⚡ OmniGEO — Open-Source Generative Engine & Answer Engine Optimization (GEO/AEO) Platform

The Open-Source Standard for AI Brand Visibility, LLM Citations & Competitor Share-of-Voice

Audit, monitor, and optimize what ChatGPT, Perplexity, Claude, Gemini & DeepSeek say about your brand.

⭐️ If you find OmniGEO helpful, please support the project by dropping a Star on GitHub!
It helps us remain 100% free, community-driven, and open-source.

Live Interactive Demo • Product Walkthrough • CLI Tool • MCP Server • Feature Comparison

📸 Product Screenshots & Feature Walkthrough

OmniGEO brings enterprise-grade AI brand analytics into a clean, modern interface:

1. 📊 Executive Overview & Multi-Factor GEO Composite Score

Composite score (0-100), AI Visibility Rate, Share of Voice (SOV), Perception Index, Citability score, and automated algorithmic critiques.

2. 🤖 Multi-Engine Side-by-Side Consensus (5 Models)

Compare raw answers, mention ranking, and latency across Perplexity AI, ChatGPT (GPT-4o), Claude 3.7 Sonnet, Google Gemini 2.5, and DeepSeek R1 on real buyer-intent prompts.

3. 📈 Competitor Share of Voice (SOV %) Benchmark

Visualize your brand's recommendation frequency vs named market competitors on category-defining queries.

4. 🌐 Citation Attribution Graph & Backlink Gap Detection

Reverse-engineer which authoritative third-party sources (Reddit, G2, TechCrunch, GitHub, Hacker News) LLMs rely on, and flag high-priority citation gaps where competitors are cited instead.

5. 🛠️ Automated AEO Fixes, llms.txt & Schema Generators

Instant copy-ready /llms.txt standard file, JSON-LD Structured Data (SoftwareApplication, FAQPage, SpeakableSpecification), and Inverted Pyramid "Answer Nuggets" designed for zero-click AI snippet quoting.

Related MCP server: Citesurf MCP

💡 The Problem: Why Traditional SEO is Dying

Over 60% of organic searches now end in zero clicks. Users no longer browse ten blue links on Google—they ask ChatGPT, Perplexity, Claude, or Google AI Overviews directly:

• "What is the best issue tracker for scaling engineering teams?"

• "Top modern alternative to legacy payment platforms with good developer APIs?"

• "Which database should I use for a high-concurrency SaaS?"

If your product is not actively cited by AI search engines, your brand is effectively invisible to modern buyers.

Existing proprietary AI brand tracking platforms (Profound, Peec AI, Athena, Otterly) charge $500 to $2,500/month.

OmniGEO is 100% free, MIT-licensed, and self-hostable.

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Open Source Generative Engine & Answer Engine Optimization (GEO/AEO)

🎯 Core Features & Capabilities

• 🤖 5-Engine Multi-Model Consensus:

  • Perplexity AI (sonar-pro with live web search and online citation parsing)

  • ChatGPT / OpenAI (gpt-4o / gpt-4o-mini)

  • Anthropic Claude (claude-3-7-sonnet architectural and DX reasoning)

  • Google Gemini (gemini-2.5-pro search integration and community momentum)

  • DeepSeek (deepseek-r1 reasoning and price-performance benchmark)

• ⚡ Zero-Key Out-of-the-Box Sandbox:

  • Run complete audits instantly with zero API keys required. Uses an intelligent, data-driven heuristic consensus engine based on real web scraping and domain analysis.

• 🔑 Live API Mode:

  • Plug in your standard API keys (OPENAI_API_KEY, PERPLEXITY_API_KEY, GEMINI_API_KEY, ANTHROPIC_API_KEY) to run live queries against production AI answer engines.

• 📊 Share of Voice (SOV %):

  • Benchmark your brand against 3+ competitors across generic buyer-intent queries.

• 🌐 Citation Attribution Graph:

  • Maps third-party backlinks (Reddit discussions, G2 Grid reviews, GitHub repositories, Wikipedia entries) that models use to formulate their answers.

• 🛠️ One-Click AEO Content Fixes:

  • llms.txt Spec Generator: Modern root markdown index for AI crawlers.

  • JSON-LD Schema: Machine-readable entity specifications with FAQ and Speakable schema.

  • Inverted Pyramid Answer Nugget: Declarative copy snippet designed to be quoted verbatim by Perplexity and ChatGPT Search.

• 🌓 Interactive Light / Dark Theme:

  • Seamless theme toggle with local storage persistence and system preference detection.

• 💻 Terminal CLI:

  • Run headless audits in terminal or CI/CD pipelines via npm run cli audit <url>.

• 🔌 Model Context Protocol (MCP) Server:

  • Native MCP server for Cursor, Windsurf, Claude Desktop, and Claude Code.

📊 How OmniGEO Compares to XanLens & Closed SaaS

OmniGEO was specifically designed to overcome the friction, paywalls, and setup barriers found in tools like XanLens and commercial platforms:

🚀 Quick Start

1. Clone & Run Web App

# Clone repository
git clone https://github.com/moajez/omni_geo.git
cd omnigeo

# Install dependencies
npm install

# Start development server
npm run dev

Open http://localhost:3000 in your browser!

2. Optional: Add Live API Keys

To run live production queries with real-time web citations:

cp .env.example .env.local

Add your keys (all optional; if omitted, intelligent simulation mode runs automatically):

PERPLEXITY_API_KEY=pplx-xxxxxxxxxxxxxxxxxxxxxxxx
OPENAI_API_KEY=sk-proj-xxxxxxxxxxxxxxxxxxxxxxxx
GEMINI_API_KEY=AIzaxxxxxxxxxxxxxxxxxxxxxxxx
ANTHROPIC_API_KEY=sk-ant-xxxxxxxxxxxxxxxxxxxxxxxx

💻 CLI Usage

Audit any brand or URL directly from your terminal:

# Basic brand audit
npm run cli audit https://linear.app

# Audit with custom competitors
npm run cli audit https://stripe.com --competitors "Paddle,Adyen,Square"

# Export report as JSON
npm run cli audit https://supabase.com --json --out supabase-geo.json

🔌 Model Context Protocol (MCP)

OmniGEO includes a built-in MCP server that enables AI code editors (Cursor, Windsurf, Claude Desktop) to run GEO audits and generate llms.txt directly within your IDE.

Cursor / Windsurf / Claude Desktop Setup

Add this snippet to your claude_desktop_config.json or Cursor MCP settings:

{
  "mcpServers": {
    "omnigeo": {
      "command": "node",
      "args": ["/ABSOLUTE/PATH/TO/omnigeo/bin/mcp.mjs"]
    }
  }
}

Available MCP Tools

• omnigeo_audit_brand: Audits visibility score, competitor share-of-voice, and citation gaps.

• omnigeo_generate_llms_txt: Generates standard llms.txt file and JSON-LD schema for any brand.

📁 Repository Structure

omnigeo/
├── assets/screenshots/   # High-resolution UI screenshots for README
├── app/                  # Next.js 16 App Router (React 19 + Tailwind v4)
│   ├── api/audit/        # POST /api/audit (Multi-engine orchestrator)
│   ├── api/presets/      # GET /api/presets (1-click brand benchmarks)
│   ├── globals.css       # Tailwind CSS v4 styling & dark/light theme
│   ├── layout.tsx        # SEO metadata, OpenGraph tags & theme script
│   └── page.tsx          # Full-featured interactive dashboard
├── bin/
│   ├── cli.mjs           # Terminal CLI interface (commander)
│   └── mcp.mjs           # Model Context Protocol (MCP) Server
├── lib/
│   ├── scraper.ts        # Live HTML metadata, schema & category detection
│   ├── prompts.ts        # Buyer-intent & alternative query generator
│   ├── analyzer.ts       # Composite GEO scoring & SOV algorithm
│   ├── optimizer.ts      # llms.txt, JSON-LD schema & inverted pyramid copy
│   ├── mockData.ts       # 1-click test brand benchmarks (Linear, Stripe, Supabase)
│   └── engines/          # Perplexity, OpenAI, Claude, Gemini & Simulator
└── types/
    └── geo.ts            # TypeScript definitions for GEO audits

🗺️ Roadmap & Upcoming Milestones

• Multi-engine prompt auditing (Perplexity, ChatGPT, Claude, Gemini, DeepSeek)

• Zero-key simulation sandbox & live API mode

• Share of voice (SOV %) and competitor benchmarking

• Citation graph & Reddit/G2 gap detection

• 1-click /llms.txt and JSON-LD schema generation

• Model Context Protocol (MCP) Server

• Light & Dark theme toggle with persistence

• Automated weekly GitHub Actions cron for brand visibility alerts

• Slack & Discord webhook notifications when competitors overtake your ranking

• Local Ollama & LMStudio integration for 100% offline local model audits

🤝 Contributing

Contributions are warmly welcomed! Feel free to submit an issue or open a pull request:

1. Fork the Project

2. Create your Feature Branch (git checkout -b feature/AmazingFeature)

3. Commit your Changes (git commit -m 'Add some AmazingFeature')

4. Push to the Branch (git push origin feature/AmazingFeature)

5. Open a Pull Request

📄 License

Distributed under the MIT License. See LICENSE for more details.