How a Job Posting Platform Secured a Place in AI’s Shortlist for Employers

How a Job Posting Platform Secured a Place in AI’s Shortlist for Employers

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

100+ cited pages

influenced,

strengthening the brand’s

presence in the sources

AI systems refer to

2,791 keywords ranked

in Google’s top 10

71% increase in brand

What Changed in the Market

Section One
Card 1
Add your text here.
Card 2
Add your text here.
Section Two
Card 1
Add your text here.
Card 2
Add your text here.
Section Three
Card 1
Add your text here.
Card 2
Add your text here.

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.

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

A reliable way to measure and strengthen AI visibility.


The team needed a repeatable measurement framework to track:

A reliable way to measure and strengthen AI visibility.

The team needed a repeatable measurement framework to 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

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

The brand’s share of

appearances

relative to tracked

competitors

across AI answers

AI Share of Voice

How often the brand

is named in AI-generated

answers

Brand Mentions

What the Brand Needed

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.

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.

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

  1. 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.


  2. 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.


  3. 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

  1. 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.


  2. 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.


  3. 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

The Outcome

Measurable, Compounding Results.

The campaign delivered results across both traditional search and AI-generated discovery, reflecting how closely the two are now intertwined.

Measurable, Compounding Results.

The campaign delivered results across both traditional search and AI-generated discovery, reflecting how closely the two are now intertwined.

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

Close to 400

citation-bearing

engagements

delivered in 4 months

Average ranking

position of

#6 forall high-intent

keywords

in the Google SERPs

71% increase in brand

mentions in AI Overviews

in a month, measured

across 30,000+

tracked prompts.

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)

Improved citation context across

100+ high-authority pages and

discussion sources influencing

AI answers

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.

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 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.

Reach us at citeworksstudio.com

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, 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 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.

More Work

See All

[

0

1

]

How a Crypto Wallet Became the Brand AI Recommends When Trust and Security Are the Deciding Factors

Startup

E-commerce