[ CASE STUDY ]
Budgeting App AI Search Case Study
How a Budgeting App Improved Search Visibility by Strengthening Its Public Citation Footprint
In just 3 days, using only 30 engagements, this campaign generated an estimated $3,336.09 in monthly branded value while expanding the app’s visibility across both search and AI-driven discovery.
Methodology Note
Directional estimate based on tracked keyword visibility and modeled paid-equivalent value. Not exact attribution.
Buyers compare budgeting apps across online community threads, video tutorials, review platforms, and increasingly inside AI-generated answers that pull from those same public sources. For a budgeting app, that means visibility is shaped not just by rankings, but by how the brand appears in the places buyers trust when they are actively comparing tools. CiteWorks Studio built a repeatable programme to improve that visibility.
[ KEY OUTCOMES ]
Results at a Glance
#8
average ranking position across the keyword set
47
pages with stronger brand context within the public citation environment AI systems commonly reference
173
high-value, intent-aligned keywords secured on page 1
352
tracked keywords with expanded visibility
[ MARKET CONTEXT ]
What Changed in the Market
The budgeting app category is no longer shaped by search alone. Users still search for phrases like “best budgeting app,” “expense tracker,” or “recurring expense tracker,” but they increasingly validate those choices through community threads, creator-led tutorials, review platforms, and AI-generated answers built from those same public sources.
That shift matters because AI systems often surface the brands that already appear consistently across the public web. A budgeting app can rank reasonably well and still miss recommendation-stage visibility if it is not represented in the discussions, reviews, and comparison contexts shaping both user perception and AI-generated answers.
In personal finance, trust carries extra weight. People want guidance that feels proven, balanced, and credible before they try a new tool.
[ THE CHALLENGE ]
What the Brand Needed
The app did not simply need more rankings. It needed stronger visibility in the places where product decisions are actually influenced. That meant improving three practical signals:
Comparative Visibility
Showing up more consistently in the environments where users actively compare budgeting tools
Citation Strength
Improving the brand’s representation across public pages and discussions that influence AI-generated recommendations
Discovery Presence
Appearing more often in budgeting, expense-tracking, recurring payment, and money-management conversations
The objective was not only to rank, but to be seen, validated, and more seriously considered when users were choosing between apps.
[ OUR APPROACH ]
What We Did
Focused on the moments where budgeting apps get compared
We identified the public platforms and discussion environments most likely to influence budgeting-app research, then aligned activity to the keywords and decision-stage conversations already shaping category demand.
Increased brand presence in trust-heavy public environments
We concentrated on natural brand placement inside discussions around budgeting, expense tracking, recurring payments, and financial planning so the app appeared more often in the same places users and AI systems were likely to encounter it.
Measured which actions translated into discoverability
We tracked how the campaign affected keyword visibility and cited-page influence, using search performance as supporting evidence that stronger public-source coverage was expanding broader discoverability.
“We weren’t just trying to rank for more keywords — we wanted to be more visible in the places people actually go to validate financial tools. CiteWorks helped us expand that footprint in a measurable way.”
— Marketing Team, Budgeting App
[ THE OUTCOME ]
Results
By improving presence in trusted discussions and review environments, the brand became easier to find during budgeting-tool research and better represented in the public sources that influence AI-generated comparisons.
173 high-value keywords on page 1
352 tracked keywords with broader visibility
#8 average ranking position
47 AI-relevant cited pages with stronger brand context
That created a more durable discovery advantage, positioning the app to be found and considered more consistently as users increasingly rely on a mix of search, public validation, and AI-assisted recommendations.
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.