Summary

GEORank is an open-source GEO (Generative Engine Optimization) workbench designed to enhance website visibility in AI search. It helps teams diagnose AI search performance, generate actionable plans, keyword strategies, structured data, and content assets, targeting SEO, content, and growth professionals.

How It Works

Utilizing a monorepo architecture (Next.js frontend, FastAPI backend), GEORank orchestrates a full-cycle GEO workflow: Discover, Diagnose, Q&A, Plan, Expand, Structure, and Manage. Key advantages include its self-hosting capability, enabling users to deploy on their infrastructure and manage data securely. It supports flexible API strategies, allowing configuration of custom OpenAI-compatible model API keys, API pooling, and failover mechanisms to reduce costs and ensure privacy. Data services encompass PostgreSQL, Redis, Qdrant, Neo4j, and MinIO.

Quick Start & Requirements

Installation requires pnpm install, copying .env.example to .env, and running docker compose -f docker-compose.yml -f docker-compose.dev.yml up -d. Local development uses pnpm dev:web and pnpm dev:admin. A prerequisite is configuring compatible AI model APIs (OpenAI format) via .env or the admin backend. Docker is necessary.

Highlighted Details

• End-to-end GEO workflow from diagnosis to content generation.
• Self-hosting priority with custom API key management and data control.
• Extensible monorepo design (Next.js, FastAPI).
• Integrated data services (PostgreSQL, Redis, Qdrant, Neo4j, MinIO).

Maintenance & Community

A roadmap is available, detailing planned enhancements. No specific community channels or notable contributors/sponsorships are mentioned.

Licensing & Compatibility

Code is licensed under Apache-2.0, permissive for commercial use. However, expert profiles, content, and built-in pages have additional rights specified in DATA_LICENSE.md, requiring careful review for specific integrations.

Limitations & Caveats

The open-source repository excludes sensitive production assets like real API keys, production data, private content, and user-specific records. Users are responsible for data compliance and model service costs. The project does not guarantee specific search rankings or AI recommendations.