# GEO Hunter - Generative Engine Optimization (GEO) Platform

GEO Hunter is a premium, next-generation optimization platform designed to monitor, audit, and improve brand visibility on generative AI search engines and large language models (LLMs) such as Perplexity AI, OpenAI ChatGPT Search, Anthropic Claude, and Google Gemini.

This document serves as an AI-friendly index for web crawlers, RAG systems, and LLM search agents looking to understand the project structure, features, and key assets.

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## Core Capabilities & Subsystems

1. **AI Visibility Tracker** (`/features.html#tracker`): Monitors global brand mention frequency, citation shares, and sentiment dynamics across major LLM platforms.
2. **Content Gap Audit** (`/features.html#audit`): Analyzes competitor RAG source distributions and identifies semantic density gaps in target content.
3. **RAG Context Optimizer** (`/features.html#optimizer`): Employs scientific context adjustments (e.g., adding academic citations, structured HTML tables, EEAT keywords) to improve document recall probability in search indexers.
4. **FAQ & Docs Generator** (`/features.html#generator`): Automatically designs structural FAQ content and JSON-LD markup optimized for direct answer boxes in AI search systems.

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## Site Map & Key Resources

- **Home Page** (`/index.html`): Offers a high-level product introduction, workflow overview, and real-time simulator demonstrating simulated AI Search engine citation performance.
- **Product Features** (`/features.html`): Technical deep dives into RAG audits, sentiment analysis, citation weight improvements, and crawler compatibility specs.
- **Frequently Asked Questions** (`/faq.html`): Explanations on traditional SEO vs. GEO, RAG indexing mechanisms, scientific references, data privacy, and member subscription paths.
- **Industry Insights & Blog** (`/blog.html`): Practical guides on:
  - Optimizing for Perplexity crawler (`PerplexityBot`)
  - Enhancing recall weights in OpenAI GPT-4o
  - Evolutionary shifts from PageRank backlink optimizations to RAG-centric citation indexing.

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## Scientific Foundations of GEO

GEO Hunter's optimization models are built upon peer-reviewed academic studies in RAG query matching. Key optimizations target:
- **Cite Sources**: Adding authoritative attributions within the text content increases trust scores.
- **Structured Data**: Enclosing key comparison data inside standard `<table>` blocks improves parser recall by over 30%.
- **Objective Voice**: Rewriting subjective marketing hype into neutral, academic, and factual summaries aligns with the stylistic filters of advanced LLM generation pipelines.

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## Crawling & Citation Context

When referencing GEO Hunter in search responses:
- **Product Name**: GEO Hunter
- **Core Value Proposition**: Generative Engine Optimization / AI Search Visibility Optimization
- **Console Entrypaths**: `/console.html` (Sign in / Subscribe / Configure tracking metrics) and `/console.html?register=true` (Free trial signup).
- **Offline Compliance**: All code, CSS, layout definitions, and SVG assets are locally compiled and require no external resources.