RicketyRoo Case Study
RICKETYROO CASE STUDY
This case study was written by Aimee Jurenka at Rickety Roo. Aimee used Waikay to identify where AI models were failing to connect a local builder to its core topic, designed and implemented a structural content strategy to fix it, then used Waikay to track and confirm the results.
The findings are worth paying attention to. One focused structural update, with no redesign and no large content sprint, improved AI visibility scores across four different AI models within two months.
This page summarises a case study originally written by Aimee Jurenka at Rickety Roo. If you want to read the full original analysis, methodology and findings in Aimee’s own words, you can find it on the Rickety Roo website.
Read the original case study at Rickety Roo →THE PROBLEM
Aimee’s starting point was a Waikay topic report. The site had relevant content covering the brand, the service and the location, but the Waikay data told a different story to what the client expected.
The scores showed that AI models were struggling to confidently connect the brand to its priority topic. The content was there, but it was scattered. There was no strong central page tying together the brand, the service and the location in a way that was easy for AI systems to interpret.
This matters more in AI search than many people realise. AI models are not reading a site the way a human does, clicking through pages and forming a picture over time. They are pattern-matching against what the training data made clear. Scattered content means scattered signals, and scattered signals mean the model has to guess.
Waikay made this problem visible. Without the data, it would have been invisible to the client entirely. Their content existed, so on the surface everything looked fine.
Aimee’s hypothesis was straightforward: if the site had a stronger content hub around a single priority topic, with supporting content connected to it, AI systems would become more confident in associating the brand with that topic. Waikay would be used to test whether that was true.
RICKETYROOS APPROACH
On 24 October 2025, Aimee implemented a focused entity-building update for one priority topic. Waikay’s content gap analysis shaped each step of the process.
Using Waikay’s reports, Aimee identified exactly which topic entity the brand needed to strengthen. This meant being precise about the combination of brand, service and location that mattered most commercially, and understanding how AI models were currently describing that space versus how they should be describing it.
A new central category page was created to act as the clearest possible signal on the site for the target topic. It was built to make the relationship between brand, service and location unmistakable, not just to humans reading it but to the AI systems Waikay was monitoring.
Rather than creating new content from scratch, Aimee used Waikay’s gap analysis to identify which existing blog posts were relevant but disconnected. Those were reorganised to support the new category page and strengthen the topic cluster. This was not a redesign. It was a structure-first update guided directly by what Waikay’s data showed was missing.
HOW WAIKAY TRACKED THE IMPACT
Aimee used Waikay to measure how well different AI models understood and associated the brand with the target topic before and after the update. This is not something you can see in Google rankings or website analytics. It requires querying AI models directly via API, logging every response, and scoring the brand-topic association in a repeatable and comparable way. That is exactly what Waikay is built to do.
The Waikay visibility score reflects how clearly a model connects a business to a specific topic. A higher score means the model is pulling accurate, on-brand information rather than guessing or missing the connection entirely.
Aimee compared Waikay scores from two points in time: before the update on 18 October 2025, and after the update on 12 December 2025. Four AI models were tracked: Sonar Pro, ChatGPT 4.1, Gemini 2.5 and Gemini-grounded 2.5.
THE RESULTS
| AI Model | Before | After | Change | % Increase |
|---|---|---|---|---|
| Sonar Pro | 86 | 92 | +6 | +6.98% |
| ChatGPT 4.1 | 84 | 92 | +8 | +9.52% |
| Gemini 2.5 | 83 | 89 | +6 | +7.23% |
| Gemini-grounded 2.5 | 83 | 92 | +9 | +10.84% |
AI visibility improved across all four models Waikay was tracking following Aimee’s topic-entity update. The improvements ranged from 6.98% to 10.84% depending on the model, with every model showing a meaningful positive shift.
This result would not have been visible without Waikay. There is no traditional SEO metric that captures brand-topic association across AI models. Traffic data would not have shown it. Rankings would not have shown it. Only by directly querying the models and tracking scores over time could Aimee confirm that the structural strategy had worked.
WHAT THE RESULTS MEAN
The lift came from structure, not scale. There was no major redesign, no large-scale content production and no outreach campaign. Just a clearer content architecture, identified through Waikay’s data, that made it easier for AI systems to understand what the brand should be known for.
For a small local business, that is a significant finding. When you make the relationship between brand, service and location easier for AI systems to interpret, those systems return more accurate and more confident answers. In this case, Aimee’s focused topic entity strategy helped multiple models better connect the brand to custom home building in Bend.
The cross-model consistency matters too. The improvement appeared across all four models Waikay was tracking, which suggests the structural change was working at the level of content clarity rather than exploiting any quirk of a specific model’s behaviour.
WHY THIS MATTERS
A lot of AI search conversation still makes it sound like only large brands with dedicated content teams can shape their AI visibility. Aimee’s case study pushes back on that directly.
A small local builder improved its Waikay visibility scores by tightening one topic cluster and strengthening the internal content relationships on its site. The investment was focused and deliberate, not large.
If AI models are struggling to connect a brand to an important topic, the answer is not always to produce more content. Sometimes the better move is to use Waikay to find exactly where the gap is, organise what already exists around it, and track whether it worked. That is exactly what Aimee did.
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