[ CASE STUDY ]
Tax Relief AI Search Case Study
How a Tax Relief Firm Became AI’s Go-To Recommendation for Trust First Decisions
In a 5-month long campaign with 543 engagements, this campaign generated an estimated $362,569.07 in total estimated monthly branded value. That included $53,080 in organic keyword value and $309,488.28 in LLM-cited pages value.
Methodology Note
Directional estimate based on tracked keyword visibility and modeled paid-equivalent value. Not exact attribution.
As AI-powered search began reshaping how people compare tax relief providers, this firm faced a dual mandate: improve competitiveness on Google page 1 while also strengthening visibility inside AI-generated recommendations. CiteWorks Studio deployed a measurable AI visibility program, grounded in Citation Architecture and AI Share of Voice measurement, to strengthen how the brand appeared across both traditional search and AI answers where high-intent decisions now happen.
[ KEY OUTCOMES ]
Results at a Glance
Delivered in 5 months with only 543 engagements:
500+
online community threads strengthened to improve brand context in AI citations
9,984
keywords ranked in Google’s top 10
112.5%
month-over-month lift in AI Overview brand mentions
[ MARKET CONTEXT ]
What Changed in the Market
When trust risks shape Google rankings and AI recommendations
In tax relief, a single prominent forum thread questioning legitimacy of a firm can dominate AI comparisons for months. This firm’s citation architecture was largely uncontrolled and exposed to competitive and reputational risk at the exact moment buyers were deciding whether to call. Additionally, the client was seeing competitors rank above them on Google page 1 and wanted to secure a stronger position in traditional search results.
Organic visibility still mattered, especially for high-intent searches where users were actively looking for tax relief options. But the discovery journey was also shifting. As Google AI Overviews, Gemini, and ChatGPT became common tools for researching and comparing tax relief providers, more prospects began relying on AI-generated summaries that surfaced recommendations in a single answer.
In this landscape, visibility isn’t driven just by rankings and paid ads, it also depends on the sources AI tools pull from and reference, including third-party websites and public discussions that influence how trust and credibility are framed. That meant the firm needed to compete on two fronts: page-1 performance in Google and presence inside AI answers where decisions were increasingly being made.
[ THE CHALLENGE ]
What the Brand Needed
Make visibility measurable across Google and AI
In a highly competitive tax relief category, the client needed a clearer way to understand and improve how it showed up across both traditional search and AI-led discovery. To do that, they needed a repeatable measurement system that could track:
Citations
Which websites and pages AI systems referenced when describing the firm
AI Share of Voice
How frequently the firm was mentioned compared with competitors
Brand Mentions
How often the firm was named in AI-generated answers
The goal wasn’t just stronger organic visibility on Google page 1. It was also to build consistent LLM visibility, where more high-intent comparisons and decisions were increasingly happening in the moment.
[ OUR APPROACH ]
What We Did
Audited How AI Answers Were Being Formed Around the Brand
We began by analysing how major AI tools described the tax relief firm and what sources they relied on to generate those responses. Our visibility reporting captured the citations and reference patterns across AI Overviews, ChatGPT, Gemini, AI Mode, Perplexity, and Copilot, showing which sites and discussions were most frequently shaping how the brand appeared in AI answers.
Set up monthly measurement and iteration
We tracked month-over-month changes to see whether new activity translated into more brand mentions, stronger citations, and improved share of voice. This made it easier to identify which topics and content angles were gaining visibility. We then refined execution based on performance, scaling what worked and pausing what didn’t deliver measurable lift.
Strengthened the Sources AI Systems Were Already Pulling From
In the tax relief space, competitive comparison queries are high-intent and high volume, and AI tools often echo what’s most visible and consistently referenced. Since third-party sources and online community discussions were already influencing AI answers, we focused on improving the accuracy and strength of the brand context in those places.
Rather than relying only on generic blog production, CiteWorks Studio executed an AI citation strategy designed to increase the quality and consistency of references tied to common tax relief searches.
We improved the quality and accuracy of brand context across the sources AI systems were already referencing. This helped the brand appear more credibly and consistently in AI-generated comparisons over time.
“We kept investing in paid search, but prospects arrived already skeptical, shaped by what they’d read in forums and AI summaries before they clicked our ad. We needed to fix what was being said before they reached us, not after.”
— Head of SEO & Marketing, Tax Relief Firm
[ THE OUTCOME ]
Results
Measurable gains in both rankings and AI visibility
These results reflect improved visibility across traditional search and AI-generated answers for high-intent tax relief queries.
#6 average ranking position secured for all high-intent keywords related to tax relief
112.5% increase in brand mentions in AI Overviews. This growth was witnessed in just a month across 19 high-intent tax-related queries
500+ high-impact community sources and cited pages with strengthened brand context influencing AI answers
9,984 keywords appearing in the top 10 results, covering 1.4M in combined monthly search volume and ~$13,562 in paid-search benchmark value (keyword volume × cost per click)
These gains weren’t just a short-term spike. They created a stronger foundation for sustained discovery across both Google and AI answers.
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.