[ CASE STUDY ]
Insurance Technology AI Search Case Study
How an Insurance Technology Company Improved AI Discovery by Reinforcing the Sources AI Systems Cite
In just 3 days, using only 25 targeted engagements, this campaign generated an estimated $11,035.62 in monthly branded value. That included $3,622.88 in organic keyword value and $7,412.75 in LLM-cited pages value.
Methodology Note
Directional estimate based on tracked keyword visibility and modeled paid-equivalent value. Not exact attribution.
Insurance technology buyers don’t rely on search results alone. They validate vendors through trusted public discussions, third-party analysis, creator-led education, and increasingly through AI-generated answers built from those same sources. Discover how CiteWorks Studio strengthened the company’s presence across high-intent public discussions, authority channels, and third-party trust environments, improving page-one influence.
[ KEY OUTCOMES ]
Results at a Glance
Achieved in a matter of days with only 25 engagements:
#7
average ranking position across the keyword set
11
pages with strengthened brand context commonly referenced by AI systems
848
high-value, intent-aligned keywords secured on page 1
1,097
tracked keywords with expanded visibility
[ MARKET CONTEXT ]
What Changed in the Market
Insurance technology buying no longer happens through search alone. Buyers still begin with high-intent queries, but they increasingly validate vendors through public discussions, third-party analysis, creator-led explainers, and AI-generated answers that summarize those same sources.
That shift matters because AI systems now shape how providers are introduced and compared. A brand can rank well in search and still miss evaluation-stage visibility if it is not well represented in the discussions, reviews, and authority sources that influence both buyer perception and AI-generated recommendations.
[ THE CHALLENGE ]
What the Brand Needed
The company did not simply need more rankings. It needed stronger presence in the sources that shape buyer trust. That required improving three decision-stage signals:
Competitive Visibility
Showing up more consistently in the environments where buyers compare options and validate providers
Citation Strength
Increasing visibility in the public pages and discussions AI systems use when generating comparisons and recommendations
Research Presence
Appearing more often in high-intent insurance and estate-planning related discovery moments
The goal wasn’t only to rank, it was to be surfaced reliably at the decision moment, when buyers are forming a shortlist.
[ OUR APPROACH ]
What We Did
Focused on the discovery surfaces shaping evaluation
We identified the public platforms and discussion environments most likely to influence insurance technology research, then aligned placements to the high-intent queries and comparison moments already shaping buyer evaluation.
Strengthened brand presence in trusted third-party environments
We improved how the brand appeared across public discussions, creator-led education, and third-party trust sources so it showed up more consistently in the places where both people and AI systems form recommendations.
Measured impact through auditable visibility signals
We tracked keyword growth and AI-cited page influence to verify that stronger public-source coverage was translating into broader discoverability and more consistent recommendation-stage visibility.
“Our priority was being consistently cited in the sources that shape buyer trust and AI recommendations. CiteWorks Studio helped us operationalize that visibility and measure it end-to-end.”
— Marketing Team, Insurance Technology Company
[ THE OUTCOME ]
Results
The campaign moved the brand into more of the high-intent spaces where buyers research, compare, and validate insurance technology providers. It also improved visibility in the third-party sources that influence AI-generated recommendations.
848 high-value keywords on page 1
1,097 tracked keywords with broader visibility
#7 average ranking position
11 AI-referenced pages with stronger brand context
The result was a stronger foundation for ongoing discovery as more buyer journeys begin with a mix of search, external validation, and AI-generated recommendations.
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.