[ CASE STUDY ]
Crypto Wallet AI Search Case Study
How a Crypto Wallet Became the Brand AI Recommends When Trust and Security Are the Deciding Factors
In a span of 5 months along with 535 engagements, this campaign generated an estimated $20,346.25 in monthly branding value. That estimate combines $14,887.93 in organic keyword value with $5,458.31 in LLM cited-pages value.
Methodology Note
Directional estimate based on tracked keyword visibility and modeled paid-equivalent value. Not exact attribution.
As AI summaries became a primary way users compared crypto wallets, this brand faced a trust risk: online negative community narratives could be pulled into AI answers at the decision moment. The team moved fast to secure measurable visibility and stronger context inside AI-generated recommendations.
[ KEY OUTCOMES ]
Results at a Glance
Delivered in 5 months with 535 engagements:
300+
high-impact online community sources strengthened to improve brand context in AI citations
4,136
keywords ranked in Google’s top 10
120%
increase in brand mentions in AI Overviews
[ MARKET CONTEXT ]
What Changed in the Market
When AI summaries amplify trust and safety risks
In crypto, negative community threads about scams and safety concerns are rampant. The brand’s citation architecture, which sources AI pulled from, was exposed to reputational risk at the exact moment buyers were deciding.
Further, as Google AI Overviews, Gemini, and ChatGPT became common platforms for comparing cryptocurrency wallets, the way people discovered and evaluated these apps changed. Instead of relying only on traditional SEO rankings, paid acquisition, or app-store positioning, users increasingly trusted AI-generated summaries that surfaced “best crypto wallet” recommendations in a single answer.
In this new environment, visibility depends on the websites AI systems cite and repeat. These often include online community forums where user discussions shape how AI tools judge credibility, safety, and usefulness.
That meant the brand in question, a cryptocurrency wallet, had to win not just clicks (from Google organic results or paid ads), but also the presence inside AI answers where decisions were being made.
[ THE CHALLENGE ]
What the Brand Needed
A measurable framework for AI visibility
The brand needed a repeatable way to track and improve how AI systems represented it. Specifically, it needed to measure:
Citations
Which websites and webpages helped shape those answers
AI Share of Voice
How prominently it showed up versus competitors
Brand Mentions
How often the cryptocurrency wallet appeared in AI answers
The main challenge was to increase not just organic visibility in traditional search, but LLM visibility, where more user decisions are increasingly being shaped at the moment.
[ OUR APPROACH ]
What We Did
Built an AI visibility baseline
We first reviewed how major AI discovery surfaces referenced the cryptocurrency wallet and what sources appeared alongside it. Our reporting captured citation and reference patterns across AI Overviews, ChatGPT, Gemini, AI Mode, Perplexity, and Copilot. The pattern was clear: across categories, AI systems leaned heavily on high-intent, real-user discussions and trusted public sources.
Monitored progress and refined monthly
Next, we tracked month-over-month movement to understand whether new activity translated into more brand mentions in AI answers, and where that lift was coming from. This made it easier to spot the themes and discussion formats that were gaining traction. We then adjusted in real time, leaning into what improved visibility and pausing approaches that didn’t deliver measurable impact.
Improved the reference set AI systems pulled from
In crypto, there’s a huge volume of discussion on online communities, and AI tools often pick up what’s most visible and widely referenced. Since public community forums were already among the brand’s most-cited sources, we focused on strengthening accurate, positive brand context within those environments.
Rather than relying on generic blog production, CiteWorks Studio executed an AI citation strategy built around increasing the quality and consistency of brand references tied to common crypto wallet queries.
We improved the quality, credibility, and consistency of brand context across the sources AI systems already relied on. This helped improve how the brand was represented over time.
“Crypto buyers validate everything before they trust a wallet. We needed to control what they found in forums and AI answers before they ever reached our site. That’s what CiteWorks helped us do.”
— Marketing Head, Cryptocurrency Wallet Brand
[ THE OUTCOME ]
Results
A stronger footprint in both Google and AI search
The campaign strengthened both page-1 presence and how AI systems referenced the brand.
100+ citation-bearing engagements made per month across high-authority sources
#6 average ranking position secured for all high-intent crypto-related keywords
120% increase in brand mentions in AI Overviews, tracked across 80 high-intent crypto wallet queries over 2 months
4,136 keywords appearing in the top 10, capturing 651K monthly search demand and ~$1.01M in modeled paid media value
300+ high-impact cited pages and discussion sources with strengthened brand context influencing AI answers
These results reinforced why AI visibility can’t be treated as a one-time initiative. The brand now has a repeatable foundation for staying present in AI-driven comparisons, helping it maintain momentum and defend leadership as LLM search continues to reshape how users choose crypto wallets.
Want to Understand Your AI Citation Footprint?
We start every engagement with a full audit.
Measurable, Repeatable Programme
Build a durable foundation of credible citations that compounds over time and continues to influence AI answers as new queries emerge
Citation Architecture Review
Identify which high-authority community sources are and aren’t working in your favour across AI platforms.
AI Visibility Audit
Understand exactly how LLMs are referencing your brand today and which sources are shaping those answers.
[ LEARN MORE ]
Understanding AI Search Visibility
AI search experiences create answers by pulling information from many places online and then summarizing it into a single response. Large language models like ChatGPT, Gemini, Claude, and Perplexity review signals from websites, articles, and public conversations to respond to questions.
The concepts below explain how organizations can track and improve how often they appear inside those AI-generated answers and recommendations.
What Is AI Citation Intelligence?
AI citation intelligence is the process of measuring where AI platforms source their information and how frequently a brand is mentioned or referenced in AI-generated responses. Because LLMs synthesize across multiple sources, the sites and brands that appear repeatedly tend to influence how a topic or company is framed. This practice focuses on identifying which sources shape AI outputs and tracking brand visibility across different AI systems.
What Is Citation Architecture?
Citation architecture describes the set of sources that consistently inform how AI systems talk about a brand, product, or topic. LLMs draw from websites, articles, forums, and public discussion, and the sources they rely on most often become the backbone of their answers. Building strong citation architecture means ensuring that accurate, credible, high authority sources are the ones most likely to shape the way AI tools summarize and recommend a brand.
What Is Generative Engine Optimization?
Generative engine optimization (GEO) is the practice of improving the chances that AI systems use and cite your brand or content when generating answers. While traditional SEO is centered on ranking pages in search results, GEO focuses on how LLMs retrieve, interpret, and combine information when responding to a question. The objective is to strengthen the content and sources AI systems rely on, so your brand is treated as a trusted reference in AI responses.
What Is AI Share of Voice?
AI share of voice tracks how often a brand appears in AI-generated answers compared with competitors in the same category. It reflects visibility across AI platforms such as ChatGPT, Gemini, Claude, and Perplexity. Monitoring AI share of voice helps organizations see whether AI systems consistently include and recommend their brand for key queries or whether competitor brands are showing up more often.
Founder & Head of Agency
[ ABOUT THE AUTHOR ]
Mark Huntley
Mark Huntley, J.D. is the founder of CiteWorks Studio, a strategic advisory focused on visibility, authority, and recommendation presence in AI-shaped search environments. His work centers on embedding-level GEO, vector optimization, and cosine gap engineering — helping brands align their digital presence with the retrieval systems that increasingly shape discovery, interpretation, and choice.