[ CASE STUDY ]
Home Services AI Search Case Study
How a Home Services Brand Increased Urgent-Intent Visibility Across Search and AI Recommendations
In just 3 days, using only 25 targeted engagements, this campaign generated an estimated $33,156.62 in monthly branding value. That included $3,089.18 in organic keyword value and $30,067.44 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.
For home services brands, speed matters because demand is immediate. When an AC unit fails, a pipe bursts, or a repair cannot wait, homeowners move fast. They search for answers, compare providers, scan reviews, and increasingly rely on AI-generated summaries to decide who looks credible enough to contact. In that environment, efficient visibility gains can influence buying decisions quickly. This campaign was built around that urgency. Rather than relying on broad awareness activity, CiteWorks Studio focused a limited number of high-intent engagements on the public sources most likely to shape homeowner trust and AI-generated recommendations. The result was a faster, more efficient increase in the brand’s visibility across the places where service decisions are actually made.
[ KEY OUTCOMES ]
Results at a Glance
Achieved in 3 days with only 25 engagements:
25
cited pages influenced in 5 days for Gemini and AI Overviews
23
high-authority citation opportunities activated during the pilot
169
high-value keywords ranking in Google’s top 10
257
total keywords with expanded visibility
[ MARKET CONTEXT ]
What Changed in the Market
Home services discovery now happens across two connected channels: traditional search and AI-generated guidance.
Homeowners still begin with Google for repair questions, contractor comparisons, and maintenance issues, but they increasingly validate options through public discussions, reviews, creator-led home advice, and AI-generated answers built from those same sources.
That shift matters because AI platforms often summarize the exact public sources homeowners already use to evaluate providers.
A home services company can rank well in traditional search and still lose ground at the recommendation stage if it is not well represented in the discussions, reviews, and trusted third-party environments shaping both homeowner perception and AI responses.
In this category, credibility is part of conversion. Homeowners want clear advice, trustworthy sentiment, and visible proof that a provider is reliable before they take action.
[ THE CHALLENGE ]
What the Brand Needed
The brand did not simply need more rankings. It needed a stronger presence in the sources that influence real service decisions. That required improving three decision-stage signals:
Competitive Visibility
Expanding presence in the environments where homeowners compare local options and decide who feels most trustworthy
Citation Strength
Improving representation across the public pages and discussions that shape brand context in AI-generated recommendations
Research Presence
Appearing more often in high-intent homeowner conversations around HVAC, plumbing, repairs, maintenance, and provider comparisons
The objective was not just to move up in search, but to become more visible where urgency, trust, and provider choice come together.
[ OUR APPROACH ]
What We Did
Focused on the moments where homeowners decide
We mapped the high-intent problem-solving environments already shaping repair and maintenance decisions, especially around HVAC issues, plumbing problems, troubleshooting, and contractor comparisons. That let us prioritize the places where visibility could influence action fastest.
Strengthened brand presence in trust-heavy third-party sources
We improved how the brand appeared across homeowner discussions, creator-led home education, and review-driven environments so it showed up more consistently in the same places people and AI systems use to form recommendations.
Verified impact through measurable visibility signals
We tracked keyword growth, citation influence, and AI-related visibility so stakeholders could see which activations were contributing to broader discoverability and recommendation-stage presence.
“We needed stronger visibility in the sources homeowners actually rely on when evaluating service providers, and CiteWorks helped us expand that presence in a measurable way.”
— Director of Marketing Team, Home Services Brand
[ THE OUTCOME ]
Results
The campaign increased visibility at the moments that matter most, when homeowners need help fast, compare providers, and decide who to trust. It also strengthened the brand’s presence in the third-party sources that influence AI-generated recommendations.
169 high-value keywords in Google’s top 10
257 total keywords where the brand appeared
25 cited pages influenced in 5 days
23 high-authority citation opportunities activated
The result was a stronger foundation for ongoing discovery as more home service decisions begin with a combination of search, public trust signals, 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.