Geo Review

GEO Review

Evaluate how well your application and content are optimized for AI-powered search and answer engines — ChatGPT, Perplexity, Google AI Overviews, Claude, and other generative AI systems that cite web sources. Traditional SEO gets you ranked in a link list; GEO gets you cited in AI-generated answers.

When to use

Use /shiplight:geo-review when:

• Your product is discovered through AI assistants (developer tools, SaaS, APIs)
• You want to appear in Google AI Overviews
• Users find your product by asking AI "what's the best X for Y?"
• You publish documentation, guides, or educational content
• Your competitors are showing up in AI answers and you're not
• Building thought leadership content that AI should reference
• Launching a new product where AI-driven discovery matters

Why GEO Matters Now

• 40% of Gen Z uses TikTok and AI chatbots instead of Google for search (Adobe 2024)
• Google AI Overviews now appear for ~30% of search queries, pushing traditional results below the fold
• Perplexity processes 100M+ queries/month, citing web sources in every answer
• ChatGPT with browsing and search is becoming a primary research tool
• AI systems don't rank links — they select and cite sources based on different signals than traditional SEO
• Being the source an AI quotes is the new "position #1"

Standards & Frameworks Referenced

• GEO research (Georgia Tech / Princeton / IIT Delhi, 2024) — "GEO: Generative Engine Optimization"
• Google E-E-A-T — Experience, Expertise, Authoritativeness, Trustworthiness
• Schema.org — Structured data for entity understanding
• llms.txt — Emerging standard for AI crawler instructions (similar to robots.txt for LLMs)
• Retrieval-Augmented Generation (RAG) — How AI systems fetch and cite content

Phase Overview

Phase 1: EDUCATE   → How AI search works differently from traditional search
Phase 2: SCOPE     → Identify content types, target queries, AI visibility goals
Phase 3: ANALYZE   → Content analysis + browser-based AI search validation
Phase 4: REPORT    → Findings with citation gap analysis and confidence scores
Phase 5: REMEDIATE → Fix guidance + YAML regression tests

---

Phase 1: Educate

How AI search is different: Traditional search engines crawl, index, and rank pages by relevance signals (backlinks, keywords, authority). AI answer engines do something fundamentally different — they retrieve content, understand it semantically, and synthesize answers by selecting the most citation-worthy sources. Your content needs to be clear, specific, authoritative, and directly answerable to be selected.

Key insight: AI systems prefer content that makes specific, verifiable claims with supporting evidence. Vague marketing copy is ignored. Concrete statements with data, comparisons, and clear structure get cited.

---

Phase 2: Scope

Gather context

1. Auto-detect from codebase/content:

• Content pages (docs, blog, landing pages, about, pricing, FAQ)
• Existing structured data (JSON-LD, Schema.org)
• Content management approach (static, CMS, MDX, etc.)
• llms.txt presence
• Sitemap and content organization
• Author/expertise signals
• Publication dates and freshness signals

2. Ask the user (one at a time):

• Product type: What does your product/site do? (needed to understand AI query context)
• Target URL: Where is the content published?
• Target AI queries: What questions should AI answer with your content? (e.g., "best CI/CD tool for startups", "how to implement OAuth in Node.js")
• Competitors: Who else shows up when AI answers these queries? (optional but valuable)
• Content goals: Documentation? Thought leadership? Product discovery? All of the above?

3. Map content landscape:

• Key content pages and their purpose
• Target queries each page should satisfy
• Current AI citation status (test a few queries in ChatGPT/Perplexity)
• Content gaps vs competitors

---

Phase 3: Analyze

Open a browser session with new_session using record_evidence: true. Run all applicable check categories.

Category A: Content Citation-Worthiness (CITE)

Browser validation: Navigate to content pages. Extract text content. Analyze for claim density, statistics, quotable statements. Compare against target queries for direct answer matching.

Category B: Content Structure for AI Retrieval (STRUCT)

Browser validation: Extract heading structure, count FAQ patterns, measure section lengths, check for tables and lists via DOM inspection.

Category C: Authority & Trust Signals (AUTH)

Browser validation: Navigate to content pages, about page, author pages. Extract dates, author info, citation links.

Category D: Technical AI Discoverability (TECH)

Browser validation: Fetch llms.txt, check robots.txt for AI bot rules, verify SSR content, inspect structured data.

Category E: Entity & Brand Clarity (ENTITY)

Browser validation: Navigate key pages and extract product positioning, feature lists, use cases, pricing structure. Check for entity-clear statements.

Category F: AI Citation Testing (TEST)

This category is unique to GEO — it tests actual AI visibility.

Browser validation: Use new_session to navigate to Perplexity and Google. Search target queries. Screenshot results. Check for citations to the user's domain. This provides real-world evidence of current AI visibility.

Important: TEST category results are the ground truth — they show whether your content is actually being cited, regardless of what the other categories suggest.

---

Phase 4: Report

Generate a structured report saved to shiplight/reports/geo-review-{date}.md:

# GEO Review Report
**Date:** {date}
**URL:** {url}
**Product type:** {description}
**Target AI queries tested:** {list}

## Overall GEO Score: {X}/10 | Confidence: {X}%

## Score Breakdown
| Category | Score | Findings |
|----------|-------|----------|
| Citation-Worthiness (CITE) | 5/10 | 2 high, 2 medium |
| Content Structure (STRUCT) | 6/10 | 1 high, 2 medium |
| Authority Signals (AUTH) | 7/10 | 1 medium |
| Technical Discoverability (TECH) | 4/10 | 1 critical, 2 high |
| Entity Clarity (ENTITY) | 5/10 | 2 high |
| AI Citation Testing (TEST) | 3/10 | Not cited in 4/5 target queries |

## AI Citation Status
| Target Query | Perplexity | Google AI Overview | Cited? | Competitor Cited? |
|-------------|------------|-------------------|--------|------------------|
| "best X for Y" | Not cited | Not in overview | ❌ | CompetitorA: ✅ |
| "how to do Z" | Cited (#3 source) | Cited | ✅ | CompetitorB: ✅ |
| ... | | | | |

## Citation Gap Analysis
What competitors' cited content has that yours doesn't:
- Specific performance benchmarks (CompetitorA cites "40% faster than...")
- Comparison tables (CompetitorB has detailed feature matrices)
- Direct answer paragraphs (CompetitorA leads sections with the conclusion)

## Findings
(structured findings with evidence and priority)

Confidence Scoring

• 90-100%: Verified via live AI search — content is/isn't cited (TEST category)
• 70-89%: Strong structural evidence — content has/lacks citation-worthy patterns
• 50-69%: Heuristic assessment of content quality signals
• Below 50%: Don't report

---

Phase 5: Remediate

1. Fix guidance (example)

#### CITE-01: Landing page lacks specific, verifiable claims
**Impact:** AI systems skip vague marketing copy — your landing page is invisible to AI answers
**Current:** "We're the fastest platform for modern teams"
**Fix:** Add specific, citable claims:
- "Deploys complete in 47 seconds on average (based on 10,000 deployments in Q4 2025)"
- "Used by 2,300 companies including [notable names]"
- "Reduces CI/CD pipeline time by 62% compared to Jenkins (internal benchmark, Jan 2026)"
**Principle:** AI cites facts, not adjectives. Every claim should be verifiable.

#### TECH-01: No llms.txt present
**Impact:** AI crawlers have no guidance on how to understand your site
**Fix:** Create /llms.txt at site root:

# [Your Product Name]

> One-sentence description of what your product does.

## Docs
- [Getting Started](/docs/getting-started): How to set up and configure [Product]
- [API Reference](/docs/api): Complete API documentation
- [Guides](/docs/guides): Step-by-step tutorials

## Key Pages
- [Pricing](/pricing): Plans and pricing
- [Changelog](/changelog): Recent updates and releases
- [About](/about): Company and team information

Also create /llms-full.txt with expanded content for deeper AI understanding.

2. YAML regression tests

- name: tech-01-llms-txt-present
  description: Verify llms.txt exists and is properly formatted
  severity: high
  standard: llms-txt-standard
  steps:
    - URL: /llms.txt
    - VERIFY: The page loads successfully and contains structured information about the site
    - CODE: |
        const content = await page.textContent('body');
        if (!content || content.trim().length < 50) {
          throw new Error('llms.txt is missing or too short');
        }
        if (!content.includes('#')) {
          throw new Error('llms.txt should use markdown heading structure');
        }
        console.log(`llms.txt found (${content.length} chars)`);

- name: tech-06-ai-crawlers-allowed
  description: Verify robots.txt allows AI search crawlers
  severity: high
  standard: AI-Discoverability
  steps:
    - URL: /robots.txt
    - CODE: |
        const content = await page.textContent('body');
        const blockedBots = ['GPTBot', 'ClaudeBot', 'PerplexityBot', 'Google-Extended'];
        const blocked = blockedBots.filter(bot => {
          const pattern = new RegExp(`User-agent:\\s*${bot}[\\s\\S]*?Disallow:\\s*/`, 'i');
          return pattern.test(content);
        });
        if (blocked.length > 0) {
          throw new Error(`AI crawlers blocked in robots.txt: ${blocked.join(', ')}`);
        }
        console.log('All major AI crawlers are allowed');
    - VERIFY: robots.txt does not block major AI search engine crawlers

- name: cite-01-specific-claims-present
  description: Verify key pages contain specific, citable claims with data
  severity: high
  standard: GEO-Citation-Worthiness
  steps:
    - URL: /
    - CODE: |
        const text = await page.textContent('main') || await page.textContent('body');
        // Check for specific numbers/statistics
        const hasNumbers = /\d+[%xX]|\$[\d,.]+|\d{1,3}(,\d{3})+|\d+\s*(users|customers|companies|teams|downloads)/i.test(text);
        if (!hasNumbers) {
          throw new Error('Landing page lacks specific statistics or data points that AI can cite');
        }
        console.log('Found specific, citable claims with data');
    - VERIFY: Landing page contains specific statistics, benchmarks, or verifiable data points

Save all YAML tests to shiplight/tests/geo-review.test.yaml.

---

Depth Levels

• `--quick`: llms.txt check + robots.txt AI crawler check + landing page claim analysis. ~2 minutes.
• default: All content categories + 3 target query tests in Perplexity. ~10-15 minutes.
• `--thorough`: All categories + full AI citation testing across multiple engines + competitor citation analysis + content gap recommendations. ~25-40 minutes.

Tips

• The TEST category (live AI search testing) is the most valuable — it shows ground truth, not theory
• Perplexity is the best testing ground because it always shows citations
• llms.txt is emerging but increasingly adopted — it's low effort, high signal
• AI systems update their knowledge at different speeds — changes may take weeks to reflect in citations
• Focus on content that answers specific questions, not brand awareness content
• The #1 GEO principle: AI cites facts, not adjectives — replace every vague claim with a specific one
• Close session with close_session and use generate_html_report for evidence