The Client
Folio Wallet is a London-based digital wallet app for storing personal documents such as IDs, passports, payment cards, and medical records.
At the start of the project, Folio already had:
- A live website
- Presence on third-party platforms like social media, Reddit, and Trustpilot
What they didn’t have was measurable visibility inside large language models, specifically, being mentioned or cited when users asked ChatGPT relevant questions in their category.
The Timeline
Phase 1: Baseline & Testing (Aug 5 – Sep 10)
We ran initial tests across a wide range of prompts. It confirmed two things:
- There was no accidental or residual visibility.
- Any future movement would be attributable to the work done.
Result: 0% visibility. Folio did not appear in model outputs.
Phase 2: Setup & Prompt Strategy (Sep 11 – Sep 17)
We started with two fundamentals:
- Technical accessibility. Ensured the site was properly accessible for LLMs.
- Prompt set discovery. We built and validated a prompt set that could be tracked consistently over time. Turbine reports score every prompt and topic by a competitiveness index (how hard it is for a brand to appear in that space).
We deliberately looked for prompts at the intersection of:
- Folio’s real product features
- Topics valuable for the brand narrative
- Areas where competitors appeared, but Folio didn’t (yet)
That intersection became the core prompt set we tracked going forward.
>> We describe the general methodology for building prompt sets here.
We also chose ChatGPT as the primary model to measure. While AI visibility work can create spillover effects across multiple models, strategies differ by model. Given ChatGPT’s current query volume, we treated it as the reference point.
Result: The initial setup and technical fixes resulted in the first 18% of visibility share for the chosen set of topics and prompts.
Phase 3: Core Optimization (Sep 18 – Nov 22)
This was the main execution phase.
We focused on three core thematic clusters:
- Security
- General document storage
- A new niche Folio wanted to win, aligned with a new feature launch: storing tickets and personal documents in one place
What we did:
- Updated existing content (metadata, page structure, FAQ blocks)
- Created new content to close clearly identified semantic gaps
Results:
- Week 1: 20%
- Week 2: 37%
- Week 3: 50%
- Following month: stabilized at 43% visibility share
Phase 4: Stabilization Check (Nov 25 – Dec 14)
For three weeks, we made no changes at all.
We simply observed whether visibility would hold.
Result: it did. The gains were not short-term fluctuations.
Why This Worked
Turbine’s Approach to AI Search Visibility
Turbine is a lab, not a traditional SEO or marketing agency, and not just another tracking tool. Our focus is on translating AI visibility into something measurable and explainable.
Large Language Models ultimately see the world through vectors. This is a different environment from classical search. What matters here is not keywords, but semantics. One correctly placed concept is often more important than repeating the same keyword ten times. When you look at the problem this way, model behavior becomes more understandable and, to a degree, predictable.
Of course, there are many variables: different usage scenarios, hallucinations, and parallel work by competitors. But the average picture is still observable. With regular tracking, you can distinguish between a one-off result and a repeated pattern. Even accounting for model instability, movement from zero visibility to the 30–50% range is clearly measurable.
What We Did in Practice
For every topic (and for every prompt within that topic) we followed the same procedure. Below is a simplified example based on a single prompt.
At the start, Folio Wallet and the brands already appearing in responses were located in different semantic clusters. Existing brands occupied one cluster, while Folio sat in another. In this state, Folio had a low probability of being shown as an answer.
We then proposed new content for Folio. When this content was taken into account, the model placed Folio in the same cluster as the brands already visible for that prompt. At that point, Folio had a significantly higher chance of appearing in responses.
How We Created and Evaluated Content
If we go one level deeper: how do we decide what content to propose, and why do we expect it to work?
Content creation
We use standard content guidelines (structure, relevance, length, etc.), but the most important input is semantics as seen by the model itself. Even in Turbine’s free reports, you can see a preview of this approach: model answers are annotated with semantic entities, showing how the LLM interprets the text. This already provides a clear signal for how new content should be constructed.
The same logic can be explored independently using Turbine’s semantic keyword extractor. It returns keywords as the model understands them and groups them into semantic clusters.
>> Try Semantic Keyword Extractor
Content evaluation
In Folio’s initial state, semantic similarity scores did not exceed 0.84, which in this case was not sufficient to be included in results. Based on analysis, we proposed a new version of the text. With this version, similarity scores reached 0.9 (on par with brands that were already being shown for the same prompt).
After publishing the updated content, we observed that it was in fact cited by the model, and Folio began appearing consistently in the results.
Overall Results
Folio moved from low visibility to a leading position in its category. A clear narrative was established and reinforced.
The Folio website became a primary citation source when models talk about Folio.
Strategically, Folio:
- Secured its core position in security wallets and digital document storage
- Successfully entered and held a new space: all-in-one travel and personal documents
All of this while competing with products like Google Wallet and Apple Wallet, as well as long-established players in the travel segment.
