AI & Automation
Personas
Ecommerce
Time to ROI
Short-term (< 3 months)
Last year, I took on a Shopify project that looked straightforward on paper—a complete SEO overhaul for an e-commerce site with over 3,000 products across 8 languages. What I discovered was a technical SEO nightmare that would have taken a traditional approach months to fix manually.
The site had broken schema markup, missing meta descriptions across thousands of pages, inconsistent title tag structures, and image optimization issues that were killing page speed. The client needed results fast, and manual fixes weren't an option with 20,000+ pages to optimize.
That's when I decided to build an AI-powered workflow to tackle technical SEO at scale. Not because I was jumping on the AI hype train, but because the math was simple: months of manual work versus days of smart automation.
Here's what you'll learn from my experiment:
Why traditional technical SEO audits break down at scale
The specific AI workflow I built to automate schema markup generation
How I used AI to create 20,000+ unique meta descriptions in multiple languages
The unexpected technical issues AI helped me discover
Real metrics from going from <500 to 5,000+ monthly visits in 3 months
If you're managing large sites where manual technical SEO feels impossible, this approach might change how you think about optimization entirely. Let me show you what actually worked when I treated AI as digital labor, not magic.
Technical Reality
What every SEO expert tells you about technical optimization
Walk into any SEO conference or read any technical SEO guide, and you'll hear the same advice repeated like gospel. Every expert will tell you the same thing about technical SEO optimization:
Manual auditing is the gold standard. Use Screaming Frog, export everything to spreadsheets, manually review each page type, and fix issues one by one. This methodical approach ensures quality and prevents mistakes.
Technical SEO can't be rushed. Proper optimization takes time. You need to understand the site architecture, review each template, and carefully implement changes to avoid breaking anything.
AI tools are unreliable for technical work. Machine-generated content might get you penalized. Schema markup needs human oversight. Automated solutions miss context and nuance.
Start small and scale gradually. Fix the most critical issues first, then move to medium-priority items. This prevents overwhelming the development team and ensures proper testing.
Quality over quantity always wins. It's better to perfectly optimize 100 pages than to do a mediocre job on 1,000 pages.
This conventional wisdom exists for good reasons. Manual approaches do ensure quality control, and rushing technical changes can break sites. The traditional methodical approach has worked for years when dealing with smaller sites.
But here's where this advice falls apart: it assumes you have unlimited time and unlimited budget. When you're facing 20,000+ pages that need optimization across multiple languages, manual approaches become mathematically impossible. You're not choosing between perfect and good—you're choosing between automated optimization and no optimization at all.
That's the reality most SEO "best practices" ignore.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When this B2C Shopify client came to me, I thought I understood the scope. "Complete SEO overhaul" sounded manageable until I dove into the actual numbers. Over 3,000 products, multiple collections, blog posts, and landing pages—all needing optimization across 8 different languages.
The technical audit revealed what I call "scale paralysis." Every page had multiple SEO issues: missing or duplicate meta descriptions, inconsistent title tag patterns, broken schema markup, unoptimized images, and inconsistent URL structures. Following traditional methods, I calculated it would take 6-8 months of full-time work just to manually fix the basic on-page elements.
My first attempt was the "proper" way. I started with the homepage and core product pages, manually crafting perfect meta descriptions and title tags. After two weeks of meticulous work, I had optimized maybe 50 pages. At that rate, I'd need two years to finish the project.
The client was patient, but they needed results within three months to capitalize on their peak season. The traditional approach wasn't just slow—it was business-threatening. They were bleeding organic traffic while I perfectioned individual page titles.
That's when I had my realization: I was treating this like a craft project when it was actually a manufacturing problem. You don't hand-craft every car on an assembly line, and you don't manually optimize every page on a 20,000-page website.
The client's business model depended on their vast product catalog being discoverable. Every day of delay meant lost revenue. I needed to shift from artisanal SEO to industrial SEO—and that meant building AI workflows that could maintain quality while operating at scale.
Here's my playbook
What I ended up doing and the results.
Instead of fighting the scale, I decided to embrace it by building what I call an "AI SEO factory." Here's the exact system I developed to tackle technical SEO issues that would take months to fix manually:
Step 1: Data Export and Analysis
First, I exported all products, collections, and pages into CSV files. This gave me the raw material—product names, descriptions, categories, prices, and existing metadata. I also pulled a complete site crawl to identify technical issues at scale.
Step 2: Building the Knowledge Base
This was crucial. I worked with the client to create a comprehensive knowledge base containing: brand voice guidelines, product category hierarchies, target keywords by product type, SEO best practices specific to their industry, and competitor analysis. This became the "brain" that would guide all AI decisions.
Step 3: Custom Prompt Engineering
I developed specific prompts for different SEO elements:
- Title tag generator (product name + category + brand)
- Meta description creator (benefit-focused, under 155 characters)
- Schema markup generator (Product, Organization, BreadcrumbList)
- Alt text creator for product images
- URL slug optimizer
Step 4: Automated Workflow Creation
Using a combination of AI APIs and automation tools, I built workflows that could process hundreds of pages simultaneously. Each workflow included quality checks and fallback options if the AI output didn't meet standards.
Step 5: Multi-Language Implementation
The real challenge was handling 8 languages consistently. I created language-specific prompts that maintained brand voice while adapting to local search behaviors and cultural nuances.
Step 6: Technical Implementation at Scale
Rather than updating pages individually, I used Shopify's bulk editing APIs to implement changes across thousands of pages simultaneously. This reduced implementation from weeks to hours.
The key insight: AI excels at pattern recognition and consistent application. Once I taught it the patterns for good SEO, it could apply those patterns across thousands of pages without fatigue or inconsistency.
Quality Control
AI-generated content with human oversight and approval workflows
Bulk Processing
20,000+ pages optimized simultaneously using Shopify's bulk APIs
Pattern Recognition
AI identified technical issues I missed during manual audits
Multilingual Scale
Consistent optimization across 8 languages maintaining brand voice
The results spoke for themselves, but not in the way I expected. Within the first month of implementation, I noticed something interesting happening in the search console data.
Traffic Growth: The site went from less than 500 monthly organic visitors to over 5,000 within three months. More importantly, this wasn't just more traffic—it was more qualified traffic with higher engagement rates.
Technical Improvements: Page load speeds improved by 40% due to optimized images and cleaner code structure. Core Web Vitals scores moved from "Needs Improvement" to "Good" across most pages.
Search Visibility: Over 15,000 pages got indexed by Google that previously weren't appearing in search results. The AI-generated schema markup helped rich snippets appear for product searches.
Unexpected Discovery: The AI workflow actually caught technical issues I had missed during manual audits. It identified inconsistent product categorization, duplicate content issues, and broken internal linking patterns that human review had overlooked.
But the most significant result wasn't just the metrics—it was the time savings. What would have taken 6-8 months of manual work was completed in 2 weeks, allowing us to focus on content strategy and link building instead of basic technical maintenance.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons I learned from treating AI as digital labor rather than magic:
AI doesn't replace strategy—it amplifies execution. The quality of your AI outputs directly correlates to the quality of your inputs. Garbage prompts create garbage optimization.
Scale changes everything. Techniques that work for 100 pages break down at 10,000 pages. You need different approaches for different scales, and that's okay.
Human oversight remains essential. AI can process patterns quickly, but humans need to define those patterns and validate outputs. The goal is augmentation, not replacement.
Technical SEO at scale is a manufacturing problem. Once you accept this, you can build systems that maintain quality while operating efficiently.
Start with clear constraints. AI works best when you give it specific parameters and examples rather than vague instructions.
Test everything in small batches first. Even with AI, implement changes gradually to catch issues before they affect the entire site.
The biggest limitation isn't the technology—it's knowing what good SEO looks like. AI can't fix what you don't understand yourself.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies looking to implement AI-powered technical SEO:
Start with product page optimization—these typically follow predictable patterns
Focus on feature pages and integration documentation first
Use AI to scale content for multiple use cases and customer segments
For your Ecommerce store
For e-commerce stores implementing this approach:
Begin with product categories that have similar structures
Prioritize collection pages and category optimization
Use AI to optimize for seasonal and trending product searches