How a Tax Relief Firm Became AI's Go-To Recommendation for TrustFirst Decisions
How a Tax Relief Firm Became AI's Go-To Recommendation for TrustFirst Decisions
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.
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.
100+ cited pages
influenced,
improving the brand’s
presence in the sources
AI systems refer to
2,791 keywords ranked
in Google’s top 10
Key Outcomes
Key Outcomes
71% increase in brand
mentions in AI Overviews
Optimized 500+ online
community threads
strengthened to improve
brand context in
AI citations
Ranked for 9,984
keywords in
Google’s top 10
Drove a 112.5%
month-over-month
lift in AI Overview
brand mentions
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.
Brand discovery was moving to AI, without them
When it comes to hiring, trust issues surface
fast and public conversations
about fake listings,
scams, and poor candidate experiences
can spread widely.
That made the platform’s citation
footprint (the sources AI systems relied on to
summarize and compare brands) a real risk
point at the decision moment,
when employers were choosing
where to post roles.
Employers increasingly turned to tools
like Google AI Overviews, Gemini, and ChatGPT
when choosing a job posting platform.
These systems don’t just rank pages;
they synthesize
recommendations from across the web,
drawing heavily on high-authority public
forums and online communities.
The platform recognized a structural risk:
even a handful of prominent negative threads could
disproportionately shape what AI systems repeated.
Meanwhile, positive sentiment buried in
low-visibility corners of the internet
had no influence at all.
The core problem wasn’t brand
reputation in the traditional sense.
It was citation architecture i.e. which
sources were being pulled into
AI answers, and what those sources said.
What the Brand Needed
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:
Win visibility in AI answers
The brand needed a clearer way to diagnose and improve how it appeared across both traditional search results and AI-driven product discovery.
To do that, they needed a repeatable measurement framework that could track:
A reliable way to measure and strengthen AI visibility.
The team needed a repeatable measurement framework to track:
How often the brand
is named in AI-generated
answers
Brand Mentions
The URLs and sources
AI platforms reference
while generating
responses,
including online
community forums
where real users
discuss pain points
and comparisons
Citations
The brand’s share of
appearances
relative to tracked
competitors
across AI answers
AI Share of Voice
Which websites
and pages AI
systems referenced
when describing
the firm
Citations
How frequently
the firm was
mentioned compared
with competitors
AI Share of Voice
The brand’s share of
appearances
relative to tracked
competitors
across AI answers
AI Share of Voice
How often the
firm was named in
AIgenerated answers
Brand Mentions
What the Brand Needed
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.
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.
This could help the brand remain
a top choice for employers and hiring managers.
The final requirement was identifying an
agency partner that could deliver this
as a measurable,
repeatable program.
What We Did
Mapped AI Visibility and Citation Sources
We assessed how AI platforms referenced the
brand and which sources
most consistently influenced those answers.
Our reporting tracked citation and mention patterns
across AI Overviews, ChatGPT, Gemini, AI Mode,
Perplexity, and Copilot, identifying
the domains and discussion environments
shaping AI-generated recommendations
in the category.
Tracked Momentum Month-Over-Month
We tracked month-over-month movement
to understand whether new activity increased
brand mentions in AI answers and
by how much. This helped identify which
topics, discussion formats, and source types
were being referenced more frequently across AI
Overviews, ChatGPT, and Gemini.
We also monitored whether citations were
consolidating around more accurate, higher
trust sources over time. Based on performance,
we scaled what delivered measurable lift and
paused approaches that didn’t.
Focused on the Channels LLMs Were
Already Pulling From
In the employment sector, conversation volume is
enormous. Popular social forums were
among the brand’s top cited domains,
so we focused our efforts on building positive
perception on these platforms to influence the
way LLMs talked about the brand.
Instead of producing run-of-the-mill blog posts,
CiteWorks Studio implemented an
AI citation strategy focused on improving
the brand's representation in high-intent,
public discussions tied to top employment queries.
By strengthening high-authority
community conversations and references,
we shifted the sources LLMs drew from
when generating answers about the client.
Over time, these discussions became
the most trusted context LLMs surfaced, helping
shape brand perception more positively.
“The shift wasn’t just in our rankings but in what AI systems
were recommending when employers searched
without knowing our name. That was a different kind of
visibility, and it’s the kind that matters now”
— VP of Marketing, Job Posting Platform
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 highvolume, 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.
“The shift wasn’t just in our rankings but in what AI systems were recommending when employers searched without knowing our name. That was a different kind of visibility, and it’s the kind that matters now”
— VP of Marketing, Job Posting Platform
The Outcome
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.
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.
Importantly, the gains were not a one-time spike.
By building a durable base of credible citations,
the firm now has a self-reinforcing foundation,
one that continues to influence
AI answers as new queries emerge.
Improved citation context across
100+ high-authority pages and
discussion sources influencing
AI answers
2,791 keywords appearing in the
top 10, covering
406,000 in combined monthly
search volume and ~$5,262
in paid search benchmark value
(keyword volume × cost per click)
71% increase in brand
mentions in AI Overviews
in a month, measured
across 30,000+
tracked prompts.
Average ranking
position of
#6 forall high-intent
keywords
in the Google SERPs
Close to 400
citation-bearing
engagements
delivered in 4 months
Average ranking
position of #6
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
Brand context
strengthened across
500+ high-impact
community sources
and cited pages
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.
Measurable, Repeatable
Programme
Build a durable
foundation of credible
citations that compounds
over time and continues
to influence AI answers
as newqueries emerge
Measurable, Repeatable
Programme
Build a durable
foundation of credible
citations that compounds
over time and continues
to influence AI answers
as newqueries emerge
Citation Architecture
Review
Identify which high-
authority community
sources are and aren't
working in your favour
across AI platforms.
Citation Architecture
Review
Identify which high-
authority community
sources are and aren't
working in your favour
across AI platforms.
Want to understand your AI citation
footprint?
Want to understand your AI citation
footprint?
AI Visibility Audit
Understand exactly how
LLMs are referencing
your brand today and
which sources are
shaping those
answers.
AI Visibility Audit
Understand exactly how
LLMs are referencing
your brand today and
which sources are
shaping those
answers.
We start every engagement with a full audit.
We start every engagement with a full audit.
AI Visibility Audit
Understand exactly how
LLMs are referencing
your brand today and
which sources are
shaping those answers.
AI Visibility Audit
Understand exactly how
LLMs are referencing
your brand today and
which sources are
shaping those answers.
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.
Citation Architecture
Review
Identify which high-
authority community
sources are and aren't
working in your favour
across AI platforms.
Understanding AI Search Visibility
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.
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, highauthority 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.
—————————————————
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 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, highauthority sources are the ones most likely to shape the way AI tools summarize and recommend a brand.
——————————————————
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 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.
——————————————————
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.
About the author
About the author
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.
Founder and Head of Agency
Mark Huntley
Mark brings over a decade of high-stakes marketing experience, merging deep expertise in performance media with a hands-on understanding of global e-commerce. His vision for CiteWorks Studio was simple: create an agency where creativity meets systems thinking, and where AI isn’t a gimmick — it’s the baseline.
Prior to founding SBM, Mark led digital campaigns from break out startups to fortune 500’s across the world, architecting strategies that reached millions and converted attention into tangible results. Today, he drives the strategic direction of our work, ensuring every campaign is dialed in, data-backed, and built to perform.
Mark brings over a decade of high-stakes marketing experience, merging deep expertise in performance media with a hands-on understanding of global e-commerce. His vision for CiteWorks Studio was simple: create an agency where creativity meets systems thinking, and where AI isn’t a gimmick — it’s the baseline.
Prior to founding SBM, Mark led digital campaigns from break out startups to fortune 500’s across the world, architecting strategies that reached millions and converted attention into tangible results. Today, he drives the strategic direction of our work, ensuring every campaign is dialed in, data-backed, and built to perform.