AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, I was working with an e-commerce client on what started as a traditional SEO overhaul. We expected the usual drill - keyword research, content optimization, technical fixes. But halfway through the project, something unexpected happened.
We started noticing our content appearing in AI-generated responses. Not from ChatGPT or the obvious players, but from Claude AI and other language models. This was in a niche where LLM usage isn't even common, yet we were getting mentions.
The big question hit me: Can traditional SEO actually work for Claude AI, or do we need to throw everything out the window and start fresh with GEO (Generative Engine Optimization)?
Here's what I discovered after diving deep into both approaches with real client data. You'll learn:
Why traditional SEO fundamentals still matter for AI responses
The specific layer you need to add on top of your existing strategy
How chunk-level thinking changes everything
Which metrics actually matter when optimizing for LLMs
The tactical approach that worked for generating AI mentions
This isn't about replacing your SEO strategy - it's about evolving it for the AI era. Check out our AI optimization playbooks for more strategies.
Real Experience
What I learned from tracking AI mentions
Most SEO experts are telling you to either stick with traditional SEO or completely pivot to GEO optimization. The industry is split into two camps:
Traditional SEO Camp says:
Keep doing what you've always done
AI models will just crawl and index like search engines
Focus on high-quality content and technical optimization
Don't get distracted by the AI hype
GEO-Only Camp says:
Traditional SEO is dead for AI responses
You need completely new optimization tactics
Forget about search engines, optimize for language models
Chunk-level optimization is the only thing that matters
Both camps make valid points, but they're missing the bigger picture. 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.
What I discovered is that this isn't an either-or situation. The foundation hasn't changed as much as people think, but there's definitely a new layer you need to add. Let me show you what actually worked in practice.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When I started this e-commerce SEO project, we expected the usual results - better Google rankings, more organic traffic, the standard wins. But something unexpected happened during our content optimization process.
We discovered our content was already appearing in AI-generated responses from Claude and other language models. This was happening naturally, without any GEO-specific optimization. We tracked a couple dozen LLM mentions per month in a traditional e-commerce niche where AI usage isn't even common.
This discovery led me down the rabbit hole of understanding how AI models actually consume and reference content. I spent weeks researching GEO techniques, talking to teams at AI-first companies, and testing different approaches.
Here's what became clear: LLM robots still need to crawl and index your content. They're not magic - they follow many of the same principles as traditional search engines. Quality, relevant content remains the cornerstone. Traditional SEO best practices are still your starting point.
But there's a crucial difference. LLMs don't consume pages like traditional search engines. They break content into passages and synthesize answers from multiple sources. This meant we needed to restructure our content so each section could stand alone as a valuable snippet.
The breakthrough came when I realized that the couple dozen LLM mentions we achieved weren't from aggressive GEO tactics. They came from solid, comprehensive content that naturally aligned with how AI systems process information.
My biggest insight? Don't abandon what works. Build your GEO strategy on top of strong SEO fundamentals, not instead of them. The landscape is evolving too quickly to bet everything on optimization tactics that might be obsolete in six months.
Here's my playbook
What I ended up doing and the results.
Instead of choosing between traditional SEO and GEO, I developed a layered approach that builds on existing SEO foundations. Here's the exact system that generated AI mentions for our e-commerce client:
Layer 1: Traditional SEO Foundation (This is non-negotiable)
Comprehensive keyword research and content planning
Technical SEO optimization for crawlability
High-quality, authoritative content creation
Proper internal linking and site architecture
Layer 2: GEO-Specific Optimizations
On top of our solid SEO foundation, I implemented five key GEO tactics:
1. Chunk-Level Retrieval
I restructured our content so each section could stand alone as a valuable snippet. Instead of long-form articles, we created modular sections that answered specific questions completely.
2. Answer Synthesis Readiness
We organized information in logical structures that made it easy for AI models to extract and synthesize. This meant clear hierarchies, defined relationships between concepts, and explicit connections.
3. Citation-Worthiness
We focused heavily on factual accuracy and clear attribution. AI models prefer content they can confidently reference, so we included sources, data points, and verifiable claims.
4. Topical Breadth and Depth
Instead of targeting single keywords, we created comprehensive topic clusters that covered all facets of subjects. This increased our chances of being referenced for related queries.
5. Multi-Modal Support
We integrated charts, tables, and visual elements that could enhance AI responses, even though the models primarily process text.
The Testing Rhythm
I established a consistent testing cadence:
Weekly content audits to identify which pieces were getting AI mentions
Monthly analysis of content structure changes and their impact
Quarterly strategy reviews to adapt to AI model updates
The key insight: Your traditional SEO metrics still matter. Pages that ranked well in Google were more likely to be referenced by AI models. The GEO layer amplified what was already working, rather than replacing it.
For more advanced strategies, check out our AI content automation playbook.
Foundation First
Traditional SEO fundamentals remain your starting point. AI models still need to discover and index your content through standard crawling processes.
Chunk Thinking
Structure content so each section answers questions independently. AI models synthesize from passages, not full pages.
Citation Ready
Focus on factual accuracy and clear sources. AI models prefer content they can confidently reference in responses.
Testing Rhythm
Establish regular monitoring of AI mentions and adjust content structure based on what's actually getting referenced.
After implementing this layered approach, we saw measurable improvements in both traditional search rankings and AI model mentions:
Traditional SEO Metrics:
Organic traffic increased as expected from solid SEO fundamentals
Content ranking improved due to better structure and depth
User engagement metrics improved from more scannable content
GEO-Specific Results:
AI mentions increased from our baseline tracking
Content appeared in more diverse AI model responses
Structured content performed better across both search and AI contexts
The most important outcome: we didn't sacrifice traditional SEO performance to optimize for AI. The layered approach enhanced both simultaneously.
What surprised me most was how complementary the strategies actually are. Better content structure helped with traditional rankings, and solid SEO fundamentals made content more likely to be referenced by AI models.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the top lessons from implementing both traditional SEO and GEO optimization:
Don't abandon SEO fundamentals - They're still the foundation that everything else builds on
Chunk-level thinking changes everything - Structure content for passage-level value, not just page-level
AI mentions follow content quality - Better traditional content gets more AI references
Testing is essential - The landscape changes too quickly for set-and-forget strategies
Integration beats replacement - Layer GEO on top of SEO rather than choosing sides
Monitor both channels - Track traditional rankings AND AI mentions to understand full impact
Structure matters more now - Clear hierarchies and logical flow benefit both search engines and AI models
The biggest mistake I see companies making is treating this as an either-or decision. The reality is that both traditional SEO and GEO optimization are necessary for comprehensive content strategy in 2025.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies implementing this approach:
Structure product documentation for both search and AI reference
Create modular help content that answers specific user questions
Monitor AI mentions of your product features and capabilities
Build topic clusters around use cases and integrations
For your Ecommerce store
For e-commerce stores applying this strategy:
Optimize product descriptions for both search visibility and AI recommendations
Structure category pages with clear, referenceable product information
Create buying guides that work as AI reference material
Track AI mentions of your products and categories