GEO CASE STUDY
Earning Trust in the Answer Layer: 116% Citations Growth for Titan Funding
How a private lending firm went from rarely mentioned to consistently cited, building the AI visibility needed to compete in one of the most intent-rich categories in financial services.
Client
Titan Funding
Niche
Finance / Private Lending
Duration
6-Month Campaign
+60%
Cited pages growth
+116%
Citations growth
+60%
Cited pages growth
The Client
Titan Funding: Private Lending for Real Estate Investors
Titan Funding is a private lending firm serving real estate investors and developers with bridge loans, hard money lending, and alternative financing solutions. Operating in a category where borrowers frequently consult AI to compare lending options and evaluate lenders before making contact, AI visibility is a direct lead-generation concern, not a secondary metric. The question of which private lenders are worth contacting is increasingly answered by an LLM before a borrower ever hits a search result.
THE CHALLENGE
Strong Lending Product, Weak AI Signal
Absent From AI Lending Comparisons
Titan Funding was not appearing in AI-generated responses to private lending queries. When prospective borrowers asked LLMs to compare lenders or explain hard money loan options, the responses drew from well-cited incumbents, leaving Titan invisible at the moment of early-stage research.
Citation Count Too Low to Compete
With only 45 citations at baseline, Titan lacked the citation mass that AI systems use as a trust proxy. Established national lenders had accumulated editorial coverage over years, and without a structured programme to close that gap, Titan’s citation profile remained structurally underweight.
Narrow Cited Page Coverage
Private lending intent is fragmented across loan type, project type, geography, and loan-to-value threshold. With few pages earning citations, Titan was absent from most of those intents even when it had a product directly suited to the query.
High-Stakes Category, High-Trust Requirement
Finance is a category where AI systems apply a higher editorial bar before citing a source. Thin or inconsistently structured content is not cited regardless of how strong the underlying product is. Titan needed content that satisfied the E-E-A-T signals LLMs use to evaluate lender credibility.
Our Approach
GEO for Lending: Building the Citation Signal Private Lenders Need to Be Recommended
Our strategy for Titan Funding was built on one insight: in financial services, AI systems function as trust filters. They cite lenders with demonstrable editorial presence, not just lenders with loan products. We focused on building the citation infrastructure that allows Titan to appear in AI responses at the specific borrower intents where the lending decision begins.
Intent-Mapped Content Architecture
We restructured Titan’s content around the specific intents private lending borrowers bring to AI systems: loan type, deal structure, borrower profile, and project category. Each content cluster was built to function as a self-contained, citation-ready answer to a defined borrower question, not as general brand copy. This gave AI retrieval systems extractable, specific information to cite rather than broad marketing content to ignore.
E-E-A-T Signal Reinforcement
Finance content faces a higher credibility bar in AI citation pipelines. We audited Titan’s existing content against the experience, expertise, authority, and trust signals that LLMs use to evaluate lender sources, and systematically addressed the gaps. Author credentials, loan term transparency, and process-level specificity were embedded across cited pages to make Titan’s content legible to AI systems as a trustworthy source rather than a promotional one.
Cited Page Expansion Across Loan Intents
We identified the borrower intent clusters where Titan had relevant loan products but no citation-ready page, and built structured content to fill those gaps. Priority went to intents with high AI query volume and limited incumbent coverage. These are the entry points into the citation graph where new entrants can earn placements fastest without competing directly against well-entrenched players.
The Impact
116% Citations Growth: Titan Funding Now Has a Structural AI Presence in Private Lending
+116%
Citations growth (45 → 97)
+60%
Cited pages growth (35 → 56)
“Titan Funding now holds a citation position across enough loan-type and borrower-intent clusters that when a real estate investor asks an AI where to find a private lender, the brand has a structural reason to appear. Not as a result of ad spend, but as a credibility signal the AI has already encountered and stored.”
Actionable Insights
What Private Lenders Can Learn From This Campaign
AI Lender Recommendations Run on Editorial Trust
When a borrower asks an AI which private lenders to consider, the response is built from lenders with accumulated editorial citations, not lenders with the strongest Google Ads presence. A private lending firm with a thin citation profile will be absent from those responses regardless of its loan product quality or rate competitiveness.
Finance Content Requires Specificity to Be Cited
AI systems in financial services apply a stricter credibility filter than in most categories. Content that is promotional, vague about loan terms, or lacks demonstrable expertise is not cited. Lenders that want AI visibility need content that reads the way an underwriter explains a product: specific, process-grounded, and free of marketing abstraction.
Intent Fragmentation Is an Opportunity
Private lending borrowers search by deal type, geography, and borrower profile, and AI systems serve each intent from a different set of cited sources. Lenders with broad cited page coverage across those intents capture borrowers at multiple decision stages. Those with one or two strong pages miss the majority of the funnel.
GEO Compounds Fastest in High-Stakes Verticals
Borrowers consulting AI about a private loan are often close to making contact. Lenders that appear in those responses capture intent at its most commercially ready moment. Early investment in citation presence in financial services compounds because borrower queries are consistent, high-volume, and structurally tied to a financing decision rather than casual browsing.