How do you scale GEO efforts beyond a few pages? We need to optimize hundreds of pages efficiently

We faced the same challenge with 5,000+ pages. Here’s how we scaled:

The Tiered Approach:

Tier 1: Full Optimization (Top 100 pages)

• Complete restructuring
• Custom FAQ sections
• Original content additions
• 3-4 hours per page

Tier 2: Standard Optimization (Next 500 pages)

• Template-based restructuring
• Automated schema markup
• Heading optimization
• FAQ addition from template
• 45 minutes per page

Tier 3: Quick Wins (Next 1,000 pages)

• Schema markup only
• First paragraph optimization
• Heading structure check
• 15 minutes per page

Tier 4: Technical Only (Remaining pages)

• Programmatic schema
• Automated checks
• 2 minutes per page (automated)

Result:

• 100 pages x 4 hours = 400 hours (Tier 1)
• 500 pages x 0.75 hours = 375 hours (Tier 2)
• 1,000 pages x 0.25 hours = 250 hours (Tier 3)
• 3,400 pages automated = minimal hours (Tier 4)

Total: ~1,000 hours for 5,000 pages, not 20,000.

Templates are how you scale without losing quality.

Our Template System:

Content Type Templates:

• Product page template
• Blog post template
• Guide/how-to template
• Comparison page template
• FAQ page template

Each template includes:

• Required heading structure
• First paragraph format
• Content block types
• FAQ section placement
• Schema requirements

Writer Guidelines: One-page guide per template:

• What makes this content AI-friendly
• Example before/after
• Checklist for self-review

Editorial Checklist: Editors check:

• Answer in first paragraph
• Question-based headings
• Paragraphs under 4 sentences
• Lists where appropriate
• Table for comparisons
• FAQ section present
• Schema implemented

Result: New content is AI-optimized from creation. Old content follows template during updates. Editors catch issues before publication.

Schema markup can be fully automated at scale:

Our Programmatic Approach:

1. Content type mapping

• Identified 12 content types in our CMS
• Each type gets specific schema

2. Template-based injection

• CMS templates include schema generation
• Pulls from structured content fields
• No manual markup needed

3. Dynamic FAQ schema

• Any H2 ending in “?” becomes FAQ Question
• Following paragraph becomes Answer
• Automatically wrapped in FAQ schema

4. Validation layer

• Automated testing on publish
• Alerts if schema is invalid or missing
• Weekly crawl to catch issues

Implementation:

• 2 weeks developer time
• Now covers all new and existing pages
• Zero per-page effort for schema

Impact: All 3,000+ pages have proper schema without manual work.

Don’t optimize schema page by page. Solve it at the infrastructure level.

Batch processing workflow for Tier 2 optimization:

Weekly Batch: 25 Pages

Monday: Audit Batch

• Export 25 pages from priority list
• Run through audit tool
• Generate optimization recommendations

Tuesday-Wednesday: Optimization

• Content team works through batch
• Follow template and checklist
• Use recommendation tool as starting point

Thursday: QA

• Editor reviews batch
• Checks against AI-friendly criteria
• Sends back any issues

Friday: Publish + Track

• Deploy optimized pages
• Log in tracking system
• Monitor for issues

Metrics:

• 25 pages per week
• 100 pages per month
• 600 pages in 6 months

With 3 people, you can run 2-3 batches per week = 200-300 pages/month.

The key is consistency. Same process, same day, every week.

Ruthless prioritization is essential at scale.

Our Prioritization Formula:

Score = (Revenue Impact x 3) + (Traffic x 2) + (AI Gap x 2) + (Effort Inverse x 1)

Revenue Impact (1-5): 5 = Directly drives sales/leads 1 = Awareness only

Traffic (1-5): 5 = Top 10% traffic 1 = Bottom 50%

AI Gap (1-5): 5 = Competitors visible, we’re not 1 = We already rank well in AI

Effort Inverse (1-5): 5 = Quick to optimize 1 = Major restructuring needed

Scoring example: Product page: (5x3) + (4x2) + (5x2) + (4x1) = 37 Old blog post: (1x3) + (2x2) + (3x2) + (2x1) = 15

Optimize highest scores first.

This prevented us from wasting time on low-impact pages.

You can’t scale with 3 people. You need to scale through training.

Our Training Approach:

1. GEO Certification Program

• 2-hour training module
• Covers AI-friendly writing
• Template usage
• Self-check process

2. Trained 40 people

• Content writers (15)
• Product marketers (10)
• Subject matter experts (8)
• Support content team (7)

3. Distributed Ownership

• Each trained person owns a content area
• They optimize during normal content work
• New content is AI-optimized by default

4. Quality Control

• Monthly spot checks by core team
• Feedback loop for improvement
• Refresher training quarterly

Result: Instead of 3 people doing GEO, 40 people incorporate it into their work.

Scale comes from capability building, not just process.

AI tools can accelerate the work:

What we use AI for:

1. Content Analysis AI reviews page and identifies:

• Missing answer-first elements
• Long paragraphs to break up
• List candidates
• FAQ opportunities

2. Draft Generation AI suggests:

• Rewritten first paragraphs
• Question-based headings
• FAQ content based on page topic

3. Audit Automation AI scores pages on:

• Structure quality
• Snippability
• Answer-first compliance
• FAQ presence

The workflow:

1. Feed page to AI analysis tool
2. Review recommendations
3. Accept/modify suggestions
4. Human final review
5. Publish

Time savings: Tier 2 optimization: 45 min → 20 min per page

AI does the analysis and drafting. Humans do the judgment and approval.

You need measurement to scale effectively.

Track optimization ROI:

For each optimized page, track:

• Pre-optimization AI visibility
• Post-optimization AI visibility (30, 60, 90 days)
• Traffic changes
• Engagement changes

What we learned from measurement:

1. Some page types respond better

   • Product pages: +45% AI visibility avg
   • Blog posts: +25% AI visibility avg
   • Resource pages: +60% AI visibility avg

2. Diminishing returns after certain point

   • 80% of benefit from first 3 optimizations
   • Full restructuring vs quick wins: marginal difference

3. Some optimizations don’t work

   • Very old content with no external signals
   • Thin content regardless of structure

Scaling insight: Focus on page types with best response rate. Use quick wins for lower-priority pages. Skip optimization for poor-response categories.

Let data drive your scaling decisions.

This thread completely changed my approach. New scaling plan:

Infrastructure (Month 1):

• Implement programmatic schema via CMS
• Build content templates for each page type
• Create writer training program
• Set up batch processing workflow

Training (Month 2):

• Train content team on AI-friendly writing
• Distribute ownership across teams
• Create self-check checklists

Tiered Execution (Months 3-6):

• Tier 1: 100 pages, full optimization
• Tier 2: 500 pages, batch process
• Tier 3: 1,000 pages, quick wins
• Tier 4: Remaining, automated only

Measurement:

• Track AI visibility by tier
• Measure ROI by page type
• Adjust priorities based on results

Key insights:

1. Solve schema programmatically - don’t do page by page
2. Templates > individual optimization
3. Train others to scale beyond core team
4. Tiered approach - not everything needs full treatment
5. Measure to know what actually works

Thank you all - this is now actually achievable.