canonry

name	canonry
description	Agent-first AEO monitoring and operating platform.
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Canonry

Open-source AEO (Answer Engine Optimization) monitoring platform. Track how AI answer engines cite your domain across Gemini, ChatGPT, Claude, and Perplexity.

Website: ainyc.ai | Docs: github.com/AINYC/canonry

When to Use

• Tracking keyphrase citations across AI providers
• Running technical SEO audits (14‑factor scoring)
• Implementing structured data (JSON‑LD)
• Diagnosing indexing gaps via Google Search Console / Bing Webmaster Tools
• Optimizing llms.txt, sitemaps, robots.txt for AI crawlers
• Submitting URLs to Google Indexing API and Bing IndexNow
• Analyzing competitor citation patterns

Core Philosophy

• Measure outcomes — AI models are black boxes; track citations, don't assume causality
• Signal over noise — Focus on high‑intent queries; avoid granular targeting until base visibility exists
• CLI‑native — API‑driven changes over manual CMS clicks; faster, repeatable, auditable

Toolchain

canonry (AEO Monitoring)

# List projects
canonry project list

# Run a sweep (all providers)
canonry run <project> --wait

# Check per‑phrase citation status
canonry evidence <project>

# Show latest run summary
canonry status <project>

# Add/remove keyphrases
canonry keyword add <project> "polyurea roof coating"
canonry keyword remove <project> "best roof coating for a warehouse"

# Submit URLs to Bing
canonry bing request-indexing <project> <url>

# Submit to Google Indexing API
canonry google request-indexing <project> <url>

aeo-audit (Technical SEO Analysis)

# Run audit (JSON output)
npx @ainyc/aeo-audit@latest "https://example.com" --format json

# 14‑factor scoring includes:
# - Structured Data (JSON‑LD)
# - Content Depth
# - AI‑Readable Content (llms.txt, llms‑full.txt)
# - E‑E‑A‑T Signals
# - FAQ Content
# - Citations & Authority Signals
# - Definition Blocks
# - Technical SEO (H1, alt text, meta)

Google Search Console / Bing WMT

# GSC coverage summary
canonry google coverage <project>

# Bing coverage summary  
canonry bing coverage <project>

# Force refresh cached data
canonry google refresh <project>
canonry bing refresh <project>

Workflow

1. Diagnose

# Baseline AEO visibility
canonry run <project> --wait
canonry evidence <project>

# Technical SEO audit
npx @ainyc/aeo-audit@latest "https://client.com" --format json > audit.json

2. Prioritize

Gaps sorted by impact:

1. Missing H1 → immediate content patch
2. No structured data → JSON‑LD injection
3. Thin content → definition blocks ("What is…")
4. County‑level targeting → refine after base visibility
5. E‑E‑A‑T signals → Person schema, author tags (needs client input)

3. Execute

• Schema injection: LocalBusiness + FAQPage JSON‑LD via site‑appropriate method (Elementor Custom Code, theme hooks, etc.)
• Content patches: H1, meta title/description, image alt text via REST API or CMS
• AI‑readable files: Upload llms.txt, llms‑full.txt to site root
• Indexing requests: Submit all URLs to Google Indexing API + Bing IndexNow
• Keyphrase strategy: Trim to 8‑12 high‑intent queries; remove noise

4. Monitor

• Weekly canonry sweeps to track citation changes
• Correlate visibility shifts with deployment dates
• Watch for competitor displacement in keyphrases

5. Report

Clear, data‑first summaries:

“Lost emergency dentist brooklyn on Gemini — two competitors moved in. Here’s what to fix.”

Common Patterns

New Site (0 citations)

• Focus on indexing first: submit sitemap to GSC/Bing, request indexing
• Implement base schema (LocalBusiness, Service)
• Create llms.txt with service‑area details
• Trim keyphrases to 8‑12 core queries
• Expect 4‑8 weeks for first citations

Established Site (regression)

• Compare canonry runs to identify when loss occurred
• Check for recent competitor content or site changes
• Validate schema is still present and error‑free
• Re‑submit affected URLs to indexing APIs

County‑Level Targeting

# Service areas in llms.txt / schema
Michigan:
  - Oakland County (Troy, Auburn Hills, Pontiac)
  - Macomb County (Sterling Heights, Shelby Township)
  - Wayne County (Detroit, Dearborn)
  - Lapeer County (HQ: Almont)

Florida:
  - Miami‑Dade County (Miami, Coral Gables)
  - Broward County (Fort Lauderdale, Hollywood)
  - Palm Beach County (West Palm Beach, Boca Raton)

• Reference counties in schema areaServed and llms.txt
• Do not create separate keyphrases per county until base visibility exists

WordPress/Elementor Specifics

• REST API user with Application Passwords (/wp‑json/wp/v2/)
• Elementor data patched via _elementor_data meta field
• Schema injection via Elementor Pro Custom Code (elementor_snippet CPT)
• Yoast SEO title/description fields often NOT REST‑writable → manual WP Admin edit
• wp‑login.php may be hidden (security plugin) → file uploads require manual WP File Manager

Example: Full AEO Audit + Action Plan

# 1. Audit
npx @ainyc/aeo-audit@latest "https://client.com" --format json > audit.json

# 2. Parse score
cat audit.json | jq '.overallScore, .overallGrade'

# 3. Check AEO baseline
canonry status client-project
canonry evidence client-project

# 4. Generate action list
cat audit.json | jq -r '.factors[] | select(.score < 70) | "- \(.name): \(.score)/100 (\(.grade)) - \(.recommendations[0])"'

Boundaries & Safety

• Never touch live WordPress without explicit approval
• Back up ~/.canonry/config.yaml before any config edit
• Never fabricate citation data — if a sweep hasn’t run, say so
• Client data stays private — canonry repo is public; no real domains in issues
• Respect API rate limits — batch operations, avoid tight loops

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Tools: canonry v1.37+, @ainyc/aeo‑audit v1.3+
Website: ainyc.ai | Reference: AINYC AEO Methodology