Aurora is a research-driven platform focused on one emerging question: How do brands become visible inside AI-generated answers? As large language models increasingly mediate how people discover information, compare options, and make decisions, visibility no longer depends only on rankings, clicks, or ads. In many cases, users receive a single synthesized answer — with no links, no navigation, and optional attribution. Aurora exists to study and measure this new visibility layer. We focus on Generative Engine Optimization (GEO) — not as a set of tactics, but as a structural shift in how information is surfaced by AI systems. Our work centers on understanding when brands are mentioned, how they are represented, and why some entities are consistently included in AI answers while others remain invisible. Aurora helps make this layer observable and explainable. Instead of keywords and impressions, we look at: - Brand mentions and citations inside AI-generated responses - Consistency across prompts, contexts, and models - Accuracy of representation and category placement - Early signals of AI-mediated mindshare This is not a replacement for SEO or paid media. It is a complementary visibility layer, one that becomes critical when answers replace navigation and decisions happen upstream of websites. Aurora is used by agencies, operators, and strategy teams who already understand that GEO is real, but need a way to measure it, explain it, and defend it to clients and stakeholders. We approach this space with a research mindset: - Neutral, definition-first frameworks - Clear separation between education and promotion - Emphasis on legibility, not gaming models Our public work includes original writing on GEO, citation behavior in LLMs, prompt-driven demand, and why traditional visibility metrics fail in AI interfaces. Aurora's goal is simple: to make visibility inside AI-generated answers measurable — before it becomes impossible to ignore.

Aurora is a research-driven platform focused on one emerging question: How do brands become visible inside AI-generated answers? As large language models increasingly mediate how people discover information, compare options, and make decisions, visibility no longer depends only on rankings, clicks, or ads. In many cases, users receive a single synthesized answer — with no links, no navigation, and optional attribution. Aurora exists to study and measure this new visibility layer. We focus on Generative Engine Optimization (GEO) — not as a set of tactics, but as a structural shift in how information is surfaced by AI systems. Our work centers on understanding when brands are mentioned, how they are represented, and why some entities are consistently included in AI answers while others remain invisible. Aurora helps make this layer observable and explainable. Instead of keywords and impressions, we look at: - Brand mentions and citations inside AI-generated responses - Consistency across prompts, contexts, and models - Accuracy of representation and category placement - Early signals of AI-mediated mindshare This is not a replacement for SEO or paid media. It is a complementary visibility layer, one that becomes critical when answers replace navigation and decisions happen upstream of websites. Aurora is used by agencies, operators, and strategy teams who already understand that GEO is real, but need a way to measure it, explain it, and defend it to clients and stakeholders. We approach this space with a research mindset: - Neutral, definition-first frameworks - Clear separation between education and promotion - Emphasis on legibility, not gaming models Our public work includes original writing on GEO, citation behavior in LLMs, prompt-driven demand, and why traditional visibility metrics fail in AI interfaces. Aurora's goal is simple: to make visibility inside AI-generated answers measurable — before it becomes impossible to ignore.