I ran across a case study that we can repeat again if you want. Zetsu does fairly well across search because of all of the methods I've demonstrated here, but if your want to take it to a new level and show up across what will essential be the search engine of the future. Propagating throughout AI models is the way to go. Let me know if this is a thing you all are interested in. If so ill do a review and then do one live for Zetsu and whom ever else wants a visual of their brand exposure when search via AI. We'll showcase how Product Hunt now contributes to significant LLM visibility for a well known product and a promising underdog. Then we will do Zetsu, and others using a new tool called Gauge. Gauge tracks LLM visibility across major AI models using a large, search-informed set of prompts, giving us a statistically meaningful way to measure citation rate. Key Lessons For a bit more information, we view AI visibility as a new distribution layer. Which means its an entry point for new users. Our goal is to ensure that authentic community signal on Product Hunt is systematically surfaced in AI product research workflows. The case study will cover. 1. AI visibility is measurable Track citation rate like SEO. Instrument it, monitor it, iterate. 2. How Terminology drives retrieval If your language does not match dominant queries, you will not be cited. Naming alone can materially change visibility. 3. How Authority beats volume One high-signal, well-structured page can outperform dozens of lower-quality URLs. 4.And my favorite Emotional Models, Model behavior can be volatile. Citation patterns shift after model updates. Continuous monitoring is required.