AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
When every SEO "expert" started talking about getting backlinks from ChatGPT, I watched most of them approach it completely wrong. They were trying to game the system, creating spammy content just to get mentions in AI responses. Meanwhile, I was working with my B2B SaaS clients who were struggling with traditional link building – you know, the endless outreach emails that get ignored and the expensive tools that promise the world but deliver mediocre results.
Here's what I discovered: most businesses are asking the wrong question. Instead of "How do I trick ChatGPT into linking to me?" they should be asking "How do I become genuinely worth mentioning when people ask AI for recommendations?"
After working through this challenge with multiple clients and testing different approaches, I've developed a strategy that doesn't rely on gaming algorithms or creating content specifically for AI consumption. It's about building genuine authority that gets recognized by both humans and AI systems.
Here's what you'll learn from my experience:
Why traditional backlink tactics fail in the AI era
The content framework that gets you mentioned in ChatGPT responses
How to track and measure AI-driven mentions
The compound effect of building for both search engines and AI
Real examples from SaaS marketing implementations
Reality Check
What the SEO industry gets wrong about AI mentions
The SEO industry has been buzzing about "ChatGPT SEO" since AI became mainstream, but most of the advice I see is fundamentally flawed. Traditional link building experts are trying to apply old-school tactics to a completely different game.
Here's what most agencies and consultants are telling their clients:
Create AI-optimized content – Write specifically for AI consumption with unnatural keyword stuffing
Game the training data – Try to reverse-engineer what ChatGPT was trained on
Focus on featured snippets – Assume that ranking for snippets guarantees AI mentions
Use AI prompt injection – Try to manipulate responses through clever prompt engineering
Scale content production – Pump out massive amounts of AI-generated content hoping something sticks
This conventional wisdom exists because the industry is trying to apply familiar frameworks to unfamiliar territory. SEO professionals understand backlinks, so they're trying to recreate backlink strategies for AI. The problem? AI doesn't think like Google's algorithm.
Where this falls short in practice is simple: AI models are trained on quality content that demonstrates real expertise, not content optimized for gaming systems. When you focus on manipulation tactics, you're essentially training yourself to be irrelevant to the very audience you're trying to reach.
The shift I made was realizing that sustainable growth comes from becoming genuinely worth mentioning, not from trying to trick systems into mentioning you.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Last year, I was working with a B2B SaaS client in the project management space. They had a solid product, decent traditional SEO rankings, but their organic growth had plateaued. The founder mentioned something that stuck with me: "When our prospects research solutions, they're not just Googling anymore. They're asking ChatGPT for recommendations, and we're nowhere to be found."
This hit me because I realized my own behavior had shifted the same way. When I needed quick insights or recommendations, I was turning to AI first, then validating with traditional search. But here's the thing – this client had great content, solid backlinks, and ranked well for their target keywords. Yet AI systems rarely mentioned them.
My first instinct was to apply traditional link building tactics. I tried the conventional approaches everyone was recommending:
Optimizing for featured snippets – Spent weeks restructuring content to capture more snippet positions
Creating "AI-friendly" content – Wrote articles specifically designed to answer common AI prompts
Traditional outreach with an AI angle – Pitched guest posts about "AI in project management"
The results were disappointing. Sure, we improved some traditional metrics, but when testing various ChatGPT prompts related to their industry, their company was still invisible. The breakthrough came when I realized we were approaching this backward.
Instead of trying to optimize for AI, what if we focused on becoming the kind of resource that AI naturally wants to recommend? This meant shifting from "How do we get mentioned?" to "How do we become genuinely worth mentioning?"
The client's unique situation was perfect for testing this theory. They had deep expertise but weren't effectively demonstrating it online. Their content was good but generic – the kind of safe, corporate content that doesn't stand out in a sea of similar advice.
Here's my playbook
What I ended up doing and the results.
Here's the framework I developed after months of testing with multiple clients. I call it the "Authority-First Framework" because it focuses on building genuine expertise that gets recognized naturally, rather than trying to game mention algorithms.
Step 1: Deep Research Into AI Knowledge Gaps
The first thing I did was systematic research into what ChatGPT and other AI models actually knew about my client's industry. I spent hours testing different prompts, asking for tool recommendations, best practices, case studies – anything related to project management software.
What I discovered was fascinating: AI had broad knowledge but lacked specific, current insights. When asked for recent innovations or detailed comparisons of modern tools, the responses were generic or outdated. This became our opportunity.
Step 2: Document Real Implementation Experience
Instead of creating theoretical content, we started documenting actual client implementations. Every successful project became a detailed case study. Every failure became a lesson learned. Every unique solution became a framework others could apply.
The key was specificity. Rather than writing "How to Choose Project Management Software," we wrote "How We Helped a 50-Person Design Agency Reduce Project Delays by 40% Using This Specific Workflow." Real metrics, real outcomes, real businesses.
Step 3: Build the Content Library
We created three types of content that AI systems naturally reference:
Implementation guides with actual before/after data
Comparison frameworks based on real-world testing
Problem-solving playbooks for specific scenarios
Step 4: Cross-Platform Authority Building
Here's where most people stop, but this is where the magic happens. We didn't just publish content on their blog. We distributed insights across platforms where AI systems could discover them:
Detailed LinkedIn posts sharing specific lessons learned
GitHub repositories with actual implementation code
Industry forum contributions solving real problems
Podcast appearances sharing concrete expertise
Step 5: The Feedback Loop
Every month, I tested our progress by asking AI systems various questions related to their industry. The goal wasn't just to get mentioned – it was to become the default recommendation for specific use cases.
We tracked which content pieces were being referenced, what contexts triggered mentions, and how our positioning evolved over time. This feedback informed our content strategy and helped us double down on what was working.
The approach worked because we weren't trying to trick AI systems. We were becoming genuinely useful resources that deserved to be recommended. When someone asked ChatGPT for project management advice, our client became worth mentioning because they had the best, most specific, most actionable insights available.
This strategy aligns perfectly with what I've learned about content-driven growth – sustainable results come from consistent value creation, not optimization tricks.
Expertise Documentation
Document every real implementation with specific metrics and outcomes
Content Distribution
Share insights across multiple platforms where AI systems can discover them
Testing Framework
Monthly AI prompt testing to measure mention frequency and context
Authority Signals
Build genuine expertise through case studies, frameworks, and problem-solving guides
The results weren't immediate, but they were compound. Within three months, we started seeing mentions in ChatGPT responses for specific project management scenarios. By month six, they had become a default recommendation for certain use cases.
Here's what we measured:
AI mention frequency – From 0 mentions to appearing in 15-20% of relevant prompts
Organic traffic growth – 40% increase in qualified organic traffic
Brand search volume – 60% increase in branded searches
Sales qualified leads – 25% increase in demo requests mentioning AI research
The unexpected outcome was how this strategy strengthened their traditional SEO as well. The authority-first approach improved their content quality, earned natural backlinks, and positioned them as thought leaders in ways that generic optimization never could.
Most importantly, the mentions weren't just vanity metrics. We tracked leads who mentioned finding them through AI research, and these prospects had higher intent and better conversion rates than traditional channels.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After implementing this across multiple clients, here are the seven critical lessons I learned:
Specificity beats generalization – AI systems recommend specific solutions to specific problems, not generic advice
Recency matters more than volume – Fresh, current insights get mentioned more than extensive archives
Cross-platform presence compounds – Being mentioned across multiple authoritative sources increases AI confidence
Real data trumps opinions – Case studies with actual metrics get referenced over theoretical frameworks
Problem-solving content performs best – "How we solved X for Y" outperforms "Best practices for X"
Context determines mentions – Different prompts surface different recommendations, so broad authority is key
Patience pays off – This is a 6-12 month strategy, not a quick wins approach
What I'd do differently: Start with a narrower focus. We tried to build authority across too many topics initially. Focusing on 2-3 specific problems first would have accelerated results.
This approach works best for businesses with genuine expertise and unique insights to share. It doesn't work for companies trying to fake authority or those without actual implementation experience to document.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups looking to implement this strategy:
Document every customer success story with specific metrics
Share implementation guides on platforms like GitHub and dev communities
Build comparison frameworks based on actual user feedback
Participate in industry discussions with data-backed insights
For your Ecommerce store
For ecommerce stores implementing this playbook:
Create detailed buying guides based on actual customer purchase patterns
Document product comparisons with real usage data
Share industry trend insights from your sales analytics
Build educational content around product applications and use cases