Job Board AI Search Case Study

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 and modeled paid-equivalent 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

Top metrics from a 5-month long campaign with 480 engagements: 

• 71% increase in brand mentions in AI Overviews
• 2,791 keywords ranked in Google’s top 10
• 100+ cited pages influenced, strengthening the brand’s presence in the sources AI systems refer to

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.

What the Brand Needed

A reliable way to measure and strengthen AI visibility

The team needed a repeatable measurement framework to track:

• Brand mentions (how often the brand is named in AI-generated answers)
• Citations (the URLs and sources AI platforms reference while generating responses, including online community forums where real users discuss pain points and comparisons)
• AI share of voice (the brand’s share of appearances relative to tracked competitors across AI 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.

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

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.

• Close to 400 citation-bearing engagements delivered in 4 months
• Average ranking position of #6 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
• 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.