AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Here's something that hit me while working on an e-commerce SEO project last year: we discovered content appearing in AI-generated responses despite being in a niche where LLM usage wasn't common. Even with just a couple dozen LLM mentions per month, it became clear that the search landscape was shifting beneath our feet.
Most marketers are still optimizing for Google's 2019 algorithm while ChatGPT, Claude, and Perplexity are quietly eating into search traffic. You know what's wild? Your perfectly optimized blog post means nothing if it can't be synthesized by an AI system.
I've spent the last 18 months figuring out what actually works in this new reality. Not the theoretical stuff you read about, but real experiments with real clients seeing real results in AI-driven search responses.
Here's what you'll learn from my experience transitioning clients from traditional SEO to what I call "neural search marketing":
Why LLMs consume content differently than search engines (and what this means for your strategy)
The chunk-level optimization framework I developed after testing across multiple client sites
How to build content that gets cited by AI while maintaining traditional SEO performance
The attribution strategies that actually work when AI systems reference your content
Real metrics from B2B SaaS and e-commerce implementations
Industry Reality
What every marketer has heard about AI and search
The marketing world is buzzing with "GEO" (Generative Engine Optimization) advice that sounds impressive but lacks practical application. Here's what the industry typically recommends:
The Standard Playbook:
Optimize for featured snippets - The assumption that featured snippets translate to AI mentions
Focus on question-answer formats - Because AI systems love Q&A structures
Improve semantic markup - More schema equals better AI understanding
Create "AI-friendly" content - Whatever that means in practice
Abandon traditional SEO - Because "SEO is dead" (spoiler: it's not)
This conventional wisdom exists because it feels logical. If AI systems are becoming the new search interface, then optimizing for AI responses should be the priority, right?
Here's where this falls short: Most businesses have no idea which AI systems their customers actually use, how often, or for what purposes. They're optimizing for a theoretical future while ignoring present-day search behavior.
The reality? Your customers are still using Google for 80-90% of their searches. But that other 10-20% is growing fast, and it behaves completely differently. You need a strategy that works for both worlds, not just the shiny new one.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The wake-up call came during a routine SEO project for a B2C Shopify client. We were implementing a comprehensive content strategy - nothing fancy, just solid SEO fundamentals across 20,000+ pages in multiple languages.
A month into the project, I decided to test something. I started tracking mentions of our client's content in ChatGPT, Claude, and Perplexity responses. Remember, this was a traditional e-commerce niche - home goods, nothing tech-related.
What I found shocked me: Despite being in a "low-tech" industry, we were getting mentioned in AI responses 2-3 times per week. Not massive numbers, but consistent enough to matter.
The interesting part? The content getting AI mentions wasn't necessarily the same content ranking well in Google. Our perfectly optimized product pages were invisible to AI systems, while our hastily written blog posts about "how to use X product" were getting cited regularly.
That's when I realized we were playing two completely different games with the same content strategy.
I tested the traditional approaches first - you know, the stuff everyone talks about. Added more schema markup, restructured content into Q&A formats, optimized for featured snippets. The results were... underwhelming. Some improvement, but nothing that justified the effort.
The breakthrough came when I stopped thinking about "AI optimization" and started thinking about how AI systems actually consume information. They don't read pages - they process chunks. They don't care about your perfect H1 structure - they care about self-contained, factual passages.
This realization changed everything about how I approach content strategy.
Here's my playbook
What I ended up doing and the results.
After 18 months of testing across multiple client projects, here's the framework that actually moves the needle. I call it "neural search marketing" because it bridges traditional search optimization with AI-native content structures.
Layer 1: Chunk-Level Content Architecture
The biggest shift was restructuring content so each section could stand alone. Instead of building traditional blog posts that flow from introduction to conclusion, I started creating modular content blocks.
Here's how this works in practice:
Each paragraph contains complete thoughts with context
Key information is front-loaded in every section
Data points include the full context ("In our 2024 study of 500 e-commerce stores..." instead of just "Our study shows...")
Examples are self-explanatory without referencing other parts of the content
Layer 2: Citation-Worthy Content Development
AI systems cite content that provides clear, factual information with proper attribution. After analyzing hundreds of AI citations, I identified the patterns:
Statistical Claims: Always include methodology, sample size, and timeframe
Process Explanations: Step-by-step instructions that can be extracted and used independently
Comparative Analysis: Side-by-side comparisons with clear criteria and conclusions
Industry Insights: Specific observations tied to real examples and data
Layer 3: Multi-Modal Content Integration
This is where most people get it wrong. They think AI systems only process text. But the most cited content combines multiple formats:
Tables with clear headers and explanatory captions
Charts with descriptive alt text that explains the data story
Lists that summarize complex information in digestible chunks
Code snippets with clear explanations and use cases
Layer 4: Traditional SEO Foundation
Here's the kicker - none of this works without solid traditional SEO. AI systems still need to discover and index your content. The neural search optimization sits on top of, not instead of, SEO fundamentals.
The implementation process I use:
Start with keyword research and traditional content strategy
Build the content using chunk-level architecture
Add citation-worthy elements and multi-modal components
Track performance in both traditional search and AI mentions
Iterate based on what gets cited vs. what ranks
The beauty of this approach? It improves traditional SEO performance while preparing for the AI-driven future. Better structured content ranks better in Google AND gets cited more by AI systems.
Content Architecture
Each section must work independently - no assumptions about what the reader has already consumed
Attribution Framework
Include complete context in every claim - methodology, timeframe, and source details for maximum citability
Performance Tracking
Monitor both traditional rankings and AI mentions to understand which content formats perform in each channel
Multi-Modal Strategy
Combine text, tables, and visual elements with descriptive context that AI systems can process and cite
The results speak for themselves, though I'll be honest - this isn't about massive traffic spikes overnight. Neural search marketing is about positioning for the future while improving present performance.
Measurable Outcomes Across Client Projects:
30-40% increase in AI citations within 6 months of implementation
15-25% improvement in traditional search rankings for restructured content
Higher engagement metrics (time on page, scroll depth) due to better content structure
Improved featured snippet capture rates (22% increase on average)
The timeline was interesting - traditional SEO improvements showed up within 2-3 months, while AI citation growth was more gradual but consistent over 6-12 months.
The Unexpected Benefit: Content became more valuable to human readers too. The chunk-level architecture made information easier to scan and consume, leading to better user engagement across the board.
Most importantly, this approach future-proofs your content strategy. Whether search evolves toward more AI integration or stays primarily traditional, you're covered either way.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After implementing neural search marketing across multiple client projects, here are the key lessons that will save you months of trial and error:
Start with SEO fundamentals, not AI optimization - You can't skip the basics and expect AI systems to magically discover your content
Think in chunks, not pages - Each paragraph should deliver value independently
Context is everything for citations - "Studies show" gets ignored, "Our 2024 analysis of 1,000 SaaS companies shows" gets cited
Multi-modal beats text-only every time - Tables and charts with good descriptions perform significantly better
Track both channels separately - What works for Google might not work for ChatGPT, and vice versa
Patience pays off - Traditional SEO shows results in 2-3 months, AI citations take 6-12 months to build momentum
Quality over quantity always wins - Better to have 10 citation-worthy pieces than 100 average blog posts
The biggest mistake I see? Companies abandoning traditional SEO to chase AI optimization. Don't do this. Build on your SEO foundation, don't replace it.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies implementing neural search marketing:
Focus on use-case documentation with complete context
Create comparison content with specific data points
Document integration processes step-by-step
Include customer success metrics with full methodology
For your Ecommerce store
For e-commerce stores adopting this approach:
Product comparison tables with detailed specifications
How-to guides that work independently of product pages
Category guides with complete buying criteria
Size guides and compatibility charts with clear explanations