Most fintech companies Austin Heaton talks to are still treating AI search like a future problem. They are watching their Google traffic hold steady, running paid campaigns that work well enough, and telling themselves they will figure out ChatGPT and Perplexity later.
That window is closing faster than most people realize.
In 60 days working with Lumanu, a B2B influencer payments platform, Austin Heaton generated 656 clicks and 101 conversions directly from AI search engines, primarily ChatGPT and Google Gemini, with zero paid ad spend behind it. This article breaks down exactly how he did it and why the same approach works for fintech companies operating in 2026.
Key Takeaways
- AI search now drives measurable, attributable conversions for B2B fintech companies, not just impressions.
- Optimizing for AI citation requires different content architecture than traditional Google SEO.
- The highest-leverage move is identifying the exact conversational queries AI models use to recommend products in your category, then owning those queries with structured, authoritative content.
Why AI Search Is a Fintech-Specific Opportunity Right Now
Fintech buyers research differently than consumers. A CFO evaluating payroll software or a creator economy startup sourcing a payments API is not typing three-word Google queries. They are asking AI assistants full questions: “What is the best way to pay international contractors without currency fees?” or “Which B2B payment platforms support creator payouts at scale?”
When a user asks ChatGPT or Perplexity a question like that, the model does not show ten blue links. It synthesizes an answer and cites two or three sources it considers authoritative. If your company is not being cited, a competitor is.
“According to BrightEdge research, AI-powered search interfaces now influence over 60% of B2B purchase research journeys, yet fewer than 12% of companies have an active strategy to appear in AI-generated answers.” – Austin Heaton, AEO Consultant
Lumanu was already a strong brand with solid domain authority in the mid-50s. But their content was not structured for AI citation. It was structured for traditional keyword ranking, which is a different problem requiring a different solution.
The Full-Stack AEO Process Austin Heaton Used
Step 1: LLM Readiness Audit
Before touching a single piece of content, Austin Heaton ran a dual audit covering both traditional SEO signals and LLM-specific readiness factors. This included:
- Reviewing their existing llms.txt configuration and structured data markup
- Mapping which queries in their category AI models were already answering, and how
- Identifying which competitors were being cited and why
- Auditing Core Web Vitals, internal linking architecture, and schema completeness
The audit revealed that Lumanu had minimal schema coverage on their core product pages and no content directly targeting the conversational query patterns AI models use when recommending payment solutions. They also lacked third-party citations that AI models could reference as corroborating sources.
The technical fixes were implemented quickly by their team, including advanced schema markup on service and comparison pages, improved Core Web Vitals scores, and a restructured internal linking network that better communicated topical authority to AI crawlers.
Step 2: Generative Query Mapping
This is the step most consultants skip entirely, and it is the most important one.
Traditional keyword research identifies what people type into Google. Generative query mapping identifies what people ask AI assistants, which are longer, more conversational, and often more purchase-intent than traditional queries.
Using a combination of query simulation and competitive citation analysis, Austin Heaton identified over 1,000 conversational AI queries most likely to produce referrals to Lumanu. These were not generic terms like “influencer payments software.” They were specific questions such as:
- “Best way to pay creators internationally without taking a fee percentage”
- “How do brands pay creators at scale without going through each creator manually”
- “B2B platform that handles payments and compliance for influencer programs”
AI models return product recommendations for these queries rather than answering them directly. That distinction is critical. A question like “what is influencer marketing” gets answered inline. A question like “what platform should I use to manage creator payments” triggers a citation.
“The most valuable real estate in AI search is not a top-ten ranking. It is being the company an AI model recommends when a buyer asks which product to use.” – Austin Heaton
Step 3: Bottom-Funnel-First Content Production
With the query map in hand, Austin Heaton produced content following a strict hierarchy: solution pages and comparison content first, case studies second, and supporting blog content last. This is the opposite of how most companies approach content marketing.
Top-of-funnel educational content rarely generates AI citations in a commercial context. When a user asks an AI assistant a question about a product category, the model cites pages that demonstrate clear product relevance, competitive differentiation, and third-party validation. Educational blog posts rarely qualify.
Each piece of content Austin Heaton produced for Lumanu was structured specifically for LLM extraction. This means:
- Clear H2 and H3 hierarchy that AI models can parse and attribute
- Bold statistics and quantified outcomes after every two to three paragraphs
- Direct Q&A sections that match the exact phrasing of conversational AI queries
- Expert attribution with named quotes, since AI models prefer citing human experts over anonymous brand content
Step 4: Authority Building Through DA40+ Backlinks
AI citation authority is not purely a content problem. It is also a reputation problem.
LLMs in 2026 weight source credibility heavily when deciding which pages to cite. Domain authority, third-party mentions, and the quality of sites linking to you all influence whether an AI model treats your content as citable or not.
Lumanu already had domain authority in the mid-50s, which was solid. But to push into consistent AI citation, Austin Heaton secured multiple backlinks from fintech-adjacent publishers with domain authorities between 40 and 80. These were not generic directory links. They were contextual placements in editorial content directly relevant to the B2B payments and creator economy space.
This pushed their effective domain authority above 60 and created the third-party citation trail that AI models use to cross-reference credibility.
The Results After 60 Days
AI Search Performance
ChatGPT referral clicks: 566 (+134.85% from baseline)
Google Gemini referral clicks: 46 (+155.56% from baseline)
Total AI-sourced clicks: 656
Total conversions from AI search: 101 (99 from ChatGPT, with Gemini showing the highest engagement rate per click)
The conversion rate from Gemini traffic was notably higher than ChatGPT, which aligns with what Austin Heaton has seen across other fintech clients. Gemini users tend to be deeper into the consideration stage when they click through, making them higher-quality leads even at lower volume.
Google Search Performance
Total organic clicks: 28,820 (+17.36%)
Total impressions: 1.06 million (+22.55%)
The Google results were a direct byproduct of the same work done for AI optimization. Structured content, technical improvements, and authoritative backlinks improve rankings across both channels simultaneously. This is why full-stack AEO produces compounding returns rather than channel-siloed gains.
Why This Works for Fintech Specifically
Fintech buyers are early adopters of AI research tools. The average fintech decision-maker is using ChatGPT or Perplexity to shortlist vendors before they ever visit a company website directly. This creates both an urgency and an opportunity.
The urgency: if you are not optimized for AI citation, you are invisible to a growing segment of your best buyers.
The opportunity: most fintech companies have not yet built a coherent AEO strategy. The category is not saturated. A focused 60-day engagement can establish category authority in AI search before competitors close the gap.
“In fintech, AI search citation is a first-mover advantage. The companies that establish authority now will be the default recommendations AI models return for years.” – Austin Heaton, AEO Consultant
What Most SEO Consultants Get Wrong About AEO
The most common mistake Austin Heaton sees is treating Answer Engine Optimization as a content format problem rather than an authority architecture problem. Companies produce FAQ sections and Q&A content, see no improvement in AI citations, and conclude that AEO does not work.
The format matters, but it is secondary to authority signals. An AI model will not cite a well-structured page from a low-authority domain over a mediocre page from a high-authority one. You need both: the right content architecture and the domain reputation to back it up.
The second most common mistake is optimizing for informational queries rather than commercial ones. AI models handle informational queries directly. They generate citations for commercial and transactional queries where the user needs a product recommendation. That is the traffic worth optimizing for.
How to Start
If you are a fintech company looking to replicate these results, the starting point is a dual-channel audit: one that evaluates your current standing in both traditional Google search and AI citation contexts simultaneously.
The audit will surface your LLM readiness gaps, identify which queries your competitors are being cited for, and map out the content and authority investments needed to enter the citation mix. From there, the content and link building work follows a predictable playbook.
The full engagement Austin Heaton ran for Lumanu, including the audit, content production, backlink acquisition, and optimization, was completed within 60 days. The 656 AI clicks and 101 conversions were measured within that same window.
Austin Heaton is a B2B SEO and Answer Engine Optimization consultant with over 12 years of experience, specializing in AI search visibility for fintech, SaaS, and crypto companies. He works with clients at austinheaton.com.
