AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
So I had this client running a B2C Shopify store with literally zero organic traffic. Less than 500 visits monthly. Classic case of being stuck in what I call Google's SEO graveyard - technically sound website, decent products, but completely invisible to search engines.
While everyone was obsessing over traditional SEO tactics and fighting for the same keywords, I took a completely different approach. Instead of competing in the red ocean of conventional search optimization, I decided to focus on the emerging world of conversational search - optimizing for how people actually talk to AI assistants like ChatGPT, Claude, and Perplexity.
Here's what you'll learn from my experiment:
Why traditional SEO is becoming a losing game for new players
How conversational search patterns differ from keyword-based queries
The AI-powered content workflow that generated 20,000+ pages across 8 languages
Why chunk-level optimization beats page-level optimization
Real metrics from scaling traffic 10x using GEO (Generative Engine Optimization)
This isn't about following the latest AI trends or jumping on another marketing bandwagon. This is about recognizing a fundamental shift in how people find information and positioning yourself ahead of the curve.
Industry Reality
The traditional SEO advice that's keeping you invisible
If you've been following conventional SEO wisdom, you've probably heard the same advice repeated everywhere:
Focus on keyword research - Use tools like Ahrefs and SEMrush to find "low competition" keywords
Create pillar pages - Build comprehensive topic clusters around your main keywords
Optimize for featured snippets - Structure content to appear in position zero
Build high-quality backlinks - Get other sites to link to your content
Focus on E-A-T - Demonstrate expertise, authoritativeness, and trustworthiness
This advice isn't wrong, but it's incomplete and increasingly outdated. Here's why this traditional approach is failing:
The competition is brutal. Every "low competition" keyword gets flooded within months. What SEMrush shows as 10/100 difficulty today becomes 70/100 difficulty next quarter. You're fighting established players with massive domain authority and content teams.
User behavior is shifting rapidly. People increasingly use conversational queries like "What's the best project management tool for remote teams under 50 people?" instead of typing "project management software." Traditional keyword research tools can't capture this shift.
AI assistants are changing discovery. More people are asking ChatGPT, Claude, or Perplexity for recommendations instead of going to Google first. These AI systems process and synthesize information differently than traditional search engines.
The problem isn't that traditional SEO doesn't work - it's that it's becoming a rich person's game. If you're a startup or small business trying to compete with established players using the same tactics, you're essentially bringing a knife to a gunfight.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
OK, so here's the situation I walked into. This was a B2C Shopify client with over 3,000 products - a massive catalog spanning 8 different languages. Sounds impressive, right? Wrong. Their organic traffic was practically non-existent.
The website was technically solid. Fast loading times, mobile responsive, proper URL structure. They'd even tried traditional SEO - hired an agency, did keyword research, created some blog content. Nothing was fundamentally broken, but nothing was working either.
When I analyzed their traffic data, I found something interesting. The few organic visitors they did get were using long, conversational search queries - stuff like "best eco-friendly cleaning products for sensitive skin toddlers" instead of just "cleaning products." These longer queries converted better, but there were so few of them.
That's when I realized the opportunity. Everyone was optimizing for how people used to search, not how they actually search now.
I started paying attention to my own behavior. When I needed to find something specific, I was increasingly asking ChatGPT or Claude instead of googling. And the way I asked these AI assistants was completely different - more conversational, more context-heavy, more specific.
For example, instead of searching "CRM software" on Google, I'd ask Claude: "What's a good CRM for a 20-person marketing agency that integrates well with HubSpot and doesn't cost a fortune?" The AI would give me specific recommendations with reasoning.
The problem was, my client's content wasn't structured for this kind of conversational discovery. Their product pages were optimized for traditional keywords, but AI systems need different signals to understand and recommend content.
I decided to run an experiment. Instead of fighting for traditional SEO rankings, I'd optimize specifically for AI discovery and conversational search patterns. This meant rethinking everything from content structure to the actual ecommerce optimization approach.
Here's my playbook
What I ended up doing and the results.
Here's exactly what I implemented, step by step:
Step 1: AI-Powered Content Architecture
First, I built what I called a "knowledge engine" - a comprehensive database of industry-specific information that could feed AI content generation. This wasn't just scraping competitor content. We dug deep into the client's actual product knowledge, customer questions, and industry expertise.
I then created a multi-layered AI prompt system:
Knowledge layer - Industry expertise and product specifications
Structure layer - SEO requirements and content organization
Voice layer - Brand tone and conversational patterns
Step 2: Chunk-Level Optimization
Traditional SEO optimizes entire pages. But AI systems break content into chunks and synthesize answers from multiple sources. So I restructured all content so each section could stand alone as a valuable snippet.
Instead of creating one comprehensive "Ultimate Guide to Eco-Friendly Cleaning," I created dozens of specific, self-contained sections that could answer precise conversational queries like "how to remove wine stains without harsh chemicals" or "safest floor cleaner for homes with crawling babies."
Step 3: Conversational Query Mapping
I used AI tools to generate hundreds of natural language queries people might use when looking for products in each category. Then I mapped specific content chunks to answer these conversational patterns.
For a simple product like "glass cleaner," I created content targeting queries like:
"What's the safest way to clean windows without streaks?"
"Can I use vinegar-based cleaners on tinted car windows?"
"Best glass cleaner that won't trigger my asthma"
Step 4: Multi-Modal Content Integration
AI systems increasingly process multiple content types. I integrated charts showing product comparisons, step-by-step visual guides, and structured data that could be easily parsed and synthesized.
Step 5: AI-Native Automation
The breakthrough was automating this entire process. I built an AI workflow that could:
Generate product descriptions optimized for conversational queries
Create FAQ sections answering specific user intents
Automatically translate and localize content across 8 languages
Update and refresh content based on trending conversational patterns
This wasn't about gaming the system - it was about genuinely providing better, more comprehensive answers to the questions people were actually asking.
Query Mapping
Created hundreds of natural conversation patterns people use when searching for products in each category
Chunk Optimization
Restructured content so each section could standalone and answer specific conversational queries effectively
AI Workflow
Built automated system to generate and maintain optimized content across 3000+ products and 8 languages
Content Synthesis
Focused on making information easily discoverable and synthesizable by AI systems rather than traditional search
The results were pretty dramatic. Over 3 months, we went from under 500 monthly organic visitors to over 5,000 - essentially a 10x increase. But the more interesting metric was the quality of traffic.
Traditional SEO traffic often has high bounce rates because people land on pages that don't quite match their intent. Our conversational-optimized content had much better engagement:
Average session duration increased by 140% - people were actually finding what they needed
Bounce rate dropped to 35% - significantly lower than industry averages
Conversion rate improved by 60% - better traffic quality led to more sales
But here's what really surprised me: we started getting mentioned by AI assistants organically. Without any specific optimization for it, our content began appearing in ChatGPT and Claude responses when people asked for product recommendations in our niche.
The 20,000+ pages we generated got indexed by Google, but more importantly, they became a comprehensive knowledge base that AI systems could draw from. We weren't just ranking for keywords anymore - we were becoming a trusted source for conversational AI recommendations.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons from this experiment:
Think synthesis, not ranking - AI systems care more about how easily they can extract and synthesize your information than where you rank for specific keywords
Conversational queries are longer and more specific - optimize for the questions people actually ask, not the keywords they type
Context matters more than keywords - AI systems understand intent and context better than traditional search engines
Chunk-level optimization is the future - structure content so individual sections can answer specific questions
Quality scales with AI - you can create high-quality content at scale if you build the right systems
Multi-modal content wins - combine text, visuals, and structured data for better AI discoverability
Traditional SEO metrics don't tell the full story - focus on engagement and conversion quality over pure traffic volume
The biggest insight? We're in the early stages of a major shift in how people discover information. Companies that adapt their content strategy for conversational AI discovery will have a significant advantage over those still fighting for traditional search rankings.
This approach works best for businesses with complex product catalogs or service offerings where people need specific, contextual recommendations. It's less effective for simple, commodity products where brand recognition trumps informational content.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups, focus on:
Creating conversational use case content that AI can easily synthesize
Building comprehensive integration guides for AI discovery
Optimizing product descriptions for natural language queries
For your Ecommerce store
For ecommerce stores, prioritize:
Product pages optimized for specific conversational buying intent
FAQ sections answering real customer questions
Category pages structured for AI-powered product recommendations