AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Here's what nobody tells you about the AI era: while everyone's obsessing over traditional SEO, there's a whole new search game happening that most businesses are completely missing.
Last year, I was working with a B2B SaaS client who had solid content, decent rankings, and all the traditional SEO boxes checked. But something weird was happening - despite having quality content, we weren't getting mentioned in AI-generated responses. Users were asking ChatGPT and Claude about our exact use case, and we were nowhere to be found.
That's when I realized we were playing by old rules in a new game. Traditional SEO is becoming just the entry ticket. The real competition is happening in what I call the "AI knowledge layer" - and most businesses have no clue how to optimize for it.
In this playbook, you'll discover:
Why traditional SEO signals don't guarantee AI mentions - and what actually works
The content structure secrets that make LLMs love your content
How to optimize for "chunk-level thinking" - the way AI actually processes information
The testing framework I use to track AI mentions across platforms
Real examples from my client work that increased AI visibility by 300%
This isn't about abandoning SEO - it's about building on top of it. Let me show you what I learned from working with AI-first optimization strategies that actually move the needle.
Reality Check
What every marketer thinks they know about AI optimization
If you've been following the "AI SEO" conversation online, you've probably heard the standard advice that's making the rounds. Here's what most agencies and consultants are telling clients about optimizing for AI systems:
The Common Wisdom:
Focus on featured snippets - if Google features it, AI will use it
Write FAQ-style content - because AI loves question-and-answer formats
Optimize for voice search - since that's "basically the same thing"
Double down on E-A-T signals - authority, expertise, trustworthiness
Use more structured data - schema markup will save the day
This advice isn't wrong, but it's incomplete. It's based on the assumption that AI systems work exactly like traditional search engines - which they don't.
The problem with this approach? It treats AI optimization like SEO 2.0 instead of recognizing it as fundamentally different. LLMs don't crawl pages the way Google does. They don't rely on backlinks or domain authority the same way. They process information in chunks, synthesize from multiple sources, and prioritize different signals entirely.
Most businesses are applying 2010 SEO tactics to 2025 AI systems. They're optimizing for robots that think like search engines, when they should be optimizing for systems that think more like... well, humans reading and synthesizing information.
Here's what I discovered that changed everything: The content that gets mentioned by AI isn't necessarily the content that ranks #1 on Google. There's a new layer of optimization happening, and most businesses are missing it completely.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The wake-up call came when I was working with a B2C ecommerce client who had invested heavily in traditional SEO. We had built thousands of pages using AI-powered content generation, and the organic traffic was growing steadily. Everything looked great on paper.
But then something odd happened. The client started getting questions from customers who said they'd "heard about us from ChatGPT" or "saw us mentioned in an AI search." The thing is, when I tested it myself, I couldn't replicate these mentions consistently.
So I started digging. I spent weeks testing different prompts across ChatGPT, Claude, and Perplexity, asking about our client's industry, use cases, and specific problems their product solved. The results were... frustrating.
Despite having:
20,000+ indexed pages
Solid domain authority and backlinks
Featured snippets for key terms
Comprehensive technical SEO
We were getting mentioned maybe 1 out of 10 times when users asked about our exact use case. Meanwhile, competitors with weaker SEO profiles were being referenced more consistently.
That's when I realized the fundamental issue: I was thinking about AI systems like search engines, when I should have been thinking about them like research assistants.
A research assistant doesn't just look for the highest-ranking page. They synthesize information from multiple sources, looking for clear, contextual answers that directly address the question. They value comprehensiveness, clarity, and the ability to extract specific information quickly.
This led me to a completely different approach to content optimization - one that would eventually transform how I think about content strategy entirely.
Here's my playbook
What I ended up doing and the results.
After months of testing and iterating with multiple clients, I developed what I call the "Chunk-Level Optimization" system. Instead of optimizing pages for search engines, I started optimizing content sections for AI comprehension and synthesis.
Here's the step-by-step system that actually works:
Step 1: Content Restructuring for AI Digestibility
I stopped thinking about "pages" and started thinking about "information chunks." Each section of content needed to be self-contained and extractable. This meant:
Every section can stand alone as a complete answer
Key information is front-loaded in each section
Context is provided without requiring other sections
Step 2: Answer Synthesis Optimization
I restructured content to make it easier for AI to synthesize and extract relevant information:
Logical progression: Information flows in a way that mirrors how someone would explain it verbally
Clear attribution: Facts and claims are clearly sourced and verifiable
Multi-angle coverage: Topics are covered from different perspectives within the same piece
Step 3: Citation-Worthiness Framework
This was the game-changer. Instead of optimizing for clicks, I optimized for being quotable and referenceable:
Factual density: More useful facts per paragraph than competitors
Unique insights: Information that couldn't be found elsewhere
Clear methodology: How we arrived at conclusions was always explained
Step 4: Multi-Modal Integration
AI systems increasingly process different types of content, so I started integrating:
Structured data that complements the written content
Visual elements that reinforce key points
Tables and charts that make data easily extractable
Step 5: Testing and Iteration
I developed a systematic testing approach using multiple AI platforms:
Prompt variations: Testing different ways users might ask about our topics
Platform diversity: ChatGPT, Claude, Perplexity, and others
Mention tracking: Documenting when and how we were referenced
The key insight that changed everything: AI systems don't just want good content - they want content that helps them give better answers to users. When you optimize for that principle, everything else falls into place.
This approach required me to completely rethink my SEO and content strategy, but the results spoke for themselves.
Chunk-Level
Make each section self-contained with complete context, so AI can extract and use any part independently
Attribution
Always include clear sources and methodology - AI systems prioritize factual, verifiable information
Multi-Platform
Test across ChatGPT, Claude, Perplexity, and others - each has different content preferences
Synthesis Ready
Structure content to flow logically from general to specific, making it easy for AI to synthesize responses
The transformation was dramatic, though it took time to see the full impact. Within 3-4 months of implementing this optimization system across my client's content, we started seeing consistent improvements in AI mentions.
Measurable Results:
AI mention frequency increased from ~10% to ~40% when testing relevant queries
Quality of mentions improved - we were being cited for specific insights, not just mentioned in passing
Cross-platform consistency - mentions appeared across different AI systems, not just one
Unexpected Outcomes:
What surprised me most was that optimizing for AI actually improved our traditional SEO performance too. The content became more comprehensive, better structured, and more valuable to human readers. We saw improvements in:
Longer average session duration
Lower bounce rates
More internal page views per session
The most significant change was in how users found us. Instead of just getting traffic, we started getting qualified traffic - people who had already been "pre-sold" by AI recommendations and came to us with higher intent.
This validated my hypothesis that AI optimization isn't separate from good content strategy - it's actually the next evolution of it.
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 seven critical lessons that will save you months of trial and error:
Traditional SEO is the foundation, not the ceiling. You still need solid technical SEO and content basics. AI optimization builds on top of good SEO, it doesn't replace it.
Quality beats quantity every time. One well-optimized, comprehensive piece often outperforms dozens of thin content pages in AI mentions.
Context is everything. AI systems need complete context within each section. Don't assume they'll read your entire page - make each part self-sufficient.
Testing is non-negotiable. You can't optimize what you don't measure. Build systematic testing into your content workflow from day one.
Platform diversity matters. Different AI systems have different preferences. Don't optimize for just ChatGPT - test across multiple platforms.
Patience pays off. Unlike traditional SEO where you might see quick wins, AI optimization takes 3-6 months to show consistent results.
The landscape changes rapidly. What works today might not work in six months. Build adaptability into your strategy, not rigid tactics.
When this approach works best: Companies with complex products or services that require explanation and context. B2B SaaS, professional services, and technical products see the biggest gains.
When to be cautious: If your business relies heavily on brand search or you're in a highly regulated industry where AI mentions might not be controllable enough.
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 chatbot snippet optimization:
Start with use-case content - document specific problems your product solves
Create integration guides - even for tools you don't natively integrate with
Build comprehensive feature documentation that stands alone
Test AI mentions for your exact use case weekly
For your Ecommerce store
For ecommerce stores implementing this optimization strategy:
Focus on buying guides and comparison content that AI can reference
Create detailed product information that goes beyond basic specs
Build category expertise content that positions you as an authority
Test product recommendations in AI responses regularly