Their goal was simple: generate qualified leads from both traditional SEO and AI search. 

To achieve this, we: 

• Applied Pain Point SEO to rank for high-buying-intent keywords in Google
• Built a GEO Topic Map that teaches LLMs when to recommend them in AI search

So whether prospects search on Google or ask LLMs (like ChatGPT or Claude) about survey software, they have a strong chance of being recommended as a solution.

Below, we explain the principles behind our GEO and SEO strategy and share examples of our results.

Our Strategy for Gaining Both Traditional SEO & AI Search Visibility for Our Client

The core principle of Pain Point SEO is prioritizing high-buying-intent keywords over the top-of-funnel informational content most brands focus on. Our data shows these bottom-of-funnel keywords convert 10x to 25x better than low-intent queries.

For example, instead of targeting keywords like “What is a net promoter score” or “What is a focus group,” where the reader still has a lot of learning to do before buying, we prioritize keywords like “best survey software for enterprises” or “best customer research tools.”

The same principles apply to AI search visibility, since LLMs rely on web results when users ask for product or service recommendations. But there’s an important distinction.

Unlike Google, where people type short, predictable queries, LLM conversations are longer, more detailed, and often informed by prior interactions. In many cases, these systems have weeks or months of context about a user, which they factor into their recommendations.

As a result, LLM recommendations are more personalized than Google search results.

Take two users evaluating our client’s survey software: a startup running feedback loops with early users and an enterprise looking for affordable scale. If both search “best survey software” on Google, they’ll see similar results. But the same query in an LLM would factor in previous conversations, and the recommendations would differ based on each user’s context.

In AI search, it’s less about ranking for well-defined keywords and more about being a brand the model recommends when a relevant topic comes up. 

That’s a fundamentally different challenge than ranking for short keywords. LLMs need enough context to make a compelling case for your product — and that context has to be relevant to each user’s situation.

We call this approach Topic-Based GEO.

Topic-Based GEO argues that to maximize AI search visibility, you need published content that gives LLMs what they need to position your product as the solution to specific use cases and situations (“topics”).

We visualize the entire universe of topics relevant to your business as a map and call it your GEO Topic Map. 

To build it, we start by brainstorming every use case and pain point someone might ask an LLM about survey software.

The result is our Topic Map: a body of content that addresses every conversation or situation in which an LLM might reasonably recommend our client’s survey software.

For example, our client works with:

• Startups using survey software to gather early customer feedback
• Enterprises with strict security requirements
• DIY market researchers looking for affordable tools

We then created highly specific content for each of these use cases. Below, we share our results.

Examples of Keywords We’re Ranking For & Our AI Search Visibility

Example 1: “DIY market research tools”

One of the first articles we published for our client targets conversations where a user is asking about DIY market research tools, a common use case among their ideal customers.

If we look inside Traqer.ai — the tool we developed to track AI search visibility — we’re showing up in the top 5 positions in ChatGPT, Perplexity, and Google AIO for the topic “DIY market research tools.”

In Traqer, we also see that whenever someone enters a prompt about DIY tools for market research, our article is cited more than any other.

As a bonus, we’re ranking 4th for “DIY market research tools” on Google and generating leads from organic search.

Example 2: “CPG market research companies”

We also want LLMs to recommend our client’s survey software when someone asks for CPG market research companies, since CPG (consumer packaged goods) brands are among the heaviest users of market research.

So, we published a highly differentiated article on “CPG market research companies” that shows how our client’s survey software addresses the specific pain points of CPG brands.

In Traqer, we can see that we’re in the top 3 recommendations in ChatGPT, Perplexity, and Google AIO for prompts related to CPG market research companies.

LLMs also pull from our article more than any other piece on this topic.

We’re also currently ranking 1st for the keyword “CPG market research companies” on traditional Google search.

Example 3: “Enterprise survey software”

A large percentage of our client’s customer base is enterprise clients looking for software that scales well and doesn’t become increasingly expensive as they add users. So, we knew that enterprise survey software is an important topic to show up for on LLMs.

In Traqer, you can see that we consistently rank in the top 5 for queries related to enterprise survey software.

Our article is also the source AI cites most often when users ask about enterprise survey software.

On top of that, the article we published targeting this keyword is ranking 2nd on Google.

These are just a few examples, since we can’t realistically cover every topic we have visibility for in AI search.

Overall, the topic map we built gives our client AI search visibility across 100+ high-buying-intent topics.

Our Client’s AI Search Visibility Versus Competitors

We also think it’s worth comparing our number of AI citations with those of competitors, as this helps put the effectiveness of our AI visibility strategy into context.

As shown in the screenshot below, our article is cited nearly twice as often as SurveyMonkey, Involve.me, and Quantilope on the topic of “AI survey tools.”

On the topic of “enterprise survey software,” AI tools are citing our client more than competitors like SurveyLab and Zonka Feedback.

The same is true for “conjoint analysis software.” We have more citations than Conjointly, OpinionX, and Qualtrics.

These are just a few examples, but the pattern holds across most topics. For most high-buying-intent prompts, AI tools cite our articles more frequently than those of our competitors.

Key Takeaways

This case study highlights two key takeaways for brands looking to appear in LLMs:

1. Prioritize high-buying-intent terms your target audience is likely to use in LLMs. This increases the chances your content is surfaced and cited when generating responses.
   
2. When writing articles targeting high-buying-intent keywords, be extremely detailed about your value proposition, differentiators, and why someone should choose you. This gives LLMs the context they need to present your solution clearly and persuasively.

Improve Your AI Search Visibility with Grow and Convert

Book a call with us to learn more about how we can help your brand show up for high-buying-intent queries in LLMs.

We started working with Level AI in 2024 with the goal of ranking for high buying-intent SEO keywords like “call center customer analytics tools.” In 2025, as AI search exploded in popularity, that goal expanded to include getting LLMs to recommend Level AI when users ask for customer intelligence solutions. 

Across both traditional and AI search, the goal is always the same: drive conversions, not just traffic. That principle guided both Level AI’s SEO and AI search (AEO/GEO) strategy. 

This case study focuses on the AI search component. We’ll show how we earned Level AI brand mentions (not just citations) in 100+ product-centric prompts using our Prioritized GEO strategy. 

If you’d like help improving your AI search visibility, get in touch with us.

How We Achieved AI Search Visibility for Level AI

Prioritized GEO is based on the fact that LLMs almost always search the web to help formulate responses when users ask for product recommendations. This process is known as grounding. Their training data alone is not always up to date or sufficient for these prompts, so they use search to supplement it. 

So, if we want LLMs to recommend Level AI when someone asks for product recommendations in their space, traditional search is the most efficient way to make that happen. For example, if a user asks ChatGPT for voice of customer platforms, it will search the web — and if our article discussing Level AI appears in those results, it significantly increases the chances of being recommended in the response. 

Learn more about Prioritized GEO.

What Keywords We Rank for and Where We Stand in LLMs

Let’s look at a few examples of high buying-intent keywords that are important for Level AI, the content we published to rank for each, where we rank, and the AI prompts we appear in.

Example #1: AI call center monitoring

One of the first keywords we targeted for Level AI was “AI call center monitoring,” and we now rank on the first page for that term.

Our AI visibility tool, Traqer, shows that we consistently appear in the top 5 positions in LLMs when users ask about AI call monitoring solutions. 

Note how Traqer asks about the topic in multiple ways. This is because AI search is not as reproducible as traditional SEO — meaning even slight variations in prompt wording produce different results, and even the same prompt asked at different times can yield different results. 

Traqer accounts for this by asking about a single topic in multiple different ways. We explain this in detail in Topic-Based GEO. 

Side note: We’ve noticed across dozens of brands that visibility on Perplexity and Google AIO often increases before ChatGPT. We hypothesize this is because Perplexity and Google AIO are more heavily influenced by traditional web search results. They function more like search summarizers, so traditional SEO rankings have a greater impact on their output. ChatGPT, on the other hand, blends more of its training data into its responses, which naturally biases it toward larger, more well-known brands. 

Beyond brand mentions of Level AI, we also see LLMs using our article as a source in responses to these prompts.

Here’s an example of Google citing our article and listing Level AI as a solution in its AI Overview for “AI call monitoring solutions.”

Example #2: Call center real time reporting

Another relevant topic for Level AI is “call center real time reporting” because it’s one of the few QA tools that analyzes customer conversations in real time, not after the fact.

Ahrefs shows we rank first for “call center real time reporting” on Google. 

Meanwhile, Traqer shows that we have good AI search visibility across ChatGPT, Perplexity, and Google AIO when someone asks for software suggestions for real-time call center reporting.

We also see that LLMs cite our article more than any other source for topics related to “call center real time reporting.” In fact, across the 6 prompts above, Google AIO, Perplexity, and ChatGPT cite our article a combined 15 times, almost twice as often as any other article. 

This is how you influence LLM responses: create content they discover through web search and use to generate their answers. 

Here’s Google listing Level AI as a popular solution in this space and citing our article first in its AI Overview:

Example #3: Call center analytics software

Another broad topic area where Level AI has a strong value proposition is “call center analytics software.” It’s a popular product category, and appearing in those discussions has long-term value for Level AI.  

So, we wrote an article targeting that keyword, which at the time of writing ranks first on Google.

In Traqer, we see strong visibility for prompts related to call center analytics software on ChatGPT, Perplexity, and Google AIO.

Perplexity recommends Level AI when it’s prompted with questions asking for call center analytics software options, and cites Level AI as one of the main sources it uses to formulate responses.

Example #4: Call center quality assurance tools

The fourth keyword we’d like to cover is “call center quality assurance tools,” where we rank third on Google.

In Traqer, Level AI has strong visibility on ChatGPT, Perplexity, and Google AIO when users ask about call center QA tools.

Although we’re not the top-cited article, LLMs consistently reference us across multiple prompts related to that topic.

We can’t cover every keyword we’ve targeted for Level AI, but these 4 examples demonstrate a clear pattern: ranking well for a keyword on Google gives us AI search visibility for prompts related to that keyword. Keywords we aren’t yet ranking for have little to no visibility in LLMs.

All in all, we’ve targeted 50+ keywords for Level AI so far, resulting in visibility in LLMs for 100+ related high buying-intent prompts.

Examples of AI Using Our Content to Respond to Prompts

Another core principle of our AI search visibility strategy is that the level of detail in your public content helps dictate how LLMs talk about your products and services (full article). 

This means LLMs act like your first salesperson — summarizing your product’s differentiators, value propositions, and arguments, and using them to educate users on what makes you different from competitors and why they should choose you.

As a result, brands need to produce thorough, detailed content that articulates their solution in extreme detail: who it’s for, use cases, features and benefits, pain points it solves, and differentiators versus competing solutions. This way, you feed LLMs your exact value propositions and differentiators so they can make a compelling case for your solution. 

If you publish generic content that echoes what others are already saying, even ranking in LLMs won’t help because it will describe you the same way it describes everyone else, making readers far less likely to convert.

The differentiators and value propositions we focused on for Level AI were:

• Level AI uses semantic intelligence to understand the full context of conversations, making its results more accurate than many alternatives. Most software simply looks for keywords.
• Level AI can detect emotions in customer conversations. Most call center QA software struggles with this.
• Level AI can auto-score all conversations based on the client’s QA rubrics. Teams don’t have to listen to hours of customer conversations and score agents manually.
• Level AI’s deep analysis of conversations uncovers hidden issues and revenue opportunities without surveys, sampling bias, or manual analysis.

As a result, we’d like to share screenshots of LLMs using almost the exact same text in their responses as we use in our articles.

When we ask ChatGPT for accurate AQM software, it emphasizes that Level AI doesn’t just look for keywords but can understand the full context of customer interactions. 

This is known as semantic understanding.

Semantic understanding is the main value proposition we focus on in our articles.

Here, we can see how ChatGPT says Level AI can detect, label, and filter specific emotions like anger, disappointment, worry, and happiness:

We use almost the exact same phrase in our articles:

These are just a few examples, but generally, when ChatGPT recommends Level AI for customer intelligence solutions, it mirrors the same language, value propositions, and differentiators we use in our content.

What This Means for Your Brand: 2 Key Takeaways

1. Target high buying-intent keywords and prompts your target audience is likely to enter into LLMs. LLMs search the web when users ask for product recommendations, so if you rank well for a keyword in Google search, they’re more likely to recommend your brand when users enter prompts related to that keyword.

2. Write highly specific, differentiated content. This gives LLMs the context to position your product or service clearly. Avoid publishing generic content, or your LLM pitch for your brand will sound the same as everyone else’s. Learn more about writing specific, high-quality content.

How to Take This Strategy Further in Your Own Organization

Ranking for high buying-intent keywords with highly differentiated content should give you a strong foundation for LLM search visibility and SEO rankings.

The next step in our Prioritized GEO strategy is Tier 2: citation outreach. This involves identifying the sources AI systems reference most for a given prompt or topic (which you can do in Traqer) and reaching out to those websites to request a mention of your brand. Sites like G2 or Capterra generally require payment, while competitors are often willing to trade mentions.

This works through the same underlying logic as Tier 1: owned content. When LLMs search the web to respond to user prompts, they see your brand mentioned across multiple sources (not just your own website), increasing the likelihood that they mention you more prominently.

That said, there are two mistakes to avoid when doing citation outreach:

1. ​Asking any website in your industry for mentions: We recommend being strategic and focusing on earning mentions in articles that LLMs are already citing.

2. Not controlling the narrative on other websites: We strongly advise writing the product snippet yourself and being specific about your value propositions and differentiators. This way, your messaging is consistent across sources, giving LLMs the information needed to position your solution in a clear and differentiated way.

Increase Your Brand’s LLM Visibility with Grow and Convert

If you’d like us to improve your brand’s LLM visibility and drive conversions, get in touch for a quick call.

But from what we see, there’s no hard data proving these tactics actually work.

Based on data from 20+ clients, we know that exposure in LLMs comes from our Prioritized GEO strategy: ranking for traditional SEO bottom-of-the-funnel keywords with highly differentiated content.

So, we wrote this article to show an example of how we used Prioritized GEO to help Constitution Lending, a private money lender, appear in the top three AI recommendations for 50+ bottom-of-the-funnel prompts, despite having a domain authority under 40.

How LLMs Recommend Products and Services: They Search the Web When Users Ask for Recommendations

Our AI search strategy is based on a simple reality: all major LLMs search the web to help formulate responses. This process is known as grounding.

In the example below, you can see how Google AIO — the largest AI search tool today by traffic — uses our content to generate its AI summary for the query “LLC mortgage lenders.” This is an important keyword for Constitution Lending, as a large portion of their target market consists of borrowers looking to secure mortgages under an LLC structure.

In this case, Google AIO is literally an SEO summarizer (an “overview” of the search results below), so it’s influenced by traditional SEO rankings. 

Even OpenAI says that ChatGPT searches the web whenever it determines users can benefit from web information — not only when someone explicitly clicks the “search” button.

You can see this when asking ChatGPT for product recommendations. It will almost always say, “Searching the web” (as shown below).

Sometimes, it even tells you what it’s searching for: 

Perplexity also lists its sources when formulating a response, which typically correlate with articles ranking on the first page of Google.

This means AI search visibility is fundamentally linked to traditional SEO. If we rank for “best private lender,” for example, LLMs are more likely to surface our content when searching the web to provide recommendations for private lenders.

In fact, there’s a strong correlation between the keywords we rank for and our visibility in AI search results.

For example, one of our most valuable keywords is “best LLC mortgage lenders,” where we currently rank #1 on Google. We also rank for close variations, including “mortgage loans for LLC” and “mortgage lenders for LLC.”

Using Traqer, the tool we developed to track AI search visibility, we can see that Constitution Lending appears as a top recommendation in ChatGPT (Column #1), Perplexity (Column #2), and Google AIO (Column #3) when users ask for LLC mortgage lenders.

Side note: Nobody knows exactly which prompt users are entering — especially in ChatGPT — because users often have long, multi-turn conversations. As a result, tracking visibility for a single prompt isn’t very useful. Instead, Traqer is built to monitor visibility across topics, where multiple prompts approach the same topic from different angles or with different wording. 

We don’t know exactly how users will phrase a given question, but we can run a range of natural-sounding prompts and measure overall visibility for that topic. If visibility is high, we know we’ll show up regardless of how users ask. If it’s low, we know there’s work to do.

For Constitution Lending, examples include topics like “private lenders” or “LLC mortgage lenders.” We’re also ranking on the first page for “best DSCR lenders,” “top DSCR lenders,” “DSCR lenders,” and “best DSCR providers,” among other variations.

In Traqer, we can see that we’re consistently in the top 1–3 brands cited for prompts related to DSCR lenders.

Here are the exact prompts we’re tracking for that topic and where Constitution is being mentioned or cited as a source:

What’s also interesting is that we see very little AI visibility for keywords we aren’t yet ranking for. 

For example, Constitution Lending recently started offering cash for houses, and we began publishing content on that topic not long ago. Some of those articles aren’t ranking yet. When we check Traqer for “sell my house fast Connecticut,” Constitution Lending rarely appears as a recommendation, showing how a lack of SEO rankings directly correlates with a lack of AI visibility.

These are just three examples, but the pattern is consistent across different keywords and clients. When we rank well in traditional SEO for a keyword, our AI search visibility for related prompts increases. When we don’t rank well in SEO, AI search visibility is limited. 

Below, we walk through our process for ranking in traditional Google search and, as a result, gaining exposure to LLMs.

Our 4-Step AI Search Visibility Strategy for Constitution Lending

Step 1: Identify Bottom-of-the-Funnel, High Intent Keywords

At the start of the engagement, we used Pain Point SEO to identify bottom-of-the-funnel keywords that someone urgently looking for a private lender would search for on Google.

We’ve written extensively about why you should target bottom-of-the-funnel keywords in your SEO strategy. In short, bottom-of-the-funnel keywords convert 10x to 25x better than top-of-the-funnel keywords because the reader is actively looking for a solution and there’s significant buying intent. 

In Constitution Lending’s case, that means borrowing intent (and yes, these keywords are still generating traffic and leads through traditional SEO, even in today’s post-ChatGPT and post-AIO world).

For Constitution Lending, these bottom-of-the-funnel keywords included:

• Best DSCR lenders in Connecticut
• Best LLC mortgage lenders
• Best fix and flip lenders
• Fastest closing private lenders

Articles that rank for these keywords — and therefore gain AI visibility — are far more likely to convert than top-of-the-funnel keywords (e.g., what is a DSCR lender, how do I calculate DSCR), where readers still need to do research before applying for a loan.

Focusing on bottom-of-the-funnel keywords is even more important in the context of AI search visibility because these are the only topics where LLMs mention brands, products, or services in their responses. If you ask an LLM a top-of-the-funnel question like “What is a DSCR loan,” it’ll simply explain the concept without recommending or linking to any brand, because there’s no buying intent.

The old content marketing convention — where a company like Constitution Lending would write content like “What is a DSCR loan” in hopes of getting traffic — is being upended by AI search. Content like that never converted well into leads (as we’ve shown for years), but it at least used to generate traffic. Now it doesn’t even do that. 

Step 2: Prioritize Lower-Competition Keywords Using Underdog SEO

Normally, we’d start producing differentiated content to rank for the bottom-of-the-funnel keywords identified in Step 1.

However, many of the lenders we were competing against had 80+ domain authority, thousands of backlinks, and decades in the lending space. Constitution Lending, by contrast, was a relatively new website with lower domain authority. We knew we were unlikely to outrank these high-authority competitors in the short term by targeting the same bottom-of-the-funnel keywords.

Instead, we applied our Underdog SEO framework to start ranking in Google and AI search results sooner.

Underdog SEO focuses on prioritizing keywords tied to specific use cases or verticals. These long-tail, vertical-specific keywords are typically easier to rank for because larger lenders aren’t targeting them, which means there’s less competition and less tailored content on the web. Since traditional SEO is the path to AI visibility, targeting lower-competition keywords helps us get on LLMs’ radars faster.

For example:

• What we didn’t target first: Best hard money lenders

• What we targeted: Best hard money lenders for first-time investors

• Why? Only one article on the first page is specifically tailored to first-time investors. The rest cover hard money lenders broadly and simply happen to rank for “best hard money lenders for first-time investors” due to limited competition. By creating content specifically tailored to first-time investors, we’re far more likely to rank well.

These are some of the vertical-specific keywords we ended up prioritizing for Constitution Lending:

• DSCR lenders for multifamily homes
• Private money lenders for residential real estate
• Hard money lenders for first-time investors
• DSCR loans for construction
• Commercial loans for rental properties
• DSCR loans for wholesalers and brokers

Step 3: Write and Publish Highly Differentiated Content on Constitution Lending’s Website

With a list of bottom-of-the-funnel keywords prioritized using Underdog SEO, we began drafting long-form blog posts to rank for them.

A common mistake at this stage is hiring freelance writers with little to no domain expertise. This often results in what we call a Google Research Paper: generic content that simply regurgitates what’s already been said online.

This is problematic because AI effectively acts like a salesperson. When LLMs search the web, they consume your content and use it to communicate your value props and differentiators to users. In other words, the content you publish directly shapes how LLMs describe and position your solution. 

For example, below you can see ChatGPT using nearly identical language to what we published in our article:

If your content lacks detail on how you address customer pain points better than the competition, AI won’t have enough information to make a compelling case for your solution. Instead, it will default to the same generic language it uses for every other option on the market.

Additionally, AI develops a deep understanding of users (their context, intent, and pain points) through both current and past conversations. When it searches the web, it looks for solutions that best align with that context. 

If your content doesn’t clearly explain how your solution addresses those needs, LLMs won’t connect your offering to users’ unique circumstances, which ultimately limits visibility.

This means your content needs to clearly articulate your differentiators and speak directly to the pain points your target customers are likely to express to LLMs.

• How are we better than the competition?
• What problems do we solve?
• Who do we serve best?
• In which scenarios or use cases do we truly shine?

With this in mind, we interviewed experts at Constitution Lending who work with borrowers every day and deeply understand their challenges. Those conversations surfaced a few consistent themes:

• Most lenders are slow. Borrowers want fast funding.
• Many lenders reject applications at the last minute. Borrowers want early certainty that they’ll get funded.
• Many lenders require large down payments. Borrowers want lower down payments.

We then created content that directly explains how Constitution Lending solves these problems.

As a result, when a borrower tells an LLM they’re looking for a fast, low-down-payment lender that won’t reject applications at the last minute, LLMs are far more likely to recommend Constitution Lending. We’ve essentially given LLMs the exact information they need to make that connection.

Step 4: Get Brand Mentions on Other Sites That Appear as Citations

Another important component of our Prioritized GEO strategy is securing brand mentions on sites that already appear as citations for the AI prompts we want to rank for. This places Constitution Lending within the articles LLMs actively reference when formulating responses. Brands shouldn’t build citations from just any website in their industry — only from sites that LLMs actually cite.

This works through the same mechanism described earlier. When LLMs search the web for solutions that match a user’s query, appearing across multiple cited sources (including your own site) increases overall AI visibility.

That said, we haven’t begun citation outreach for Constitution Lending yet. So far, we’ve focused on publishing high-quality content on their own site, which has already driven strong AI visibility. In the near future, we may begin outreach to cited websites to secure inclusion in their articles.

Note: We suspect that the lack of brand mentions across multiple third-party sources explains why they appear less frequently in ChatGPT compared to Google AIO and Perplexity. Our data suggests ChatGPT prioritizes diverse brand mentions and places less emphasis on owned content alone. We’ll continue monitoring ChatGPT visibility as we begin citation outreach.

Results: We Appear in the Top 3 AI Recommendations for 50+ Bottom-of-the-Funnel Prompts

Without getting too deep into the specifics of Constitution Lending’s business, our content strategy focuses on four core areas:

1. Lending: Constitution Lending provides financing to borrowers purchasing investment properties.

2. Investing: Constitution Lending allows investors to purchase shares in loans in exchange for borrower interest payments.
   
3. Cash for houses: Constitution Lending buys houses directly, helping homeowners sell in as little as 24–48 hours.
   
4. Non-performing loans: Constitution Lending operates a real estate fund that invests in non-performing loans.

Lending Results

On the lending side of the business, the goal was to get LLMs to recommend Constitution Lending to users when they search for any type of real estate investment lender. 

In the screenshots below, you can see that they’re first in Google’s AI overview for numerous keywords:

If we look inside Traqer, you’ll see that they have strong visibility for several bottom-of-the-funnel prompts in ChatGPT, Perplexity, and Google AIO:

Investor Results

We also create content targeting keywords investors would search for when evaluating real estate loans. 

As a result, Constitution Lending appears as a top recommendation for bottom-of-the-funnel AI prompts such as:

We Buy Houses Results

Constitution Lending recently expanded into the houses-for-cash business, helping homeowners sell quickly at a fair price. They can purchase houses in as little as 24 hours, compared to the months it often takes with a traditional real estate agent.

Our objective, therefore, is to appear in AI search results whenever someone asks for help selling their house fast.

We only began publishing “houses for cash” content a few months ago. Even so, Constitution Lending is already being recommended for several bottom-of-the-funnel AI prompts such as “sell my house fast Waterbury” and “sell my house fast Bristol.”

Non-Performing Loan Fund Results

The non-performing loan fund is another part of the business where we only recently began publishing content. Many of the real estate investment funds we’re competing against are massive — firms like Blackstone and Vanguard that have been around for several decades.

As a result, we expected it would take several months to gain traction in AI search results. However, we’re already seeing AI search visibility for prompts where users are actively looking for a fund.

Increase Your AI Search Visibility with Our Prioritized GEO Strategy

Schedule a quick call to learn how we can help your brand increase AI search visibility for high-buying intent keywords.