The tax-savings answer service-business owners find first
A B2C platform in a category buyers research one question at a time, in Google and in ChatGPT. We made it the source for both.
This platform helps individuals who run service-based businesses, from car washing to HVAC to landscaping, understand and capture the tax savings they are leaving on the table. Their buyers are sole proprietors researching a hundred variations of one question: how do I pay less tax on this income. That research now happens as much inside AI engines as in Google. In six months we built the organic and cited presence to be the answer in both, lifting organic clicks 86%, organic leads 57%, and turning AI engines into a channel that drives roughly 40% of new leads.
A million tax questions, none of them yours to answer.
Self-employed service-business owners do not search for tax software. They search for answers: which tools they can write off, whether their truck qualifies, how much a sole proprietor can really save. Every one of those questions was being answered by generic finance blogs and forums, never by the platform built to solve exactly that problem.
Increasingly, those questions were not going to Google at all. Buyers were asking ChatGPT and Perplexity directly, and the answers, when they mentioned a product, named competitors. The platform had strong software and a clear value proposition, but it was invisible at the exact moment a buyer decided they needed help.
Own the question, in every place it gets asked
Rather than chase a handful of head terms, we treated the whole space of tax questions as the opportunity, then built content that ranks in Google and gets lifted into AI answers. If a service-business owner was asking it, the platform would be the source that answered it.
The work, month by month
The same plan, laid out on the calendar it actually ran on.
Foundation and keyword map
Technical cleanup plus a map of the tax questions each trade actually asks, sized by volume and intent.
Trade hubs and guides
Shipped the first trade hubs and deduction guides, structured as answers from day one.
GEO layer
Added llms.txt and answer blocks, and began daily citation monitoring across the four major AI engines.
Compound and attribute
Doubled down on the questions engines quoted and wired AI-referred leads into attribution.
Clicks and leads on the same curve
Organic clicks over the six-month engagement, with the trade hubs and GEO layer marked. Leads tracked the same line.
AI engines became a real channel
New-lead sources at month six. The platform went from zero AI presence to AI engines driving roughly 40% of new leads.
AI-referred sign-ups are attributed alongside organic in one dashboard, so leadership can see the LLM channel growing, not just guess at it.
The cited answer, and a new lead channel to prove it
Six months in, the platform was the source service-business owners found first, whether they searched Google or asked an AI engine. Organic more than held its own, and AI engines turned into a measurable, growing share of new leads.
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