AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
OK, so here's something that happened to me last year that completely changed how I think about SEO tracking. I was working with an e-commerce Shopify client who needed a complete SEO overhaul. What started as a traditional SEO project quickly evolved into something more complex when we discovered their content was starting to appear in AI-generated responses.
Even in a traditional e-commerce niche, we tracked a couple dozen LLM mentions per month. This wasn't something we initially optimized for - it happened naturally as a byproduct of solid content fundamentals. But here's the thing: none of our traditional tracking tools were catching this.
While everyone's obsessing over traditional SERP rankings, there's an entire parallel search ecosystem emerging through ChatGPT, Claude, Perplexity, and other AI assistants. And most businesses have no idea if they're showing up there or not.
Here's what you'll learn from my experience diving into this new world:
Why traditional SEO tools miss 90% of AI-driven visibility
The tracking system I built to monitor LLM mentions across platforms
How to optimize content for both traditional search and AI responses
Real metrics from tracking AI mentions for 6 months
Why GEO (Generative Engine Optimization) needs a completely different approach
The reality? We're in the early days of a massive shift in how people find information, and most tracking strategies are stuck in 2020. Let me show you what actually works in 2025.
Reality Check
What the SEO industry isn't telling you about AI search
If you've been following SEO advice lately, you've probably heard the usual recommendations about tracking rankings. The industry standard playbook goes something like this:
Use traditional rank tracking tools like Ahrefs, SEMrush, or Moz to monitor your positions
Focus on Google Search Console for impression and click data
Track featured snippets since they're "position zero"
Monitor voice search optimization for Siri and Alexa
Set up alerts for brand mentions across the web
This advice exists because it worked for the past decade. Google dominated search, rankings were predictable, and tools like Ahrefs could reliably track where you appeared for specific keywords.
But here's where this conventional wisdom falls short: it completely ignores the fastest-growing search behavior among younger demographics. People are increasingly using AI assistants to research products, compare solutions, and make decisions. When someone asks ChatGPT "what's the best CRM for small businesses," they're not seeing traditional search results.
The problem? Traditional SEO tools can't track AI-generated responses. They can't tell you if ChatGPT mentioned your brand, if Claude recommended your product, or if Perplexity included your content in a research summary. You're essentially flying blind in what's becoming a significant traffic channel.
This creates a massive gap in most companies' understanding of their true search visibility. You might think you're ranking well because your traditional metrics look good, while completely missing mentions in AI responses that could be driving qualified traffic.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Last year, while working on an SEO overhaul for a Shopify e-commerce client, I stumbled into what became my education in AI search tracking. What started as traditional SEO work quickly became something entirely different.
The client operated in a traditional e-commerce niche - nothing particularly "AI-forward" about their industry. But during our content audit, something interesting emerged: we discovered their content was getting mentioned in AI-generated responses, even though we hadn't optimized for it.
I started manually checking ChatGPT, Claude, and Perplexity for queries related to their products. To my surprise, we were getting mentions - not consistently, but frequently enough to matter. A couple dozen per month, which doesn't sound like much until you realize each mention could influence multiple potential customers.
The problem was obvious: none of our traditional tracking tools were catching this. Ahrefs showed our traditional rankings, Google Search Console tracked our regular search performance, but we had zero visibility into this emerging channel.
Through conversations with teams at AI-first startups like Profound and Athena, I realized everyone was figuring this out in real time. There's no definitive playbook yet for tracking AI-driven search visibility. What we do know is that it requires a completely different approach than traditional SEO tracking.
The breakthrough came when I realized that LLMs don't consume content the same way traditional search engines do. They break content into passages and synthesize answers from multiple sources. This meant our tracking needed to be chunk-level, not page-level. We needed to understand not just if we were mentioned, but how our content was being used to construct responses.
That's when I decided to build a systematic approach to track AI mentions alongside our traditional SEO metrics. Instead of guessing, I wanted real data on our AI search performance.
Here's my playbook
What I ended up doing and the results.
Instead of relying on traditional SEO tools that miss AI mentions entirely, I developed a multi-layered tracking system that actually captures AI-driven search visibility. Here's the exact process I implemented:
Layer 1: Manual AI Platform Monitoring
I started with direct testing across the major AI platforms. Every week, I'd run 20-30 queries related to our client's industry through ChatGPT, Claude, Perplexity, and Google Bard. I tracked:
Direct brand mentions in responses
Indirect mentions (content referenced without attribution)
Competitive mentions (who else was being recommended)
Context of mentions (positive, neutral, or negative framing)
Layer 2: Automated Keyword Monitoring
Using API access where available, I set up automated queries for our target keywords. For platforms without API access, I used browser automation tools to systematically test queries and capture responses. This gave us consistent data without the manual overhead.
Layer 3: Content Chunk Analysis
This was the game-changer. Instead of optimizing pages, I restructured content so each section could stand alone as a valuable snippet. I tracked which content chunks were most likely to be referenced by AI systems.
Layer 4: Cross-Platform Comparison
Different AI platforms showed different patterns. ChatGPT favored certain types of content, while Perplexity had different preferences. I tracked performance across platforms to understand where to focus optimization efforts.
The Implementation Process:
Audit existing content for AI-friendliness (clear structure, factual accuracy, citation-worthiness)
Set up tracking systems for major AI platforms using both manual and automated methods
Restructure content into self-contained, referenceable chunks
Monitor competitor mentions to understand the competitive landscape
Optimize based on data rather than assumptions about what AI systems prefer
The key insight was treating this as an entirely new channel requiring its own optimization and tracking approach, not just an extension of traditional SEO.
Core Tracking Setup
Manual monitoring across 4 major AI platforms weekly, plus automated queries for consistent data collection without overwhelming manual work.
Content Restructuring
Breaking content into self-contained chunks that AI systems can easily reference, rather than optimizing for full-page consumption.
Platform Differences
Each AI system shows different preferences - ChatGPT favors certain content types while Perplexity has completely different ranking factors.
Competitive Intelligence
Tracking not just your own mentions but mapping who else gets recommended helps understand the full competitive landscape in AI responses.
After 6 months of systematic tracking, the results told a story that traditional SEO metrics completely missed. We discovered that AI mentions were driving qualified traffic that never showed up in Google Analytics as "AI traffic."
The numbers were revealing:
AI platform mentions increased 300% after optimizing content structure
Competitive analysis showed we went from occasional mentions to consistent inclusion in relevant responses
Content chunks optimized for AI reference performed better in traditional search too
Brand authority increased as we became a more frequently cited source
But the most important discovery was qualitative: the mentions we were getting in AI responses were highly contextual and relevant. Unlike traditional search where you might rank for loosely related queries, AI systems were recommending us specifically when our solution was genuinely the best fit.
This created a compound effect. As our content became more frequently referenced, it seemed to improve our authority across all AI platforms. The tracking system revealed patterns we never would have discovered through traditional SEO tools alone.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons learned from building an AI search tracking system from scratch:
Traditional SEO foundations still matter - LLM robots still crawl and index content using familiar methods
Content structure is more important than content volume - AI systems prefer well-structured, factual information over lengthy articles
Citation-worthiness beats keyword density - focus on being a reliable source rather than keyword optimization
Cross-platform tracking reveals different opportunities - each AI system has unique preferences worth understanding
Manual monitoring is still necessary - automated tools miss context and nuance in AI responses
Competitive intelligence is crucial - understanding who else gets mentioned helps identify optimization opportunities
The landscape changes rapidly - what works today might not work in six months as AI systems evolve
The biggest realization? Don't abandon traditional SEO for AI optimization. Build AI tracking and optimization on top of strong SEO fundamentals. The platforms are evolving too quickly to bet everything on tactics that might be obsolete soon.
My approach now focuses on creating genuinely useful content that aligns with how AI systems process information, rather than trying to game specific algorithms that could change tomorrow.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups looking to track AI search performance:
Start with manual monitoring across ChatGPT, Claude, and Perplexity for your key feature keywords
Track competitor mentions to understand market positioning in AI responses
Focus on use-case content that AI systems can easily reference and recommend
For your Ecommerce store
For e-commerce stores tracking AI-driven product discovery:
Monitor product recommendation queries across AI platforms
Track brand mentions in buying guide and comparison responses
Optimize product descriptions for AI system comprehension and citation