AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
OK, so here's what happened when ChatGPT started showing up in search results: my client's content basically disappeared. One day we're ranking nicely for B2C e-commerce keywords, getting steady organic traffic. The next day? Crickets. People were asking ChatGPT instead of Googling, and guess what - our content wasn't showing up in those AI responses.
This is the reality most businesses are facing right now. Traditional SEO is still important, sure, but if your content isn't visible to large language models like ChatGPT, Claude, and Perplexity, you're missing out on a massive chunk of visibility. I learned this the hard way when working with an e-commerce client who needed a complete SEO overhaul.
The crazy part? While everyone was panicking about "SEO being dead," I discovered that the fundamentals hadn't changed - they just needed to be adapted for how AI systems consume and surface content. After months of experimentation, I cracked the code on what I call "LLM content visibility" - making your content not just search-engine friendly, but AI-model friendly.
Here's what you'll learn from my real-world experiments:
Why traditional SEO tactics fail with AI-powered search
The chunk-level thinking approach that gets your content cited by LLMs
How I increased LLM mentions from zero to dozens per month for a client
The 5-layer optimization framework that works for both search engines AND AI models
Practical tactics you can implement today to future-proof your content strategy
This isn't theory - it's what actually worked when I had to solve this problem for a paying client. And spoiler alert: the solution wasn't abandoning SEO for flashy new "GEO" tactics.
Reality Check
What the industry is getting wrong about AI search
If you've been following the marketing world lately, you've probably heard about GEO (Generative Engine Optimization) being the "future of SEO." Every guru is screaming about how you need to completely abandon traditional SEO and optimize specifically for ChatGPT responses.
Here's what the industry typically recommends:
Forget keyword research - just write conversational content that answers questions
Optimize for featured snippets - because that's what LLMs supposedly prefer
Use more natural language - write like you're talking to ChatGPT directly
Focus on question-based content - create FAQ-style pages for everything
Ignore technical SEO - AI can understand context without perfect technical optimization
Look, I get why this advice exists. AI models do process information differently than traditional search crawlers. But here's the problem: most of this guidance comes from people who haven't actually tested what works at scale with real businesses.
The conventional wisdom exists because everyone sees ChatGPT giving conversational answers and assumes that's how you should write content. They're thinking in terms of human conversation rather than how AI systems actually consume and synthesize information from millions of sources.
Where this falls short in practice is simple: you're still competing with millions of other pieces of content. Writing conversational content doesn't guarantee visibility. In fact, I've seen plenty of "AI-optimized" content that gets zero mentions because it lacks the authority signals and structural elements that both search engines AND AI models actually value.
The real shift isn't about choosing between SEO and GEO - it's about understanding that LLMs are just another way content gets discovered and consumed. The fundamentals of creating authoritative, well-structured, valuable content haven't changed. They just need to be applied differently.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Let me tell you about the project that opened my eyes to this whole LLM visibility thing. I was working with a B2C Shopify e-commerce client who needed a complete SEO overhaul. Standard stuff - their site had virtually no organic traffic despite having a solid product catalog.
This was right around the time when AI-powered search was becoming mainstream. While implementing our SEO strategy, something weird started happening. The client mentioned they were getting questions from customers who said they "found us through ChatGPT" - but we couldn't track these visits in our analytics.
That's when I realized we had a tracking problem, but also an opportunity. Even though we were in a traditional e-commerce niche where you wouldn't expect heavy LLM usage, we were actually getting mentioned in AI responses. Not consistently, not frequently, but it was happening naturally as a byproduct of our content improvements.
This discovery sent me down a rabbit hole. I started testing different content approaches, tracking mentions across ChatGPT, Claude, and Perplexity. What I found was fascinating: the content that performed well in traditional search also tended to get picked up by AI models, but only when it was structured in a specific way.
The conventional approach would have been to panic and completely rebuild our strategy around "AI-first" content. Instead, I took a different route. I started treating LLM visibility as an additional layer on top of solid SEO fundamentals, not a replacement for them.
The client's main challenge was that their existing content was too generic and shallow. They had product pages, sure, but no real depth or expertise demonstration. When AI models were looking for authoritative sources to cite, we weren't even in the running because our content didn't establish credibility or provide unique insights.
Through conversations with teams at AI-first startups, I realized everyone was still figuring this out. There was no definitive playbook. The landscape was evolving too quickly to bet everything on tactics that might be obsolete in six months. That's when I developed what I call the "foundation-first" approach to LLM content visibility.
Here's my playbook
What I ended up doing and the results.
After months of testing and client implementation, here's the exact system I developed for maximizing content visibility across both traditional search and AI models. I call it the "foundation-first" approach because it builds LLM optimization on top of proven SEO principles.
Layer 1: Content Authority Foundation
First, I had to solve the credibility problem. AI models don't just randomly pick content to cite - they favor sources that demonstrate expertise and authority. For my e-commerce client, this meant creating genuinely useful content that went beyond basic product descriptions.
I implemented a knowledge base approach where we documented industry-specific insights that competitors couldn't replicate. This wasn't generic "how to" content - it was specific expertise that only someone with deep product knowledge could provide. The key was making each piece of content stand alone as valuable, while connecting to the broader business context.
Layer 2: Chunk-Level Content Architecture
Here's where it gets interesting. LLMs don't consume pages the way search engines do - 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.
Instead of writing long-form articles that required reading from start to finish, I created modular content where each heading and paragraph provided complete value on its own. If someone only read one section, they'd still get a complete, actionable insight.
Layer 3: Citation-Worthiness Optimization
I discovered that AI models favor content with certain characteristics: factual accuracy, clear attribution, logical structure, and specific examples. So I optimized for these elements explicitly.
Every claim needed to be specific and verifiable. Every recommendation included context about when and why it works. Every example provided enough detail that someone could actually implement it. This made the content naturally more citation-worthy because AI models could confidently reference it.
Layer 4: Multi-Modal Integration
AI models are getting better at processing different content types, so I started integrating charts, tables, and visuals strategically. Not just for human readers, but as additional signals for AI processing.
Data tables became particularly effective. When we included pricing comparisons, feature matrices, or step-by-step processes in table format, these frequently got pulled into AI responses. The structured data was easier for models to process and present.
Layer 5: Cross-Platform Visibility Testing
Finally, I implemented a systematic approach to track mentions across different AI platforms. This wasn't just vanity metrics - different models seemed to favor different content characteristics, and tracking helped optimize for broader visibility.
The breakthrough came when I realized this wasn't about gaming AI algorithms - it was about creating content so useful and well-structured that both humans and AI systems naturally wanted to reference it. The techniques that worked for traditional SEO (authority, relevance, user value) still applied, just with some additional considerations for how AI models process information.
Authority Signals
Build credibility that both search engines and AI models recognize through industry expertise and unique insights
Modular Structure
Design content in self-contained chunks that work as standalone snippets in AI responses
Citation Standards
Include specific examples, clear attribution, and verifiable claims that AI models can confidently reference
Systematic Testing
Track mentions across multiple AI platforms to identify what content characteristics drive the most visibility
The results from this approach were pretty interesting, though I should mention that LLM mentions are still a relatively new metric compared to traditional SEO KPIs.
For my e-commerce client, we went from zero trackable LLM mentions to several dozen per month within about three months of implementing this system. More importantly, the content improvements that made us more citation-worthy also improved our traditional search performance.
The organic traffic growth was significant - we scaled from under 500 monthly visitors to over 5,000 in the same timeframe. But what was really interesting was the quality improvement. The visitors coming from both traditional search and AI-influenced discovery were more engaged and had higher conversion intent.
The timeline was interesting too. Traditional SEO improvements typically take 3-6 months to show results, but LLM mentions started appearing within 4-6 weeks of publishing well-structured content. AI models seem to discover and process new content faster than traditional search crawlers.
One unexpected outcome was how this approach improved our content creation efficiency. By thinking in terms of modular, citation-worthy chunks, we could repurpose sections across multiple pieces while maintaining quality. A single well-researched insight could provide value in several different contexts.
The cross-platform testing revealed some interesting patterns too. Different AI models seemed to favor different content characteristics - ChatGPT liked conversational explanations with examples, while Claude preferred more structured, analytical content. Perplexity seemed to favor content with clear sourcing and attribution.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After implementing this system across multiple client projects, here are the key lessons that actually matter in practice:
1. Foundation beats optimization tricks
Don't abandon proven SEO fundamentals for shiny new "GEO" tactics. The best LLM-visible content is built on solid authority and expertise signals that search engines already value.
2. Structure for synthesis, not just reading
AI models break your content into pieces and recombine it with other sources. Make sure each section provides complete value even when taken out of context.
3. Specificity wins over generality
Generic advice doesn't get cited. Specific examples, detailed processes, and unique insights are what make content citation-worthy for both humans and AI.
4. Test across multiple platforms
Different AI models have different preferences. What works for ChatGPT might not work for Claude or Perplexity. Track mentions across platforms to optimize broadly.
5. Quality scales better than quantity
One piece of well-researched, expertly-written content gets cited more than ten generic articles. Focus on depth and authority over content volume.
6. Technical SEO still matters
LLM crawlers still need to access and understand your content. Don't neglect technical fundamentals like site speed, mobile optimization, and clean code structure.
7. Measure leading indicators, not just citations
Track content depth, expertise demonstration, and user engagement as leading indicators of citation-worthiness, not just final mention counts.
What I'd do differently next time is start tracking LLM mentions earlier in the process. It took me several months to realize this was happening organically, and I could have optimized for it sooner. Also, I'd invest more in creating visual content formats that AI models can process - infographics, charts, and data visualizations seem to have strong citation potential.
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 LLM content visibility:
Document your unique processes and methodologies in detail
Create comprehensive use case studies with specific metrics
Build comparison tables and feature matrices
Structure API documentation as citable references
For your Ecommerce store
For e-commerce stores wanting to increase AI model mentions:
Create detailed product comparison guides with data tables
Document industry expertise through buying guides and tutorials
Build comprehensive FAQ sections with specific scenarios
Include technical specifications in structured formats