy Formatting
I discovered that AI systems favor content that's already formatted for citation. This means clear headings, numbered lists, and factual statements that can be easily attributed. The goal was to make it as easy as possible for AI to extract and cite our content.
Step 4: Multi-Modal Integration
While working with the e-commerce client, I found that content with supporting data - charts, tables, specifications - was more likely to be referenced. AI systems seem to value content that provides concrete, verifiable information alongside explanatory text.
The implementation was straightforward but required completely changing how we thought about content creation. Instead of asking "How do we rank for this keyword?" we started asking "How do we create information that AI would want to reference?"
Content Structure
Each section must work as a standalone answer that AI can easily extract and quote
Technical Setup
Focus on factual accuracy and clear attribution rather than keyword density
Distribution Strategy
Build content that works well when combined with other sources in AI responses
Tracking System
Monitor AI mentions through direct testing rather than traditional analytics tools
The results were honestly surprising. Within three months of implementing this chunk-level optimization approach, we saw our AI mentions increase from about 24 per month to over 60. But here's what was really interesting - our Google rankings actually improved too.
It turns out that content optimized for AI citation often performs better in traditional search as well. The clear structure, factual accuracy, and logical flow that AI systems love also happens to align with what Google considers high-quality content.
The timeline looked like this: Month 1 showed minimal change as the new content was still being indexed. Month 2 showed the first significant increase in AI mentions. By Month 3, we had more than doubled our AI visibility, and Google rankings for several key terms had improved as well.
But the most unexpected outcome was the quality of traffic. Users who found the business through AI-generated responses were more qualified and had higher conversion rates. This makes sense when you think about it - they were getting specific, detailed information that matched their exact queries rather than generic search results.
We also discovered that AI mentions had a compound effect. Once content started getting referenced by AI systems, it seemed to build momentum and get referenced more frequently. This suggests that AI systems may have some form of quality signaling that helps determine which sources to prioritize.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
This experience completely changed how I approach content strategy. Here are the most important lessons I learned:
AI indexing operates independently of search rankings - Content that performs well in ChatGPT responses isn't necessarily the same content that ranks well on Google
Chunk-level thinking is essential - Each section of your content needs to be valuable and quotable on its own
Citation-ready formatting matters - Clear headings, logical structure, and factual statements make content more AI-friendly
Quality compounds in AI systems - Once you start getting referenced, momentum builds quickly
AI-optimized content often improves traditional SEO too - The principles align more than you'd expect
User quality improves with AI discovery - People finding you through AI responses are often more qualified prospects
The landscape is still evolving rapidly - What works today may change as AI systems develop
If I were starting this project today, I'd put even more emphasis on creating modular, self-contained content from day one. The traditional approach of writing comprehensive guides is still valuable, but it needs to be combined with AI-specific optimization strategies.
The biggest mistake I see businesses making is trying to optimize for AI as an afterthought. This needs to be built into your content strategy from the beginning, not bolted on later.
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 creating feature explanations that work as standalone answers
Structure use case content for easy AI extraction and citation
Build integration guides with clear, step-by-step formatting
Create comparison content that presents factual information objectively
For your Ecommerce store
For ecommerce stores implementing this strategy:
Create product information that answers specific customer questions independently
Structure buying guides with clear, quotable recommendations
Build category content that provides standalone value for each section
Focus on factual product comparisons rather than promotional content