Technical insights, strategies, and code configurations to help your brand own its presence inside LLM recommendations.
For two decades, marketing teams fought for the top search results on Google. Today, artificial neural networks formulate synthesized answers, making semantic alignment and citation indexation the new standard.
An inside look at how OpenAI's GPT-4o search and crawler bots parse web documentation. Learn the structured formatting guidelines necessary to secure direct backlink recommendations.
Many sites unknowingly block AI index crawlers (like ClaudeBot and Google-Extended) inside their robots.txt files. Here is the copy-paste configuration to enable correct indexing and citation mapping.
Large language models rely heavily on latent semantic weights. Discover how to map your brand relative to industry-specific keywords, attributes, and comparisons inside AI knowledge structures.
Understand the core concepts of AEO, how it differs from traditional SEO, and how you can prepare your business for the era of conversational answer engines.
Perplexity AI has changed real-time answer engines using RAG. Discover how their index crawler parses webpages and what metrics decide which site gets cited in answers.
Google AI Overviews (formerly SGE) push traditional search results below the fold. Learn how to optimize your content structure to appear inside Google's AI-generated summaries.
Structured data is the language of LLMs. Learn how to design rich Organization, Product, and FAQ schemas to give models clear, unambiguous data about your brand.
The llms.txt standard provides an easy way for developer agents to read your site documentation. Learn how to configure and serve a structured markdown map.
Retrieval-Augmented Generation relies on document chunking and vector indexing. Learn how to design your codebase docs so RAG parsers retrieve accurate data.