[ CASE STUDY ]
Job Board AI Search Case Study
How a Job Posting Platform Secured a Place in AI’s Shortlist for Employers
In a 5-month long campaign with close to 500 engagements, this campaign generated an estimated $8,797,300.28 in total estimated monthly branded value. That included $4,629,347.18 in organic keyword value and $4,167,953.10 in LLM-cited pages value.
Methodology Note
Directional estimate based on tracked keyword visibility, combined monthly search volume, and paid search benchmark value. Not exact attribution.
When AI-powered search began reshaping how employers discover job posting platforms, this firm didn’t just adapt, they built a measurable, repeatable system to increase its visibility and competitiveness in AI-generated recommendations.
[ KEY OUTCOMES ]
Results at a Glance
Top metrics from a 5-month long campaign with 480 engagements:
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 mentions in AI Overviews
[ MARKET CONTEXT ]
What Changed in the Market
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.
[ THE CHALLENGE ]
What the Brand Needed
A reliable way to measure and strengthen AI visibility
The team needed a repeatable measurement framework to track:
AI Share of Voice
The brand’s share of appearances relative to tracked competitors across AI answers
Citations
The URLs and sources AI platforms reference while generating responses, including online community forums where real users discuss pain points and comparisons
Brand Mentions
How often the brand is named in AI-generated answers
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.
[ OUR APPROACH ]
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
[ THE OUTCOME ]
Results
Measurable, Compounding Results.
The campaign delivered results across both traditional search and AI-generated discovery, reflecting how closely the two are now intertwined.
~400 citation-bearing engagements delivered in 4 months
#6 average ranking position for all 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 results for priority queries
100+ high-authority pages and discussion sources with improved citation context 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.
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.