Deep dives into generative search engine behaviors, visual audits, crawler architecture, and developer workflows.
Why I built Ultra Frog SEO Crawler—a high-performance, open-source alternative to Screaming Frog infused with MCP servers, Mistral AI, and real-time sitemap validation.
An analysis of how LLMs select and prioritize brand mentions in their training data, fine-tuning, and retrieval-augmented generation (RAG) contexts.
A deep dive into Answer Engine Optimization (AEO), why traditional SEO is no longer enough, and how to optimize content for generative search engine indexing.
A comparison of indexing mechanisms, retrieval algorithms, and user interaction patterns between legacy search engines and modern AI search agents.
A developer guide to measuring brand share-of-voice, citation frequency, and sentiment analysis within AI-driven search engine outcomes.
How AI visibility tools collect data: API-based collection vs real browser-based UI execution. A technical breakdown of their trade-offs.
All 30 AEO and GEO agencies compared side-by-side, evaluating their LLM citation performance, pricing, and optimization strategies.
Step-by-step guide to tracking brand visibility in AI search with specialized agents, evaluating citation shares and brand mentions.
Somewhere between talks on day one, I think it was during a hallway chat that I decided to run an aggressive crawl experiment using direct link scraping and BFS.
I took Meta AI's TRIBE v2 model and turned it into an automated image-to-intent classifier, enabling highly precise visual SEO auditing.
I had a simple question: what if I could talk directly to my search metrics? Here is how I loaded 16 months of queries and clicks into ChromaDB.