[ CASE STUDY ]
Language Learning App AI Search Case Study
How a Language Learning App Earned Visibility in AI “Best App” Recommendations
In just 3 days and with only 25 engagements, this campaign generated an estimated $169,171.84 in monthly branding value. That total includes $64,242.29 in organic keyword value and $104,929.55 in LLM cited-pages value.
Methodology Note
Directional estimate based on tracked keyword visibility, combined monthly search volume, and paid search benchmark value. Not exact attribution.
As language learners increasingly relied on AI summaries and trusted public sources to compare apps, this brand partnered with CiteWorks Studio to strengthen its citation footprint, building measurable visibility across high-intent discovery surfaces and the sources AI systems reference when recommending language learning tools.
[ KEY OUTCOMES ]
Results at a Glance
These outcomes were achieved in 3 days with only 25 engagements:
#8
average ranking position across the tracked keyword set
12
pages with strengthened brand context that AI systems commonly reference, within 5 days of campaign activation
770
high-value, intent-aligned keywords secured on page 1
1,034
tracked keywords with broadened organic footprint
[ MARKET CONTEXT ]
What Changed in the Market
Learners still begin with high-intent searches such as “best language learning app,” “learn Spanish app,” or “Babbel vs Duolingo,” but they increasingly validate their choices through trusted public discussions, creator-led lessons, and third-party review environments before committing.
That shift matters because AI systems now synthesize recommendations from the same sources people already rely on. A language learning app can rank well and still miss recommendation-stage visibility if it is underrepresented in the third-party conversations, comparisons, and review contexts shaping both learner perception and AI-generated answers.
In education products especially, trust signals carry weight. Learners want practical proof, credible teaching context, and balanced sentiment before subscribing, which makes citation footprint a strategic asset rather than just a reputation layer.
[ THE CHALLENGE ]
What the Brand Needed
The language learning app needed to strengthen its competitive presence across the sources shaping both Google discovery and AI-generated comparisons. That required improving three measurable signals:
AI Share of Voice
Improving competitive presence across the environments where prospective buyers actively compare options
Citations
Expanding visibility within the public pages and discussions AI systems cite when forming recommendations
Brand Mentions
Increasing how often the brand appears across relevant high-intent research prompts
The goal was not only to rank, but to be surfaced reliably at the decision moment, when buyers are forming a shortlist.
[ OUR APPROACH ]
What We Did
Pinpointed where the brand was missing in decision-stage discovery
We improved how the brand appeared across the sources buyers rely on, including public discussions, creator-led education, and third-party trust environments, so it showed up more consistently in the same places people and AI systems use to form recommendations.
Strengthened brand context across trusted third-party sources
We improved how the brand showed up across the sources buyers rely on, public discussions, creator-led education, and third-party trust environments, so it appeared more consistently in the same places people (and AI systems) use to form recommendations.
Measured what translated into real visibility lift
We tracked changes in keyword coverage and the number of AI-cited pages influenced, using search performance as supporting proof that stronger public-source coverage was expanding discoverability.
“Shortlist visibility matters more than rankings alone. We needed the brand to be cited in the trusted sources buyers consult and reflected accurately in AI comparisons. CiteWorks Studio helped us build and measure that footprint end-to-end.”
— Digital Marketing Team, Language Learning App
[ THE OUTCOME ]
Results
The campaign produced a stronger visibility footprint for the language learning app across both Google search and recommendation-shaping environments. By increasing presence in trusted third-party discussions, authority content, and review surfaces, the brand improved association with high-intent language-learning and comparison-related queries and strengthened recommendation-stage inclusion.
770 high-value, intent-aligned keywords secured on page 1
1,034 tracked keywords with broadened organic footprint
#8 average ranking position across the tracked keyword set
12 pages with strengthened brand context that AI systems commonly reference
These gains created a stronger foundation for sustained discovery as more learners begin their decision-making process through a mix of search, social proof, and AI-generated recommendations.
Want to Understand Your AI Citation Footprint?
We start every engagement with a full audit.
Measurable, Repeatable Programme
Build a durable foundation of credible citations that compounds over time and continues to influence AI answers as new queries emerge
Citation Architecture Review
Identify which high-authority community sources are and aren’t working in your favour across AI platforms.
AI Visibility Audit
Understand exactly how LLMs are referencing your brand today and which sources are shaping those answers.
[ LEARN MORE ]
Understanding AI Search Visibility
AI search experiences create answers by pulling information from many places online and then summarizing it into a single response. Large language models like ChatGPT, Gemini, Claude, and Perplexity review signals from websites, articles, and public conversations to respond to questions.
The concepts below explain how organizations can track and improve how often they appear inside those AI-generated answers and recommendations.
What Is AI Citation Intelligence?
AI citation intelligence is the process of measuring where AI platforms source their information and how frequently a brand is mentioned or referenced in AI-generated responses. Because LLMs synthesize across multiple sources, the sites and brands that appear repeatedly tend to influence how a topic or company is framed. This practice focuses on identifying which sources shape AI outputs and tracking brand visibility across different AI systems.
What Is Citation Architecture?
Citation architecture describes the set of sources that consistently inform how AI systems talk about a brand, product, or topic. LLMs draw from websites, articles, forums, and public discussion, and the sources they rely on most often become the backbone of their answers. Building strong citation architecture means ensuring that accurate, credible, high authority sources are the ones most likely to shape the way AI tools summarize and recommend a brand.
What Is Generative Engine Optimization?
Generative engine optimization (GEO) is the practice of improving the chances that AI systems use and cite your brand or content when generating answers. While traditional SEO is centered on ranking pages in search results, GEO focuses on how LLMs retrieve, interpret, and combine information when responding to a question. The objective is to strengthen the content and sources AI systems rely on, so your brand is treated as a trusted reference in AI responses.
What Is AI Share of Voice?
AI share of voice tracks how often a brand appears in AI-generated answers compared with competitors in the same category. It reflects visibility across AI platforms such as ChatGPT, Gemini, Claude, and Perplexity. Monitoring AI share of voice helps organizations see whether AI systems consistently include and recommend their brand for key queries or whether competitor brands are showing up more often.
Founder & Head of Agency
[ ABOUT THE AUTHOR ]
Mark Huntley
Mark Huntley, J.D. is the founder of CiteWorks Studio, a strategic advisory focused on visibility, authority, and recommendation presence in AI-shaped search environments. His work centers on embedding-level GEO, vector optimization, and cosine gap engineering — helping brands align their digital presence with the retrieval systems that increasingly shape discovery, interpretation, and choice.