
From Rankings to Recommendations: How a Kitchen Appliance Brand Won Visibility in AI Answers
From Rankings to Recommendations: How a Kitchen Appliance Brand Won Visibility in AI Answers
As buyer research moved beyond product pages and into online communities, this kitchen appliance brand saw a clear gap: real purchase decisions were increasingly shaped by what people shared, compared, and recommended in public conversations.
The brand partnered with CiteWorks Studio to build presence across those high-intent decision environments, strengthening both Google visibility and the citation signals that influence AI-generated recommendations.
As buyer research moved beyond product pages and into online communities, this kitchen appliance brand saw a clear gap: real purchase decisions were increasingly shaped by what people shared, compared, and recommended in public conversations.
The brand partnered with CiteWorks Studio to build presence across those high-intent decision environments, strengthening both Google visibility and the citation signals that influence AI-generated recommendations.

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 100 online
community threads
to improve brand
context AI citations.
Ranked for 2,398
keywords in Google’s
top 10 results.
Drove a 15%
month-over month
growth in overall
LLM mentions.
What Changed in the Market
Where Buyers Decide Now: Online Communities + AI Recommendations
The brand was seeing competing kitchen appliance brand outrank them on Google page 1 and wanted to secure stronger visibility for high-intent searches, especially from shoppers comparing features, pricing, and reviews.
Organic search still mattered because it captured buyers at peak purchase intent. At the same time, Google AI Overviews, Gemini, and ChatGPT became common tools for researching and comparing products. More shoppers began relying on AI-generated summaries that surfaced “best product" recommendations in a single answer.
In practice, ranking position alone wasn’t the full story. AI summaries reflect what the web already says, especially third-party pages and real-user discussions and those inputs materially shape the narrative buyers see during comparison.
While the shift was clear, the brand needed a repeatable path to earn visibility in these new decision channels and sustain it over time.
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
Earn a consistent spot in AI "best-of" comparisons
In a crowded ice cream maker market, 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:
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:
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 product
Citations
How frequently the
brand appeared
compared with
competing ice
cream makers
AI Share of Voice
The brand’s share of
appearances
relative to tracked
competitors
across AI answers
AI Share of Voice
How often the brand was
named in AI
generated answers
Brand Mentions
What the Brand Needed
The aim wasn't only to climb Google page 1. It was also to build reliable LLM visibility, so the brand showed up consistently when shoppers were making high-intent comparisons at 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.
The aim wasn't only to climb Google page 1. It was also to build reliable LLM visibility, so the brand showed up consistently when shoppers were making high-intent comparisons at the moment.
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 Product Recommendations Were Being Generated
We assessed how major AI tools described the brand and what sources they relied on to form those summaries. Our reporting mapped citation and reference patterns across:
Measured Impact Month Over Month and Iterated Fast
We tracked month-over-month movement to see whether new activity translated into more brand mentions, stronger citations, and improved share of voice in AI responses. This made it easier to spot which consumer questions and comparison angles (features, pricing, ease of use, results) were gaining traction. We then adjusted based on performance, scaling what worked and pausing what didn't deliver measurable lift.
Strengthened the Sources AI Systems Were Already Referencing
In consumer appliances, buying decisions are heavily influenced by “real-world proof” i.e. what people recommend, compare, and validate publicly. Since third-party sources and online community conversations were already influencing AI-generated product summaries, we focused on strengthening accurate, positive brand context in those environments.
Rather than relying only on generic blog production, CiteWorks Studio executed an AI citation strategy designed to increase the quality and consistency of brand references tied to common “best ice cream maker” and comparison searches.
The Outcome
Measurable Lift Across Google and AI Answers
Across traditional search and AI-generated product summaries, the brand saw measurable improvements in visibility for high-intent “best product” queries.
Measurable Lift Across Google and AI Answers
Across traditional search and AI-generated product summaries, the brand saw measurable improvements in visibility for high-intent “best product” 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
important keywords
15% increase in overall
LLM mentions across
high-intent prompts
Brand context
strengthened across
100 high-impact
community sources
and cited pages
influencing AI answers
2,398 keywords appearing in the
top 10 results, covering 1.2M
in combined monthly search volume and
~$585 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.

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.