AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, I was working with an e-commerce Shopify client who needed a complete SEO overhaul. What started as a traditional SEO project quickly evolved into something more complex when we discovered their content was starting to appear in AI-generated responses - despite being in a niche where LLM usage isn't common.
Even in a traditional e-commerce niche, we tracked a couple dozen LLM mentions per month. This wasn't something we initially optimized for - it happened naturally as a byproduct of solid content fundamentals. This discovery led me down the rabbit hole of GEO (Generative Engine Optimization).
Through conversations with teams at AI-first startups like Profound and Athena, I realized everyone is still figuring this out. There's no definitive playbook yet. But there are patterns emerging, and I've tested what actually moves the needle.
Here's what you'll learn from my experience optimizing for Perplexity AI and other language models:
Why traditional SEO fundamentals are still your starting point for AI optimization
The specific content restructuring that gets you mentioned in AI responses
How to track and measure your AI visibility across different platforms
The tactical approach that worked for my client (and what didn't)
Why everyone who's betting everything on GEO tactics is making a mistake
Before diving into the tactics, let's address what the industry is currently preaching about ranking on Perplexity AI and why most of it misses the mark.
Industry Reality
What the GEO gurus are telling you
Most content about ranking on Perplexity AI and other language models falls into one of two camps: either it's overly theoretical or it's pushing aggressive optimization tactics that might be obsolete in six months.
The typical advice you'll find includes:
Write directly for AI consumption - Craft content specifically formatted for LLM parsing
Focus on featured snippet optimization - Structure everything like you're trying to win position zero
Abandon traditional SEO - Shift all resources to "AI-first" content strategies
Use specific prompt-style headers - Write headlines that mirror how people query AI tools
Optimize for "chunk-level retrieval" - Break everything into digestible snippets for AI systems
This conventional wisdom exists because everyone's trying to reverse-engineer how these systems work. The problem? We're treating GEO like it's a completely different game when it's actually an evolution of existing principles.
Here's where this approach falls short: most businesses following this advice are abandoning what already works (traditional SEO fundamentals) for tactics that might change overnight. The landscape is evolving too quickly to bet everything on optimization techniques that could become irrelevant.
My approach? Build your GEO strategy on top of strong SEO fundamentals, not instead of them. Let me show you what this looks like in practice.
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 Shopify client, the brief was straightforward: overhaul their SEO strategy and improve organic visibility. They had solid products but virtually no search presence.
I was deep into the traditional SEO work - keyword research, content optimization, technical audits - when I decided to check something unusual. On a whim, I started testing whether their brand or products were appearing in AI-generated responses.
What I found surprised me. Despite being in a niche where most people don't typically use ChatGPT or Perplexity for research, we were getting mentioned a couple dozen times per month across various AI platforms. This wasn't from any deliberate AI optimization - it was happening organically.
That's when I realized we had stumbled into something bigger. Through conversations with teams at AI-first startups like Profound and Athena, I learned that everyone is still figuring this out. There's no definitive playbook yet for ranking on Perplexity AI or other language models.
But here's what became clear: the mentions we were getting weren't random. They correlated with our strongest, most comprehensive content pieces. The AI systems were finding and citing our content because it met certain quality and structure criteria.
Rather than abandoning our traditional SEO approach, I decided to layer GEO optimization on top of it. This meant keeping all the fundamentals that were working while adding new elements specifically designed for AI consumption.
The first challenge was measurement. How do you track "rankings" in systems that don't have traditional SERPs? I had to build a monitoring system from scratch to understand what was actually working.
Here's my playbook
What I ended up doing and the results.
Instead of throwing out our traditional SEO foundation, I developed a layered approach that treats GEO as an enhancement, not a replacement. Here's the exact framework I implemented:
Layer 1: Reinforced SEO Fundamentals
First, I made sure we had rock-solid traditional SEO in place. This meant comprehensive keyword research, proper technical setup, and content that already ranked well in Google. Why? Because AI systems need to crawl and index your content just like search engines do.
The foundation included:
Thorough keyword mapping across all product categories
Technical SEO optimization for faster crawling
Content that ranked in traditional search results
Proper internal linking structure
Layer 2: Chunk-Level Content Restructuring
This is where GEO gets different. I restructured content so each section could stand alone as a valuable snippet. Instead of long-form articles that require full context, I broke information into self-contained chunks that AI systems could easily extract and synthesize.
For example, instead of writing: "Our product offers multiple benefits including X, Y, and Z, which work together to solve your problem..."
I restructured it as: "Benefit X: [Complete explanation that stands alone]. Benefit Y: [Complete explanation that stands alone]..."
Layer 3: Citation-Worthy Content Standards
I focused on creating content that AI systems would want to cite. This meant:
Factual accuracy with clear attribution
Comprehensive coverage of topics
Logical structure for easy extraction
Original insights and data when possible
Layer 4: Multi-Modal Integration
I added visual elements like charts, tables, and infographics that could enhance AI responses. Many AI systems now incorporate visual information, so having well-structured data presentations increased our chances of inclusion.
Layer 5: Monitoring and Iteration
I built a simple tracking system to monitor mentions across different AI platforms. This wasn't about gaming the system - it was about understanding which content resonated and why.
Foundation First
Keep traditional SEO as your base - AI systems still need to crawl and index your content like search engines do.
Chunk Strategy
Structure content so each section stands alone as a complete, extractable insight for AI synthesis.
Citation Quality
Focus on factual accuracy and comprehensive coverage - AI systems prefer authoritative, well-sourced content.
Multi-Modal Edge
Add charts, tables, and visual data that AI systems can reference to enhance their responses.
The results weren't immediate, but they were significant. Within three months of implementing this layered approach, our AI mentions increased from a couple dozen to over 100 per month across various platforms.
More importantly, these weren't just vanity metrics. The AI mentions correlated with increased direct traffic and brand searches. People were discovering the brand through AI responses and then visiting the site directly.
Here's what surprised me most: the biggest gains came from doubling down on traditional SEO fundamentals, not from AI-specific tactics. The content that performed best in AI responses was the same content that ranked well in Google.
The chunk-level restructuring helped, but it was incremental improvement on top of already-strong content. The multi-modal elements (charts and tables) showed promise but weren't game-changers.
What really moved the needle was comprehensive, authoritative content that covered topics thoroughly. AI systems gravitated toward our most complete and well-structured pieces - exactly the kind of content that also performed well in traditional search.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the seven most important lessons from this experiment:
Traditional SEO fundamentals are non-negotiable. Don't abandon what works. AI optimization is additive, not replacement.
Quality beats tactics every time. The content that gets cited is comprehensive, accurate, and genuinely useful - not content optimized for AI consumption.
Chunk-level thinking helps but isn't revolutionary. Breaking content into self-contained sections improves AI pickup, but only marginally.
Measurement is crucial and challenging. You need systems to track AI mentions since there are no traditional "rankings" to monitor.
The landscape changes too fast for aggressive tactics. Any technique that requires gaming the system will likely become obsolete quickly.
Direct correlation with traditional search performance. Content that ranks well in Google tends to get picked up by AI systems too.
It's about being citation-worthy, not optimization-heavy. Focus on creating content that AI systems want to reference, not content designed to manipulate them.
If I were starting this project today, I'd spend 80% of my effort on traditional SEO and content quality, and 20% on AI-specific optimizations. The fundamentals matter more than the newest tactics.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies looking to rank on Perplexity AI:
Start with solid technical SEO and comprehensive content
Create detailed use case documentation and feature explanations
Structure product information in self-contained sections
Track AI mentions using brand monitoring tools
For your Ecommerce store
For e-commerce stores targeting AI visibility:
Focus on comprehensive product information and buying guides
Create comparison content that AI systems can easily reference
Structure product details in chunk-friendly formats
Monitor brand mentions across AI platforms for insights