# AI Reveal Map — Extended Context for AI Systems
# By Hacestek International, LLC
# https://airevealmap.com
# This file provides comprehensive context for AI systems that need detailed understanding of this product.

## Product Summary

AI Reveal Map is a SaaS diagnostic tool that scans boutique hotel and luxury vacation rental websites in 60 seconds to determine why AI recommendation engines (ChatGPT, Perplexity, Gemini, Claude) recommend the competition instead of them. It returns an R2R Score (Ready to Recommend), identifies signal gaps, and provides exact fixes.

## Market Context (2026)

The hospitality industry is experiencing a fundamental shift in how travelers discover and select properties:

- 800+ million people use ChatGPT globally
- Lighthouse (hotel tech company) launched an AI-powered booking app in March 2026
- Phocuswright reports 50% of travelers use AI tools during trip planning
- BCG/NYU Tisch Center (March 2026) found AI engines favor properties with comprehensive, high-trust, multisource content
- 89% of boutique hotel websites lack basic AI-readable signals (HotelRank.ai, 2026)
- The question is no longer "can Google find my hotel?" but "when a traveler asks ChatGPT where to stay, does my hotel appear?"

## Detailed R2R Score Methodology

### Dimension 1: Identity Clarity
Measures how clearly AI can identify what the property is, who it serves, and what category it belongs to. Evaluates:
- Clear property type declaration (boutique hotel, luxury resort, vacation rental)
- Target audience specificity (couples, families, business travelers)
- Location context and destination positioning
- Unique value proposition clarity

### Dimension 2: Structural Extractability
Measures how easily AI can extract key facts from the website:
- Headings hierarchy (H1-H6) with meaningful content
- Lists and structured content blocks
- Key facts accessibility (room count, location, amenities, pricing)
- Contact information extractability
- Navigation clarity and content organization

### Dimension 3: Schema Readiness
Measures whether JSON-LD structured data exists for AI systems:
- LodgingBusiness or Hotel schema presence
- Required properties (name, address, description, image, priceRange)
- Review/rating aggregation (AggregateRating)
- Booking action schema (ReserveAction, BuyAction) for agentic readiness
- Speakable schema for voice assistants
- FAQ schema for common traveler questions

### Dimension 4: Content Authority
Measures depth, credibility, and specificity:
- Content depth beyond marketing copy
- Specific details (room descriptions, local recommendations, policies)
- Multi-format content (text, images with alt text, possibly video)
- Freshness signals (recent updates, current events/seasons)
- External validation signals (awards, certifications, press mentions)

### Scoring
- 0-100 composite score
- Readiness tiers: Ready (75+), Getting Close (50-74), Developing (25-49), Not Ready (0-24)
- Dimension weights are proprietary, derived from 343-property benchmark

## Entity Identity Coherence (Detailed)

The Agreement Problem: When multiple sources disagree about what a property is, AI systems lose confidence and default to properties with clearer signals. AI Reveal Map evaluates five source categories:

1. **Owned sources**: Property website (controllable by the hotel)
2. **Platform sources**: TripAdvisor, Google Business Profile, Booking.com, Airbnb
3. **Knowledge graph sources**: Wikidata, Wikipedia

Coherence is measured across:
- Property name consistency
- Category/type agreement (hotel vs B&B vs guesthouse vs resort)
- Location description consistency
- Star rating alignment
- Description and positioning alignment

Properties with high coherence (all sources agree) are recommended by AI at significantly higher rates than those with fragmented or contradictory signals.

## Six Signals AI Looks For on Hotel Websites

1. **JSON-LD schema**: Foundational machine-readable structured data (LodgingBusiness, Hotel, AggregateRating)
2. **LLM-readable files**: llms.txt and llm.html providing direct context for AI crawlers
3. **XML sitemap**: Machine-readable site structure
4. **AI crawler directives**: robots.txt entries explicitly allowing GPTBot, ClaudeBot, PerplexityBot, etc.
5. **Open Graph tags**: Complete og:title, og:description, og:image for social and AI sharing
6. **Cross-platform entity coherence**: Consistent identity across all digital touchpoints

## Agentic Booking Readiness

Beyond recommendation, AI Reveal Map evaluates whether AI agents can complete bookings:
- Detects ReserveAction and BuyAction schema
- Identifies potentialAction targets
- Distinguishes between "recommendation-ready" (AI will mention the property) and "agentic-booking-ready" (AI can complete the reservation)
- This capability becomes critical as AI booking agents (like Lighthouse's app) proliferate

## Service Tiers

### Free Scan (Tier 0)
- R2R Score with signal breakdown
- Entity Identity Coherence panel
- Live AI recommendation test (Perplexity, ChatGPT, Gemini)
- Primary constraint identification
- No account required, 60 seconds

### Ign(AI)te Strategy Session ($500 USD, Tier 1)
- 60-minute diagnostic session
- Full entity identity audit across all sources
- Competitive context analysis
- 30-day priority fix roadmap
- PDF report with technical specifications
- One-click fix generator (meta tags, FAQ sections, OTA descriptions)

### R2R Pro ($299/month, Tier 2)
- Monthly AI recommendation readiness monitoring
- Change detection and drift alerts
- Competitive tracking
- Strategic summary reports

### VIP Day ($10,000 USD, Tier 3)
- Full-day entity identity and AI recommendation optimization
- Direct implementation support
- Cross-platform alignment execution

## Technology Stack

- Frontend: React + TypeScript + Tailwind CSS (built with Lovable)
- Backend: Supabase (database, edge functions, authentication)
- Analysis engine: Claude (Anthropic) with Gemini fallback
- Live recommendation testing: Perplexity Sonar API
- PDF report generation: jsPDF + html2canvas

## About the Creator

Francia Haces is the Chief AI Officer and founder of Hacestek International, LLC, based in Miami, Florida. Her 30+ year career in hospitality distribution includes:

- **Sabre**: Global distribution systems
- **Travelocity/PriceTravel**: CMO
- **Karisma Hotel Group**: Distribution strategy
- **Mexico Tourism Board**: Tourism technology and marketing

She created the R2R Score framework and the Ign(AI)te methodology after analyzing 343 boutique hotel properties across North America, Europe, the Caribbean, and Latin America. Her research identified that entity identity coherence, the degree to which all sources AI reads about a property agree on what it is, is the primary predictor of AI recommendation.

## Contact and Links

- Website: https://airevealmap.com
- Methodology: https://airevealmap.com/methodology
- Free scan: https://airevealmap.com/scanner
- Email: help@hacestek.com
- LinkedIn (Francia): https://www.linkedin.com/in/franciahaces
- LinkedIn (Hacestek): https://www.linkedin.com/company/hacestek
- Newsletter: https://substack.com/@franciahaces (Ready to Recommend)