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. We'd implemented all the traditional SEO tactics—keyword research, meta descriptions, backlinks—the whole playbook. But something weird started happening that completely changed how I think about search.
Their content began appearing in AI-generated responses, despite being in a niche where LLM usage isn't common. We tracked a couple dozen LLM mentions per month organically, without any AI optimization. This discovery led me down the rabbit hole of what I call conversational search optimization—the reality of how people actually search today.
While everyone's still optimizing for "what is best CRM software," real users are asking ChatGPT: "I'm a 10-person startup, we tried HubSpot but it's too complex, what CRM would actually work for us?" The search landscape has fundamentally shifted from keywords to conversations.
Here's what you'll learn from my real-world experiments with conversational search:
Why traditional keyword research is leading you astray in 2025
The chunk-level content strategy that gets AI mentions
How I restructured content for both search engines and LLMs
The new metrics that actually matter for conversational search
Real case studies from my AI content experiments
Industry Reality
What the SEO industry is still teaching
Walk into any SEO conference today and you'll hear the same tired advice that worked in 2018. The industry is stuck in a time warp, optimizing for a search behavior that's rapidly disappearing.
The Traditional SEO Gospel:
Find high-volume, low-competition keywords
Create pillar pages and topic clusters
Build backlinks from high-authority domains
Optimize for featured snippets
Track rankings for target keywords
This approach assumes people still type "best project management software" into Google and scroll through 10 blue links. But here's the uncomfortable truth: that's not how your customers search anymore.
The shift started with voice search, accelerated with mobile, and exploded with AI assistants. People now have conversations with search engines. They ask follow-up questions. They provide context about their specific situation.
Yet most businesses are still optimizing for single-intent keywords while their customers are having multi-turn conversations with ChatGPT, Claude, and Perplexity. The disconnect is massive, and it's why so many companies are seeing their organic traffic plateau despite following "best practices."
The industry's response? Double down on the same tactics, just with AI tools to scale keyword research. They're missing the fundamental shift in user behavior.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When I discovered my client's content appearing in LLM responses organically, I knew I had stumbled onto something important. This wasn't a tech company or AI startup—this was a traditional e-commerce business in a niche where AI adoption is minimal.
The client situation was complex: over 3,000 products across 8 languages, virtually no organic traffic, and traditional SEO tactics weren't moving the needle. But somehow, AI systems were finding and citing their content without any specific optimization.
My first instinct was to investigate how this was happening. 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 for what I started calling Generative Engine Optimization (GEO).
Here's what became clear: LLMs don't consume pages like traditional search engines. They break content into passages and synthesize answers from multiple sources. This meant our entire content structure was wrong for this new reality.
I started tracking not just Google rankings, but mentions across ChatGPT, Claude, and Perplexity. The patterns were fascinating—content that ranked poorly on Google was being heavily referenced by AI systems, while some of our top-ranking pages were ignored by LLMs completely.
The breakthrough came when I analyzed which content was getting AI mentions versus traditional search traffic. The difference wasn't in topic or keyword optimization—it was in how the information was structured and presented. AI systems favored content that could stand alone as complete answers, even when extracted from larger pieces.
Here's my playbook
What I ended up doing and the results.
Instead of abandoning traditional SEO for experimental GEO tactics, I developed a layered approach that optimizes for both search engines and conversational AI simultaneously.
The Chunk-Level Content Strategy
I restructured all content so each section could function as a self-contained answer. Rather than writing long-form articles that required reading from start to finish, I created content where every paragraph cluster provided complete value independently.
For example, instead of: "In the next section, we'll explore pricing strategies..." I wrote: "SaaS pricing strategies fall into three categories: per-seat, usage-based, and flat-rate. Per-seat works best for team collaboration tools because costs scale with value received."
Answer Synthesis Readiness
I optimized content for easy extraction and combination. This meant:
Leading with conclusions, then supporting evidence
Using clear logical structures that AI can follow
Providing context within each section
Including relevant data points and examples
Citation-Worthiness Framework
I focused on creating content that AI systems would want to reference. This required balancing factual accuracy with clear attribution. Every claim needed to be verifiable, and complex topics were broken down into logical components.
Topical Breadth and Depth
Rather than targeting single keywords, I covered all facets of topics comprehensively. For a piece on "email marketing automation," I'd address tool selection, setup process, common mistakes, pricing considerations, and integration challenges—everything someone might ask about in a conversation.
Multi-Modal Integration
I enhanced text content with charts, tables, and visual elements that provide additional context. AI systems increasingly reference well-structured data presentations, not just prose.
The key insight: optimize for understanding, not just ranking. When content truly serves the user's intent comprehensively, both traditional search engines and AI systems recognize its value.
Chunk Strategy
Each section provides complete, standalone value that AI can extract and reference independently
Citation Readiness
Content structured for easy extraction with clear attribution and verifiable claims
Multi-Modal Content
Enhanced with charts, tables, and visuals that provide additional context for AI systems
Conversational Intent
Optimized for how people actually ask questions, not just keyword variations
The results from this conversational search approach were significant but took time to materialize. Unlike traditional SEO where you might see ranking changes within weeks, conversational search optimization showed results over months.
AI Mention Growth: From a couple dozen monthly LLM mentions to consistent presence across ChatGPT, Claude, and Perplexity responses. More importantly, these mentions came with context that actually helped users.
Content Engagement: Pages optimized for conversational search showed 40% higher dwell time and 25% more scroll depth. People were actually reading and engaging with the content.
Unexpected Discovery: Traditional Google rankings improved as well. Content structured for AI consumption often aligned perfectly with Google's E-A-T guidelines and user intent signals.
Lead Quality: Traffic from AI-referenced content converted at higher rates than traditional organic traffic. Users coming from conversational search had more context and intent.
The most surprising result was how this approach future-proofed our SEO strategy. As search continues evolving toward natural language queries and AI integration, our content was already positioned for these changes.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After implementing conversational search optimization across multiple client projects, several key lessons emerged that changed how I approach SEO entirely.
1. User Intent Has Evolved Faster Than SEO Tactics
People search conversationally now, but most businesses still optimize for robotic keyword phrases. The gap between user behavior and optimization strategy is widening.
2. AI Systems Favor Comprehensive, Self-Contained Content
Content that answers questions completely without requiring additional reading performs better in both AI mentions and traditional search.
3. Context Is More Valuable Than Keywords
A piece about "CRM for 10-person startups with HubSpot experience" outperforms generic "best CRM software" content every time.
4. Traditional SEO Metrics Don't Capture Conversational Success
Tracking keyword rankings misses the bigger picture of how content performs in actual user conversations.
5. Quality Over Quantity Finally Matters
AI systems and improved search algorithms reward depth and expertise over content volume.
6. The Future Is Multi-Modal
Content that works across text, voice, and visual search channels provides sustainable competitive advantage.
7. Optimize for Understanding, Not Just Discovery
Content that truly helps users solve problems naturally aligns with both search engines and AI systems.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups implementing conversational search:
Focus on specific use case content rather than broad feature descriptions
Create comparison content that addresses real user contexts
Optimize help documentation for AI extraction and reference
Track mentions across AI platforms, not just Google rankings
For your Ecommerce store
For e-commerce stores adapting to conversational search:
Create product guides that answer "what should I buy if..." questions
Structure category pages for conversational product discovery
Optimize for voice shopping and AI-assisted purchasing decisions
Build comparison content around actual shopping scenarios