Optimizing for Generative Engine Optimization (GEO) with GeoTracker

I built a SaaS to measure AI visibility (GEO) — here’s how it works

GeoTracker is a web application designed to analyze how AI models perceive and mention specific companies. The system evaluates brand visibility across major models including OpenAI’s GPT, Google Gemini, and Anthropic Claude.

Why This Matters

As users shift from traditional search engines to AI-driven query layers, SEO alone is no longer sufficient for digital discovery. Technical teams must now implement Generative Engine Optimization (GEO) to ensure brands are cited as reliable sources within LLM-generated responses, avoiding complete invisibility in the new search landscape.

Key Insights

• Multi-model analysis tracks brand mentions and competitor dominance across OpenAI and other major LLM providers.
• Source extraction identifies the specific domains LLMs use to generate authoritative answers for brand-related queries.
• Automated batch testing simulates real-world user queries to identify visibility gaps in specific industries like aerospace.
• The technical architecture utilizes FastAPI and PostgreSQL for high-performance backend processing and data persistence.
• Actionable GEO recommendations help teams pivot from keyword density to citation-based authority for LLM retrieval.

Practical Applications

• Use Case: Aerospace suppliers using GeoTracker to identify why competitors dominate AI-generated recommendations for niche components like slip rings. Pitfall: Relying on legacy SEO metrics which fail to account for LLM training data or RAG-based retrieval sources.
• Use Case: Marketing teams generating automated PDF reports to visualize brand perception trends and citation sources across multiple AI models. Pitfall: Manual testing of individual prompts which lacks the scalability of batch processing and historical trend analysis.

References:

• https://dev.to/n8labs/i-built-a-saas-to-measure-ai-visibility-geo-heres-how-it-works-7o0