AI & Automation
Personas
Ecommerce
Time to ROI
Medium-term (3-6 months)
Last month, I was testing something completely different for one of my ecommerce clients. While every other "SEO expert" was obsessing over Google rankings, I decided to experiment with optimizing content for AI chatbots.
The results? Within three months, our content was being referenced by ChatGPT, Perplexity, and Claude in response to industry-specific queries. But here's the kicker - this wasn't from following some generic "AI SEO" guide. It came from understanding what I call the "chunk-level optimization" approach.
Most businesses are still playing the old SEO game while missing the biggest shift in search behavior since Google launched. People are asking AI chatbots for recommendations, comparisons, and solutions - and if your content isn't optimized for this new reality, you're invisible in conversations that matter.
Here's what you'll learn from my real-world experiment:
Why traditional SEO tactics fail with AI chatbots (and what works instead)
The exact content structure that gets you mentioned in AI responses
How to optimize for "chunk-level retrieval" without destroying your Google rankings
The metrics that actually matter when tracking AI mentions
Why this approach works better for ecommerce than traditional link-building
If you're tired of chasing algorithm updates and want to position your brand where customers are actually asking questions, this playbook is for you. Let's dive into what I discovered when I stopped optimizing for search engines and started optimizing for AI conversations.
Industry Reality
What every ecommerce owner has been told about AI and SEO
The SEO industry has been scrambling to figure out "AI SEO" ever since ChatGPT exploded. Most of what you'll hear falls into these predictable categories:
"AI will kill SEO completely" - The doomsday crowd claims that search engines are dead and everyone will just ask ChatGPT instead. This ignores the fact that AI models still need to crawl and index content from somewhere.
"Just add AI-generated content" - The lazy approach. Pump out thousands of AI-written articles hoping quantity beats quality. Google's getting better at detecting this, and AI chatbots prefer authoritative, well-sourced content anyway.
"Optimize for featured snippets" - The old-school SEO pivot. Since AI models often pull from featured snippets, the thinking goes that if you rank there, you'll show up in AI responses. Partially true, but missing the bigger picture.
"Focus on E-A-T" - Expertise, Authoritativeness, Trustworthiness. Solid advice, but most people interpret this as "write longer articles with more credentials" rather than understanding how AI models actually evaluate authority.
"Wait and see" - The paralysis approach. Many businesses are sitting on the sidelines waiting for "best practices" to emerge while early adopters grab market share in AI conversations.
The problem with all these approaches? They're still thinking like traditional SEO - trying to game a system instead of providing genuine value. They miss the fundamental shift: AI models don't rank pages, they synthesize information from multiple sources to answer specific questions.
This creates entirely different optimization requirements that most "AI SEO experts" don't understand because they haven't actually tested anything. They're just recycling old SEO advice with "AI" slapped on top.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The experiment started with a B2C Shopify client who was struggling with traditional SEO. Despite having over 3,000 products and decent content, organic traffic was growing slowly in an increasingly competitive niche. We'd tried the usual playbook - better product descriptions, blog content, technical SEO improvements - with modest results.
But something interesting happened during our content audit. When I searched for industry-specific questions on ChatGPT and Perplexity, I noticed our competitors were getting mentioned in AI responses despite having weaker traditional SEO metrics. This got me curious about what was actually driving AI citations.
The conventional wisdom said to focus on featured snippets and E-A-T, but when I analyzed which content was being referenced by AI models, the patterns didn't match traditional SEO ranking factors. AI models were pulling from content that was structured differently - not necessarily the highest-ranking pages.
My client sold specialized equipment across 8 different languages, which meant we had thousands of pages that needed optimization. Traditional SEO would have taken years to show meaningful results. But AI optimization presented a different opportunity - instead of competing for rankings, we could compete for mindshare in AI conversations.
The challenge was that most "AI SEO" advice was theoretical. I needed to understand how AI models actually processed and retrieved information from web content. So I started reverse-engineering successful examples - content that consistently appeared in AI responses across different models.
What I discovered changed how I think about content optimization entirely. AI models don't consume pages the way search engines do. They break content into passages and synthesize answers from multiple sources. This meant traditional page-level optimization was missing the point.
The breakthrough came when I realized that AI models prioritize "chunk-level retrieval" - they need each section of content to be self-contained and valuable on its own. This was completely different from traditional SEO, where you optimize entire pages for specific keywords.
Here's my playbook
What I ended up doing and the results.
Instead of following generic "AI SEO" advice, I developed what I call the "chunk-level optimization" approach based on how AI models actually process content. Here's the exact system I implemented:
Step 1: Content Architecture Redesign
First, I restructured existing content so each section could stand alone as a valuable snippet. Instead of traditional blog posts that required reading from top to bottom, I created modular content blocks that answered specific questions completely within each section.
For product pages, this meant breaking down complex information into self-contained chunks: "How this works," "Who this is for," "Common applications," "Technical specifications." Each chunk included enough context that an AI model could reference it independently.
Step 2: The Knowledge Base Integration
Working with my client, I built a comprehensive knowledge base that went beyond product specifications. We documented industry expertise, common problems, use cases, and practical applications. This wasn't generic content - it was specific insights that only someone with deep industry knowledge could provide.
The key was making this knowledge searchable and accessible to AI models. I used structured data markup, clear headings, and logical content hierarchy that AI models could easily parse and understand.
Step 3: Cross-Referencing and Authority Building
Unlike traditional SEO that focuses on external backlinks, AI optimization requires internal authority signals. I created extensive cross-references between related content, ensuring that any piece of information existed within a broader context of expertise.
I also implemented citation-style linking, where claims were supported by internal data, case studies, and specific examples. This helped AI models understand the authoritative nature of the content.
Step 4: Multi-Language Consistency
Since we were operating across 8 languages, I developed a system to maintain consistency in how information was structured across all versions. This wasn't just translation - it was ensuring that the same logical flow and chunk-level optimization existed in every language.
Step 5: Continuous Testing and Refinement
I set up a monitoring system to track when our content appeared in AI responses. This involved regularly testing industry-specific queries across different AI models and documenting which content was being referenced and how.
Based on this feedback, I continuously refined the content structure, improving the chunks that were performing well and restructuring sections that weren't being picked up by AI models.
Key Discovery
AI models prefer self-contained information chunks rather than traditional page-level optimization
Content Structure
Each section must answer questions completely with sufficient context for standalone value
Authority Signals
Internal cross-referencing and citation-style linking matter more than external backlinks for AI
Monitoring System
Regular testing across multiple AI models reveals which content structures perform best
Within three months of implementing this approach, we achieved significant improvements in AI visibility. Our content began appearing in ChatGPT, Perplexity, and Claude responses for industry-specific queries, often as the primary or secondary source cited.
The organic traffic impact was substantial - we saw a 10x increase in monthly visitors, growing from under 500 to over 5,000 monthly visits. But more importantly, the quality of traffic improved significantly. Visitors coming from AI-driven searches showed higher engagement and better conversion rates.
What surprised me most was the compound effect. As our content gained recognition in AI responses, it created a feedback loop. More AI citations led to increased authority signals, which led to even more AI mentions and better traditional search rankings.
The multilingual approach paid off particularly well. Content optimized for AI in one language often performed better in other languages too, suggesting that the structural improvements had benefits beyond just AI optimization.
Perhaps most importantly, this approach proved more sustainable than traditional SEO tactics. Instead of constantly chasing algorithm updates, we'd built content that provided genuine value to both AI models and human readers.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
This experiment taught me that the future of content optimization isn't about choosing between traditional SEO and AI optimization - it's about understanding how both systems can work together.
AI models reward expertise over optimization. The content that performed best was created by people with deep industry knowledge, not SEO specialists following checklists. This validates the importance of domain expertise in content creation.
Structure matters more than keywords. While keyword optimization still has value for traditional search, AI models care more about logical information architecture and self-contained content chunks.
Authority comes from internal signals. Unlike traditional SEO that emphasizes external backlinks, AI models seem to evaluate authority based on internal consistency, cross-referencing, and depth of coverage.
Testing is everything. The AI optimization landscape changes rapidly. What works today might not work tomorrow, making continuous testing and refinement essential.
Quality scales better than quantity. Instead of publishing hundreds of thin articles, focusing on comprehensive, well-structured content that covers topics thoroughly produces better AI visibility.
This approach works best for complex products or services where customers need detailed information and expert guidance. Simple commodity products might not benefit as much from this level of content investment.
The compound effect is real. Early investment in AI-optimized content creates long-term advantages that become harder for competitors to replicate over time.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies:
Focus on use case documentation and integration guides
Create self-contained feature explanations
Optimize help docs for AI retrieval
Build comprehensive API documentation
For your Ecommerce store
For ecommerce stores:
Structure product information in standalone chunks
Create detailed buying guides and comparisons
Optimize category descriptions for AI understanding
Focus on technical specifications and use cases