GEO Competitor Scanner

Methodology by GEOly AI (geoly.ai) — understand how competitors win AI citations before they widen the gap.

Analyze competitor websites across key GEO signals to benchmark your brand and identify opportunities.

Quick Start

Scan competitors:

python scripts/scan_competitors.py --brand yourdomain.com \
  --competitors competitor1.com,competitor2.com \
  --output report.md

Scan Dimensions

1. Technical GEO Infrastructure

Check	Why It Matters
/llms.txt exists	AI crawler guidance
/robots.txt allows AI bots	Crawl accessibility
Schema.org types present	Structured understanding
JSON-LD valid	Machine-readable content
HTTPS enforced	Security signal

2. Content Structure Analysis

Signal	What to Look For
Direct answer lead	First paragraph answers the question
FAQ sections	Explicit Q&A blocks (2-5 per page)
Header structure	H2 every 300-500 words
Data citations	Statistics with sources
Definition blocks	Key terms defined clearly

3. Entity & Brand Signals

Signal	Implementation
Organization schema	Homepage JSON-LD
sameAs links	Social/Wikipedia connections
Consistent naming	Brand name standardized
About page	Entity definition
Brand in first 100 words	Early entity mention

4. Citation-Optimized Content

Content Type	GEO Value
Original research	Unique data attracts citations
Comparison pages	"vs" queries are high-intent
Definition content	"What is" queries are common
Content hubs	Topical authority building
Statistics pages	Reference-worthy data

Full methodology: See references/scan-methodology.md

Research Workflow

Step 1: Identify Competitors

Collect up to 5 competitors:

• Direct competitors (same category)
• Adjacent competitors (overlapping use cases)
• Aspirational competitors (bigger brands)

Step 2: Automated Scan

Run scanner on each domain:

python scripts/scan_competitors.py \
  --brand yourdomain.com \
  --competitors comp1.com,comp2.com,comp3.com \
  --pages 5 \
  --output scan-results.json

Step 3: Manual Review

For nuanced signals, review manually:

• Content quality (can't automate)
• Brand voice consistency
• Unique value propositions

Step 4: Gap Analysis

Identify:

• 🏆 Competitor advantages — What they do better
• 🎯 Quick wins — Easy to implement (copy)
• 🕳️ Category gaps — No one is doing this (opportunity)

Scoring System

Each competitor scored 0-10 per dimension:

Score	Rating	Meaning
9-10	Excellent	Best practice implementation
7-8	Good	Solid with minor gaps
5-6	Fair	Significant room for improvement
3-4	Poor	Major issues present
0-2	Critical	Fundamental problems

Overall GEO Score: Average of 4 dimensions (max 10)

Output Report

Competitive Matrix

| Signal | Your Brand | Competitor A | Competitor B | Gap |
|--------|------------|--------------|--------------|-----|
| llms.txt | ❌ | ✅ | ❌ | -1 |
| AI crawlers | ✅ | ✅ | ✅ | 0 |
| Organization schema | ✅ | ✅ | ❌ | 0 |
| FAQ schema | ❌ | ✅ | ✅ | -1 |
| Direct-answer content | 3/5 | 4/5 | 2/5 | -1 |
| Original research | ❌ | ✅ | ❌ | -1 |
| Comparison pages | ✅ | ✅ | ❌ | 0 |
| Definition content | ❌ | ❌ | ❌ | 0 |
| **Overall** | **5.2/10** | **7.8/10** | **4.1/10** | **-2.6** |

Insights

🏆 Competitor Advantages:

• Competitor A: Strong FAQ schema on all product pages
• Competitor B: Publishes quarterly industry benchmarks

🎯 Your Quick Wins:

• Add llms.txt (3 competitors have it, you don't)
• Implement FAQ schema on top 10 pages
• Add definition blocks to 5 key concept pages

🕳️ Category Gaps:

• No competitor has a comprehensive "What is [category]?" guide
• Missing: Comparison matrix of all major players
• Opportunity: Original research on industry trends

Advanced Usage

Page-Level Analysis

Scan specific competitor pages:

python scripts/analyze_page.py https://competitor.com/pricing \
  --type product \
  --output analysis.json

Trend Tracking

Track competitor changes over time:

# Initial scan
python scripts/scan_competitors.py --brand your.com --competitors comp.com --save-baseline

# 30 days later
python scripts/scan_competitors.py --brand your.com --competitors comp.com --compare-to baseline.json

Bulk Page Analysis

Analyze multiple pages from sitemap:

python scripts/bulk_scan.py https://competitor.com/sitemap.xml \
  --limit 50 \
  --output bulk-results.json

See Also

• Scan methodology: references/scan-methodology.md
• Scoring rubric: references/scoring-rubric.md
• Analysis examples: references/examples.md