Language Learning App AI Search Case Study
How a Language Learning App Earned Visibility in AI “Best App” Recommendations
How a Language Learning App Earned Visibility in AI “Best App” Recommendations
Language learners don’t choose apps through search results alone anymore. They compare options across trusted public discussions, creator-led lessons, and third-party reviews, and increasingly through AI-generated answers that synthesize those same sources.
For a language learning app, visibility is shaped not only by rankings, but by how the brand is cited, framed, and compared in the places learners trust during evaluation.
CiteWorks Studio strengthened the app’s presence across high-intent public discussions, authority channels, and third-party trust environments. As a result, we improved page-one influence, expanded keyword coverage, and increased the cited pages shaping how the brand appears during research and recommendation-stage discovery.
Language learners don’t choose apps through search results alone anymore. They compare options across trusted public discussions, creator-led lessons, and third-party reviews, and increasingly through AI-generated answers that synthesize those same sources.
For a language learning app, visibility is shaped not only by rankings, but by how the brand is cited, framed, and compared in the places learners trust during evaluation.
CiteWorks Studio strengthened the app’s presence across high-intent public discussions, authority channels, and third-party trust environments. As a result, we improved page-one influence, expanded keyword coverage, and increased the cited pages shaping how the brand appears during research and recommendation-stage discovery.
What This Visibility Could Be Worth
For a language learning app, the upside isn’t just incremental traffic, it’s shortlist inclusion when learners search for “best language learning app,” compare alternatives, and evaluate credibility signals before committing to a subscription.
This campaign generated an estimated $169,171.84 in monthly branding value. That estimate combines $64,242.29 in organic keyword value with $104,929.55 in LLM cited-pages value, reflecting the sources AI systems reference when forming recommendations and comparisons.
That matters because it increases the likelihood of being considered during evaluation, when users weigh options across search results, trusted third-party sources, and AI-generated answers. In a category driven by trust, retention, and recurring subscription revenue, stronger discovery can influence not just clicks, but trials, conversions, and long-term customer value.
Methodology Note:
Directional estimate based on tracked keyword visibility, combined monthly search volume, and paid search benchmark value. Not exact attribution.
Key Outcomes
Achieved an
average ranking
position of #8 across
the tracked keyword set
Strengthened brand context
across 12 pages that
AI systems commonly
reference,
within 5 days of
campaign activation
Secured page-1 placement
for 770 high-value,
intent-aligned keywords
Broadened the brand’s
organic footprint across
1034 tracked keywords
What Changed in the Market
Learners still start on Google with high-intent searches like “best language learning app,” “learn Spanish app,” or “Babbel vs Duolingo,” but they increasingly validate 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’s 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, making citation footprint a strategic asset, not just a reputation layer.
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:
Improving competitive
presence across the
environments where
buyers actively
compare options
AI Share of Voice
Improving competitive
presence across the
environments where
buyers actively
compare options
AI Share of Voice
Expanding visibility within
the public pages and
discussions AI systems
cite when forming
recommendations
Citations
Increasing how often
the brand appears across
relevant high-intent
research prompts
Increasing how often
the brand appears across
relevant high-intent
research prompts
Brand Mentions
What the Brand Needed
The goal wasn’t only to rank, it was to be surfaced reliably at the decision moment, when buyers are forming a shortlist.
The goal wasn’t only to rank, it was to be surfaced reliably at the decision moment, when buyers are forming a shortlist.
What We Did
Pinpointed where the brand was missing in decision-stage discovery
We mapped the high-intent surfaces shaping insurance-tech evaluation and identified the discussion environments most likely to influence both buyer research and AI citation patterns. We then aligned placements to the queries and comparison moments already driving consideration.
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
The campaign produced a stronger visibility footprint for the online 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 insurance and trust-related queries and strengthened recommendation-stage inclusion.
Secured page-1
placement for 770
high-value,
intent-aligned
keywords
Broadened the
brand’s organic
footprint across
1034 tracked keywords
Achieved an average
ranking position
of #8 across
the tracked keyword set
Strengthened brand context across
12 pages that AI systems commonly
reference, within 5 days of campaign
activation
These gains created a stronger foundation for sustained discovery as more budgeting decisions begin with 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.
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.
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.
About the author
Founder and Head of Agency
Mark Huntley
Mark Huntley, J.D. is the founder of CiteWorks Studio and a growth strategist focused on AI-driven discovery, citation architecture, and high-intent demand capture. With more than
a decade of experience across performance media, global
e-commerce, affiliate publishing, and search-led growth, he has built and scaled marketing systems that influence how brands are found, trusted, and chosen in competitive categories. His work centers on the signals that shape AI recommendations, including authority sources, prompt-cluster positioning, and recommendation rank across the moments that actually drive revenue.
Through CiteWorks Studio, Mark helps companies strengthen visibility, credibility, and decision-stage performance in an internet increasingly shaped by AI systems.
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.
About the author
Mark Huntley, J.D. is the founder of CiteWorks Studio and a growth strategist focused on AI-driven discovery, citation architecture, and high-intent demand capture. With more than
a decade of experience across performance media, global
e-commerce, affiliate publishing, and search-led growth, he has built and scaled marketing systems that influence how brands are found, trusted, and chosen in competitive categories. His work centers on the signals that shape AI recommendations, including authority sources, prompt-cluster positioning, and recommendation rank across the moments that actually drive revenue.
Through CiteWorks Studio, Mark helps companies strengthen visibility, credibility, and decision-stage performance in an internet increasingly shaped by AI systems.
Founder and Head of Agency
Mark Huntley
What This Visibility Could Be Worth
For a language learning app, the upside isn’t just incremental traffic, it’s shortlist inclusion when learners search for “best language learning app,” compare alternatives, and evaluate credibility signals before committing to a subscription.
This campaign generated an estimated $169,171.84 in monthly branding value. That estimate combines $64,242.29 in organic keyword value with $104,929.55 in LLM cited-pages value, reflecting the sources AI systems reference when forming recommendations and comparisons.
That matters because it increases the likelihood of being considered during evaluation, when users weigh options across search results, trusted third-party sources, and AI-generated answers. In a category driven by trust, retention, and recurring subscription revenue, stronger discovery can influence not just clicks, but trials, conversions, and long-term customer value.
Methodology Note:
Directional estimate based on tracked keyword visibility, combined monthly search volume, and paid search benchmark value. Not exact attribution.
Key Outcomes
Secured page-1 placement
for 770 high-value,
intent-aligned keywords
Broadened the brand’s
organic footprint across
1034 tracked keywords
Achieved an
average ranking
position of #8 across
the tracked keyword set
Strengthened brand context
across 12 pages that
AI systems commonly
reference,
within 5 days of
campaign activation
What Changed in the Market
Learners still start on Google with high-intent searches like “best language learning app,” “learn Spanish app,” or “Babbel vs Duolingo,” but they increasingly validate 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’s 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, making citation footprint a strategic asset, not just a reputation layer.
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:
Increasing how often
the brand appears across
relevant high-intent
research prompts
Brand Mentions
Expanding visibility within
the public pages and
discussions AI systems
cite when forming
recommendations
Citations
Improving competitive
presence across the
environments where
buyers actively
compare options
AI Share of Voice
The goal wasn’t only to rank, it was to be surfaced reliably at the decision moment, when buyers are forming a shortlist.
What We Did
Pinpointed where the brand was missing in decision-stage discovery
We mapped the high-intent surfaces shaping insurance-tech evaluation and identified the discussion environments most likely to influence both buyer research and AI citation patterns. We then aligned placements to the queries and comparison moments already driving consideration.
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
The campaign produced a stronger visibility footprint for the online 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 insurance and trust-related queries and strengthened recommendation-stage inclusion.
Secured page-1
placement for 770
high-value,
intent-aligned
keywords
Broadened the
brand’s organic
footprint across
1034 tracked keywords
Achieved an average
ranking position
of #8 across
the tracked keyword set
Strengthened brand context across
12 pages that AI systems commonly
reference, within 5 days of campaign
activation
These gains created a stronger foundation for sustained discovery as more budgeting decisions begin with 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.
AI Visibility Audit
Understand exactly how
LLMs are referencing your
brand today and which
sources are shaping those
answers.
AI Visibility Audit
Understand exactly how
LLMs are referencing your
brand today and which
sources are shaping those
answers.
Citation Architecture
Review
Identify which high-
authority community sources are and
aren't working in your
favour across AI platforms.
Measurable, Repeatable
Programme
Build a durable foundation
of credible citations that
compounds over time and
continues to influence AI
answers as new queries
emerge.
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.
About the author
Founder and Head of Agency
Mark Huntley
Mark Huntley, J.D. is the founder of CiteWorks Studio and a growth strategist focused on AI-driven discovery, citation architecture, and high-intent demand capture. With more than
a decade of experience across performance media, global
e-commerce, affiliate publishing, and search-led growth, he has built and scaled marketing systems that influence how brands are found, trusted, and chosen in competitive categories. His work centers on the signals that shape AI recommendations, including authority sources, prompt-cluster positioning, and recommendation rank across the moments that actually drive revenue.
Through CiteWorks Studio, Mark helps companies strengthen visibility, credibility, and decision-stage performance in an internet increasingly shaped by AI systems.