AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
When I started noticing my e-commerce client's content appearing in AI-generated responses—despite being in a niche where LLM usage wasn't common—I knew something fundamental was shifting.
This discovery came during what was supposed to be a straightforward SEO overhaul for a traditional retail site. We were tracking around two dozen LLM mentions per month, completely unprompted. No voice search optimization, no special AI-friendly content—just solid fundamentals that somehow made it into conversational AI responses.
That's when I realized everyone asking "do voice assistants use SEO tactics?" was asking the wrong question. The real question is: how do traditional SEO principles translate to an AI-driven search landscape?
Here's what you'll discover in this playbook:
Why voice assistants don't actually "use" SEO—but content optimization still matters
The chunk-level thinking approach that gets your content into AI responses
Real experiments from AI content generation projects across multiple clients
Why traditional SEO foundations beat shiny new "voice optimization" tactics
A practical framework for content that works across all AI platforms
Reality Check
The voice search optimization myths everyone believes
Walk into any marketing conference and you'll hear the same recycled advice about voice search optimization. The industry has created an entire mythology around voice assistants and SEO that sounds compelling but misses the mark completely.
The conventional wisdom goes like this:
Voice searches are longer and more conversational than text searches
You need to optimize for question-based queries and featured snippets
Local SEO becomes more important because people ask "near me" questions
Schema markup is crucial for voice search visibility
Page speed matters even more because voice assistants prioritize fast-loading content
Here's the problem with this framework: it assumes voice assistants work like traditional search engines. They don't crawl, index, and rank pages the way Google does. They don't have a "voice SERP" that shows ten blue links.
Most voice optimization guides treat AI assistants like they're just another search interface when they're actually generative systems that synthesize information from multiple sources. The optimization strategies that work for traditional search often fall flat in conversational AI because the underlying technology is fundamentally different.
This misconception has led to a lot of wasted effort optimizing for scenarios that don't exist while missing the real opportunities that do.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
During my work with the e-commerce client I mentioned earlier, we stumbled into something unexpected. While implementing a comprehensive SEO strategy for their Shopify store—generating over 20,000 pages of content across 8 languages using AI—we started noticing mentions in ChatGPT and Claude responses.
The fascinating part? We weren't optimizing for voice search at all. We were focused on traditional SEO fundamentals: keyword research, content structure, internal linking, and technical optimization. Yet somehow, our content was getting picked up by AI systems.
This led me down a research rabbit hole. I started tracking mentions across different AI platforms and discovered something the voice search optimization guides don't tell you: LLMs don't actually "search" in the traditional sense.
When you ask ChatGPT or Claude a question, they're not crawling the web in real-time and ranking results. They're drawing from their training data and, in some cases, retrieving information from pre-indexed content that was selected during their training process.
The breakthrough came when I realized we were approaching this backwards. Instead of trying to optimize for voice assistants specifically, we needed to understand how AI systems process and synthesize information—then create content that aligns with those patterns.
Through conversations with teams at AI-first startups and tracking our own content performance across platforms, I developed what I call "chunk-level thinking"—optimizing content so each section can stand alone as a valuable, citable piece of information.
Here's my playbook
What I ended up doing and the results.
After months of experimentation across multiple client projects, here's the framework I developed for creating content that works in an AI-driven search landscape:
Step 1: Build Your SEO Foundation First
This might seem counterintuitive, but traditional SEO fundamentals are more important than ever. AI systems still need to discover and process your content, and the signals that worked for search engines—relevance, authority, comprehensive coverage—still matter for AI training data selection.
Focus on:
Comprehensive topic coverage that answers related questions
Clear content hierarchy with logical section breaks
Internal linking that demonstrates topic relationships
Technical SEO that ensures content is crawlable and indexable
Step 2: Implement Chunk-Level Content Structure
This is where the real magic happens. Instead of writing long-form content that flows together, I structure each section to be self-contained and citation-worthy.
Each content "chunk" includes:
A clear topic statement or question
Specific, factual information that can stand alone
Context that makes the information useful without surrounding paragraphs
Attribution or sourcing when relevant
Step 3: Optimize for Answer Synthesis
AI systems excel at combining information from multiple sources to create comprehensive answers. Instead of trying to cover everything in one article, I create content that complements other authoritative sources.
This means:
Providing unique perspectives rather than regurgitating common information
Including specific examples and data points that add value
Structuring information logically so it can be easily extracted and combined
Step 4: Focus on Multi-Modal Content Integration
One discovery from working with e-commerce SEO projects was that AI systems increasingly value content that works across different formats. This includes charts, tables, lists, and visual elements that support the text.
The key is ensuring your core information is accessible regardless of how it's consumed—whether someone is reading it directly or hearing it synthesized by an AI assistant.
Chunk-Level Thinking
Structure each content section to be self-contained and citation-worthy, ensuring it provides value even when extracted from its original context.
Citation-Worthiness
Focus on factual accuracy and clear attribution. AI systems prioritize content that can be confidently cited and verified.
Multi-Modal Support
Integrate charts, tables, and visual elements that support your core information across different consumption formats.
Foundation First
Traditional SEO fundamentals remain crucial for AI discovery. Build solid technical and content foundations before adding AI-specific optimizations.
The results from this approach have been consistently positive across multiple client projects. The e-commerce site that sparked this research went from virtually no AI mentions to regular appearances in ChatGPT and Claude responses within three months.
More importantly, the traditional SEO performance improved dramatically. When you optimize content to be clear, comprehensive, and well-structured for AI consumption, it also performs better in traditional search results.
Across the projects where I've implemented this framework:
Increased organic traffic from improved content quality and structure
Higher engagement rates due to more focused, valuable content sections
Better internal linking opportunities from chunk-level content organization
Improved conversion rates from content that directly addresses user questions
The unexpected outcome? This approach future-proofs your content strategy. As AI systems evolve and new platforms emerge, content optimized for synthesis and citation continues to perform well.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons I've learned from optimizing content for AI-driven search:
AI systems don't "use" SEO—they benefit from good SEO principles. Focus on fundamentals rather than specific voice optimization tactics.
Chunk-level thinking beats long-form optimization. Structure content so each section provides standalone value.
Citation-worthiness matters more than keyword density. AI systems prioritize accurate, verifiable information.
Multi-modal content performs better. Support your text with visual elements that work across formats.
Traditional SEO fundamentals are more important than ever. Don't abandon proven strategies for experimental tactics.
Comprehensive topic coverage outperforms keyword-focused content. AI systems value depth and context over optimization tricks.
The landscape is evolving rapidly. Build flexible content systems rather than platform-specific optimizations.
The biggest mistake I see companies making is treating AI optimization as separate from traditional SEO. The most effective approach integrates both, using AI-friendly content structure to enhance rather than replace proven SEO foundations.
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 means:
Structure product documentation and help content for AI citation
Create comprehensive use-case content that addresses specific customer scenarios
Optimize integration guides and API documentation for AI synthesis
Focus on educational content that demonstrates expertise and builds authority
For your Ecommerce store
For e-commerce stores, this strategy involves:
Product descriptions that work as standalone information chunks
Category pages optimized for comprehensive topic coverage
FAQ content structured for direct AI citation
Review and comparison content that provides unique value