Tax Relief AI Search Case Study

In a 5-month long campaign with 543 engagements, this campaign generated an estimated $362,569.07 in total estimated monthly branded value. That included $53,080 in organic keyword value and $309,488.28 in LLM-cited pages value.

Methodology note: Directional estimate based on tracked keyword visibility and modeled paid-equivalent value. Not exact attribution.

As AI-powered search began reshaping how people compare tax relief providers, this firm faced a dual mandate: improve competitiveness on Google page 1 while also strengthening visibility inside AI-generated recommendations.

CiteWorks Studio deployed a measurable AI visibility program, grounded in Citation Architecture and AI Share of Voice measurement, to strengthen how the brand appeared across both traditional search and AI answers where high-intent decisions now happen.

Key Outcomes

Delivered in 5 months with only 543 engagements:

• Drove a 112.5% month-over-month lift in AI Overview brand mentions
• Ranked for 9,984 keywords in Google’s top 10
• Optimized 500+ online community threads strengthened to improve brand context in AI citations

What Changed in the Market

When trust risks shape Google rankings and AI recommendations

In tax relief, a single prominent forum thread questioning legitimacy of a firm can dominate AI comparisons for months. This firm's citation architecture was largely uncontrolled and exposed to competitive and reputational risk at the exact moment buyers were deciding whether to call.

Additionally, the client was seeing competitors rank above them on Google page 1 and wanted to secure a stronger position in traditional search results. Organic visibility still mattered, especially for high-intent searches where users were actively looking for tax relief options.

But the discovery journey was also shifting. As Google AI Overviews, Gemini, and ChatGPT became common tools for researching and comparing tax relief providers, more prospects began relying on AI-generated summaries that surfaced recommendations in a single answer.

In this landscape, visibility isn’t driven just by rankings and paid ads, it also depends on the sources AI tools pull from and reference, including third-party websites and public discussions that influence how trust and credibility are framed.

That meant the firm needed to compete on two fronts: page-1 performance in Google and presence inside AI answers where decisions were increasingly being made.

What the Brand Needed

Make visibility measurable across Google and AI

In a highly competitive tax relief category, the client needed a clearer way to understand and improve how it showed up across both traditional search and AI-led discovery.

To do that, they needed a repeatable measurement system that could track:

• How often the firm was named in AI-generated answers
• Which websites and pages AI systems referenced when describing the firm
• How frequently the firm was mentioned compared with competitors

The goal wasn’t just stronger organic visibility on Google page 1. It was also to build consistent LLM visibility, where more high-intent comparisons and decisions were increasingly happening in the moment.

What We Did

1. Audited how AI answers were being formed around the brand

We began by analysing how major AI tools described the tax relief firm and what sources they relied on to generate those responses. Our visibility reporting captured the citations and reference patterns across AI Overviews, ChatGPT, Gemini, AI Mode, Perplexity, and Copilot, showing which sites and discussions were most frequently shaping how the brand appeared in AI answers.

2. Set up monthly measurement and iteration

We tracked month-over-month changes to see whether new activity translated into more brand mentions, stronger citations, and improved share of voice. This made it easier to identify which topics and content angles were gaining visibility. We then refined execution based on performance, scaling what worked and pausing what didn’t deliver measurable lift.

3. Strengthened the sources AI systems were already pulling from

In the tax relief space, competitive comparison queries are high-intent and high-volume, and AI tools often echo what’s most visible and consistently referenced. Since third-party sources and online community discussions were already influencing AI answers, we focused on improving the accuracy and strength of the brand context in those places.

Rather than relying only on generic blog production, CiteWorks Studio executed an AI citation strategy designed to increase the quality and consistency of references tied to common tax relief searches.

We improved the quality and accuracy of brand context across the sources AI systems were already referencing. This helped the brand appear more credibly and consistently in AI-generated comparisons over time.

“We kept investing in paid search, but prospects arrived already skeptical, shaped by what they'd read in forums and AI summaries before they clicked our ad. We needed to fix what was being said before they reached us, not after.”

— Head of SEO & Marketing, Tax Relief Firm

The Outcome

Measurable gains in both rankings and AI visibility

These results reflect improved visibility across traditional search and AI-generated answers for high-intent tax relief queries.

• Average ranking position of #6 secured for all high-intent keywords related to tax relief
• 112.5% increase in brand mentions in AI Overviews. This growth was witnessed in just a month across 19 high-intent tax-related queries. 
• 9,984 keywords appearing in the top 10 results, covering 1.4M in combined monthly search volume and ~$13,562 in paid-search benchmark value (keyword volume × cost per click). 
• Brand context strengthened across 500+ high-impact community sources and cited pages influencing AI answers

These gains weren’t just a short-term spike. They created a stronger foundation for sustained discovery across both Google and AI answers.