The Client

Lendbuzz is an AI-powered auto financing platform that helps dealerships approve more customers, increase loan volume, and expand access to credit for borrowers who lack traditional credit history. By analyzing alternative data and real-time risk indicators, Lendbuzz enables dealers to close more qualified buyers while reducing friction and manual decisioning.

Lendbuzz is well known across the auto financing ecosystem. Their technology, dealer enablement tools, and underwriting accuracy are respected by partners, yet none of this established strength translated into visibility within AI-driven search results.

Despite strong real-world traction, Lendbuzz was not being surfaced across LLMs for solution-aware and high intent evaluation prompts that directly match dealership buying behavior.

The Challenge

When we partnered with Lendbuzz, their AI search presence was nonexistent.

Across seven tracked LLM sources, including ChatGPT, Perplexity, Claude, and others, Lendbuzz had zero visibility for solution-aware queries such as:

• “No credit auto loans” (this is our <core> BOFU keyword)

• “AI auto lending solutions”

• “Dealership financing tools for credit-thin buyers”

• “Best alternatives to traditional credit scoring for auto loans”

Inside an AI-driven discovery environment, this lack of presence has real consequences.

Dealers who search for tools, platforms, or approval technologies inside LLMs were not being introduced to Lendbuzz.

Competitors with weaker technology but stronger structured content were being prioritized instead.

AI systems do not assume. They surface brands only when they can parse, trust, and cite the source material behind them. Lendbuzz had the product strength, the dealer success metrics, and the underwriting performance, but LLMs had no structured ecosystem to reference.

Our objective was clear:

We needed to give LLMs the content and citations required to recognize Lendbuzz as a leading financing solution and begin surfacing it during solution-aware evaluations.

The Solution

Our approach with Lendbuzz was the same proven framework we deployed for Churn Buster. The goal was to quickly build structured content and authority signals that LLMs consistently cite.

The strategy unfolded in two phases.

Phase 1: Build LLM-Optimized Content That Models Can Parse

The most effective way to influence AI search results is to give models the structured content that matches how they interpret and respond to evaluation prompts.

We created high quality listicles that targeted Lendbuzz’s core solution-aware queries. These listicles consistently outperform all other formats in LLM visibility for several reasons.

• They are simple for models to interpret

• The structure matches the way models answer “best tool” and “top platform” questions

• They attract backlinks naturally

• They provide clear hierarchy and comparison frameworks

• They give models the confidence to reference a source directly

Our listicles followed a consistent, LLM-friendly format:

1. Problem-focused openings

We introduced the challenges dealerships face: thin-credit buyers, low approval rates, outdated underwriting, and the pain of losing qualified customers.

2. Clear number one positioning for Lendbuzz

Lendbuzz was presented in the first position with:

• Feature clarity

• Real dealer use cases

• Performance advantages

• Approval improvement indicators

• A simple, readable summary

Here’s our guide on how to build software listicles that not only rank, but drive demos and trials.

3. Comparison frameworks that models use in citations

For every tool, we standardized:

• Best-fit use cases

• Ratings

• Integration availability

• Who the product is ideal for

• Short, scannable descriptions

4. Highly structured, short-form formatting

Models perform better when content is predictable. Every section used repeatable formatting, short paragraphs, and consistent labels.

5. Recency and freshness signals

We added clear publish dates, updated dates, and year indicators that models rely on when making relevance decisions.

Once published, these assets began being referenced by models almost immediately.

Within five days, Lendbuzz went from zero visibility to 37 percent visibility across our seven tracked LLM sources.

Phase 2: Build the Citations and Authority That Scale Visibility

Since the initial visibility has appeared, we’re shifting to strengthening and expanding it through targeted authority building.

Our second phase included three core activities:

1. Outreach to high-authority websites that LLMs already cite

Secure placements on sources that are heavily trusted by major AI models.

2. Inclusion in existing listicles that already appear inside LLM results

By inserting Lendbuzz into these assets, we’ll accelerate recognition inside LLMs that were already trained to cite these sources.

3. Direct link support to Lendbuzz’s new listicles

All links were pointed at the specific pages that fuel LLM visibility, creating a compounding authority loop.

This combination of structured content and focused authority signals is what lifts a brand from partial visibility to consistent, dominant presence across AI search engines.

The Results

The outcome of the initial activation was both immediate and significant.

1. From zero to 37 percent LLM visibility in five days

Across seven tracked sources, Lendbuzz went from complete absence to measurable presence within a single business week.

This included appearances inside ChatGPT for solution-aware financing prompts, along with growing visibility inside other leading AI search channels.

2. Position #1 for “No credit auto loans”

We skyrockted from an average position of 28 to position #1 (as of the time of writing, now over 1 week owning the position #1) for the core solution-aware target term “no credit auto loans”

2. Expansion across solution-aware and evaluation queries

Lendbuzz began surfacing for prompts related to:

• No credit auto loans

• Best auto financing platforms

• Tools for credit-thin borrowers

• AI auto lending solutions

• Dealer-focused loan platforms

• Auto loan approval technology

These prompts represent real dealership research and vendor evaluation behavior.

Final Thoughts

LLM-driven search is rapidly shaping how buyers discover financing platforms and approval technologies.

Vendors who invest early in structured content and citation-building will own the next generation of search visibility.

The work with Lendbuzz is a strong example of what is possible when the right structure and authority signals are introduced into the LLM ecosystem.

Within five days, Lendbuzz went from invisible to meaningfully present across seven AI search channels.

The foundation is in place. The acceleration is underway.

We are excited to help Lendbuzz continue expanding its AI search presence and becoming a reliable, consistently surfaced leader in auto financing across the entire LLM landscape.