In 2026, interactions with large language models (LLMs) are part of how people discover and evaluate Web3 projects. Users ask tools like ChatGPT, Gemini, Claude or Perplexity questions that range from “Is this token legitimate?” to “What are good wallets for active traders?”. When an LLM answers, it draws on patterns in its training data and, in some cases, data indexed from the open web. For crypto companies, this creates a new visibility layer: being accurately cited by AI in response to relevant queries. Outset PR was one of the first crypto PR agencies to explicitly address this shift by offering AI Optimization (AIO) to its clients and improving how brands appear in AI-generated answers. What LLM visibility means When people search with Google, they see a ranked list of links. When asking an LLM a question, they receive a synthesized answer. In both cases, discoverability matters, but the mechanisms differ: Traditional SEO relies on links, keywords, and crawling. LLM visibility depends on the information the model learned during training plus any systems that connect the model to up-to-date sources. For crypto brands, LLM visibility can show up in LLM responses in several ways: The project is named in answers to topical queries (“Layer 2 networks with the strongest developer activity”). The model includes a correct brief explanation of what the project does. The model’s response reflects recent developments (e.g., token utility changes, funding rounds). If the model neither knows the brand nor can construct a factual description, the answer may default to generic alternatives or omit the brand entirely. Why visibility in LLMs matters for crypto brands People ask LLMs questions before making financial decisions, researching protocols, or comparing tools. When an LLM generates answers: Many users do not click through to source content. They assume the summary is accurate. The presence or absence of a brand in the answer influences perception. Being omitted or described incorrectly can suppress interest. Being cited accurately in relevant contexts increases the likelihood that a potential user or investor learns about the project. Unlike traditional media placements, LLM visibility operates through synthesized answers. It is not a ranking in search results, but the inclusion of your brand in responses to user queries. Outset PR and AI Optimization (AIO) Outset PR was among the first crypto-focused agencies to frame PR around AIO. It improves how brands are interpreted and cited by LLMs, using following techniques: Building a consistent narrative across crypto and mainstream tech media Prioritizing explanatory and analytical formats over promotional language Positioning founders and executives as quoted sources in industry coverage Ensuring brand descriptions remain stable and factual across publications The goal of AIO is long-term inclusion in the knowledge layer that AI systems draw from when generating answers. This approach reflects how discovery works today: LLMs rely on accumulated context, not campaign bursts. Other agencies active in AIO for Web3 companies Several other agencies operate in the crypto and Web3 PR space and contribute indirectly to LLM visibility through broad editorial coverage: Coinbound focuses on crypto-native media, influencer programs, and community-driven exposure. Serotonin works with infrastructure and protocol-level projects on narrative development and media strategy. MarketAcross emphasizes large-scale distribution and enterprise blockchain communications. ReBlonde helps Web3 brands reach both crypto and mainstream tech outlets. While these agencies may not frame their services explicitly around AIO, their work contributes to the public content ecosystem that LLMs learn from. Conclusion LLM visibility is becoming part of how crypto brands are evaluated, even if users are not aware of it. It depends on whether AI systems can find, recognize, and summarize a project based on the public record. If your goal is to have people discover your project through AI tools, the focus should be on clarity, accuracy, and sustained visibility in the public record. That is what supports inclusion in AI responses. Disclaimer: This article is provided for informational purposes only. It is not offered or intended to be used as legal, tax, investment, financial, or other advice.
