CiteWorks Studio

Debt Relief AI Search Case Study

See how a debt relief provider gained 287 top-10 keywords, 8 cited pages, and $525K in monthly branded value in just 3 days.

Mark HuntleyBy Mark HuntleyFounder and CEO
3 minutes read

Results at a Glance

In just 3 days, using only 25 targeted engagements, this campaign generated an estimated $525,232.32 in monthly branding value. That included $339,734.09 in organic keyword value and $185,498.23 in LLM cited-pages value.

$525,232.32 in monthly branding value

Directional estimate based on tracked keyword visibility, combined monthly search volume, and paid search benchmark value.

$339,734.09 in organic keyword value

This was the estimated monthly branding value attributed to organic keyword visibility.

$185,498.23 in LLM cited-pages value

This was the estimated monthly branding value attributed to LLM cited-pages value.

287 high-value keywords in Google’s top 10

The campaign delivered 287 high-value keywords ranking in Google’s top 10.

650 total tracked keywords

The brand achieved visibility across 650 total tracked keywords.

12 average ranking position

The campaign produced an average ranking position of 12 across the tracked set.

8 cited pages influenced within 5 days

The campaign influenced 8 cited pages within 5 days.

What Changed in the Market

Debt relief discovery now happens across multiple channels at once. Borrowers still begin with search terms such as “best debt consolidation,” “debt settlement strategies,” and other financial hardship queries, but the decision process rarely ends on the search results page.

Before choosing a provider, many users validate options through public discussions, authority-led education, and third-party trust signals. At the same time, AI systems increasingly assemble recommendations from those same sources. That means a debt relief brand can rank well in search and still lose visibility at the recommendation stage if it is not well represented in the discussions, reviews, and comparison contexts shaping both consumer perception and AI-generated answers.

In financial services, trust is not a supporting factor. It is central to conversion. Borrowers want balanced information, social proof, and signs of reliability before moving forward.

What the Brand Needed

The provider did not simply need more rankings. It needed stronger influence in the environments that shape decisions.

Research Presence

Appearing more often when consumers explored debt relief, debt consolidation, settlement options, and financial recovery topics.

Citation Strength

Improving representation across the public pages and discussions AI systems use when generating summaries and recommendations.

Comparison Visibility

Increasing presence in the environments where borrowers actively compare providers and decide who appears credible.

What We Did

The campaign concentrated a limited number of high-intent engagements on the public sources most likely to shape both consumer research and AI-generated recommendations.

Prioritized the Moments That Influence Provider Selection

We mapped the search and public-discussion environments most likely to shape how borrowers evaluate debt relief options, especially around consolidation, settlement, and hardship-related research. This allowed the campaign to focus effort where visibility could influence decision-making fastest.

Strengthened the Brand’s Presence in Trust-Heavy Third-Party Environments

We improved how the provider appeared across public conversations, educational content, and review-oriented sources so the brand showed up more consistently in the places consumers rely on for validation. That also increased the likelihood of stronger representation in AI-generated summaries built from those same sources.

Measured Impact Through Auditable Discovery Signals

We tracked keyword movement, citation influence, and visibility across AI-relevant source environments to confirm that the campaign was generating measurable discovery gains, not just surface-level exposure.

The Outcome

The campaign gave the provider a broader and more commercially useful visibility footprint across both Google search and AI-influenced discovery. As the brand gained stronger presence in trusted discussions, authority-led content, and third-party review surfaces, it improved how it appeared during the comparison stage of the borrower journey.

  • 287 high-value keywords in Google’s top 10
  • 650 total tracked keywords where the brand appeared
  • 12 as the average ranking position
  • 8 cited pages influenced within 5 days

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Understanding AI search visibility.

AI search experiences create answers by pulling information from many places online and summarizing it into a single response.

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.

About The Author

Mark Huntley

Mark Huntley

Founder and CEO

Mark Huntley, J.D. is 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.

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