Busuu Appears in 52.0% of AI Answers. Here's Our Five-Step Plan to Improve Its AI Visibility.

Mark HuntleyBy Mark HuntleyFounder and CEO
10 minutes read

Key Takeaways

  • Busuu appears in just over half of benchmark AI answers and is usually recommended when mentioned, but it rarely earns the top spot.
  • The main issue is positioning, not awareness, so the work focuses on improving where Busuu appears in answers rather than increasing mentions alone.
  • Two conflicting claims need verification before any page edits or publisher requests are made, including language count and CEFR coverage.
  • Priority actions include reviewing key Busuu pages, checking cited third-party sources, and building content around Spanish and French buyer questions.

Answer Capsule

In November 2026, Busuu appeared in 275 of 529 AI answers included in the Language Learning Software benchmark (51.98%). It received 259 recommendations (48.96%), with 91 Top 3 placements and 8 first-place recommendations. A mention means the company appeared in an answer; a recommendation means the answer affirmatively endorsed it.

Our plan compares the company with its competitors, checks conflicting AI claims against reliable sources, and identifies website pages, third-party sources and buyer questions to address. The historical results show how the figures changed across available months. Different monthly samples limit those comparisons, and conflicting AI claims require verification before corrections are proposed.

Current AI visibility

Questions This Section Answers

  • How do Busuu mentions, recommendations and placements compare with its Language Learning Software peers?

Busuu recorded 275 mentions (51.98%) and 259 recommendations (48.96%) across 529 answers included in the benchmark. Top 3 placements numbered 91; first-place recommendations numbered 8. Framing among its mentions was 259 positive, 15 neutral and 1 negative.

Rank

Company

Mentions

Mention rate

Recommendations

Recommendation rate

Top 3

Top 3 rate

#1

#1 rate

1

Duolingo

517

97.73%

473

89.41%

356

67.30%

234

44.23%

2

Babbel

473

89.41%

448

84.69%

379

71.64%

108

20.42%

3

Pimsleur

430

81.29%

416

78.64%

264

49.91%

28

5.29%

4

italki

323

61.06%

303

57.28%

85

16.07%

29

5.48%

5

Busuu

275

51.98%

259

48.96%

91

17.20%

8

1.51%

6

Rosetta Stone

199

37.62%

163

30.81%

55

10.40%

4

0.76%

7

Memrise

150

28.36%

129

24.39%

32

6.05%

2

0.38%

8

Lingoda

113

21.36%

103

19.47%

39

7.37%

6

1.13%

9

Mango Languages

57

10.78%

46

8.70%

12

2.27%

3

0.57%

10

Mondly

28

5.29%

19

3.59%

8

1.51%

3

0.57%

Busuu is named in roughly half of the answers in this benchmark, and almost every mention carries a recommendation. That is real reach: when the company appears, the answer usually endorses it. The gap sits in position, not presence. Busuu is recommended far more often than it is placed near the top of an answer, and first-place finishes are rare.

For a language-learning buyer, that means Busuu is a credible option in the conversation but seldom the lead suggestion. The agency would treat this as a positioning problem rather than an awareness problem, and would look at which questions and competitors sit between a recommendation and a top placement before proposing work.

What changed since last month

Questions This Section Answers

  • How did Busuu counts and coverage differ across the available monthly snapshots?

Metric

Jul 2026

Aug 2026

Sep 2026

Oct 2026

Nov 2026

Mentions

240 / 520 (46.15%)

265 / 522 (50.77%)

274 / 558 (49.10%)

246 / 539 (45.64%)

275 / 529 (51.98%)

Recommendations

212 / 520 (40.77%)

245 / 522 (46.93%)

249 / 558 (44.62%)

235 / 539 (43.60%)

259 / 529 (48.96%)

Top 3 recommendations

94 / 520 (18.08%)

107 / 522 (20.50%)

72 / 558 (12.90%)

86 / 539 (15.96%)

91 / 529 (17.20%)

#1 recommendations

4 / 520 (0.77%)

10 / 522 (1.92%)

6 / 558 (1.08%)

14 / 539 (2.60%)

8 / 529 (1.51%)

From October 2026 to November 2026, Busuu's mention count rose from 246 to 275, while its recommendation count rose from 235 to 259. The recommendation rate was 43.60% and 48.96% respectively.

The November sample shows more mentions and more recommendations than October, and the recommendation rate moved up with them. Top-three placements also rose, while first-place finishes fell back from the October figure. These are separate monthly samples, so the movement describes what was recorded, not a trend with a known cause.

The practical reading is that Busuu's presence in answers is holding up while its lead position remains volatile. The agency would watch the position measures across several months before drawing conclusions, and would not credit any single month's change to a specific action.

AI recommendation trend

Observed recommendation rate (%), Jul 2026 to Nov 2026

0%15%30%45%60%Jul 2026Aug 2026Sep 2026Oct 2026Nov 202640.77%Jul 2026: 40.77%46.93%Aug 2026: 46.93%44.62%Sep 2026: 44.62%43.60%Oct 2026: 43.60%48.96%Nov 2026: 48.96%
  • Busuu48.96%

Separate selected monthly samples. Missing points are unavailable, not zero. Provider/prompt equivalence is unverified; rate movement does not demonstrate causation.

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Step 1. Measure

Questions This Section Answers

  • What should the agency measure and verify first for Busuu?

AI answers contained 2 conflicting claims about Busuu: 0 critical, 0 high, 1 medium, 1 low. Of these, 0 were rated critical or high priority; the critical claims are included in that total. These ratings help prioritize fact-checking. They do not establish that a company or publisher made an error.

Two conflicting claims about Busuu appeared in the captured answers, and only one was rated medium priority. That is a small fact-checking load, which makes it practical to resolve properly rather than guess. The ratings order the work; they do not show that Busuu or any publisher got something wrong.

The agency would build a short scorecard pairing each disputed topic with the approved company position, then assign an owner and an evidence requirement to each. This gives the client a clear, bounded list of claims to verify before any page or publisher conversation begins.

Step 2. Make the website consistent

Questions This Section Answers

  • Which recorded claim differences about Busuu should be verified before proposed page edits?

Review topic

Severity

Recorded AI claim A

Recorded AI claim B

number of languages offered

low

around 13 languages

14 languages

CEFR level range covered

medium

A1 to B2

A1 to C1

Both columns quote AI answers, not verified company facts. Resolve the entity, date and source before deciding whether a correction is warranted; different dates can explain an apparent conflict.

The disputed topics are narrow: how many languages Busuu offers and how far its CEFR coverage extends. Both columns in the record are AI statements, not confirmed company facts, and a difference in dates could account for an apparent conflict. Nothing should be corrected until the current position is checked against primary sources.

The agency would confirm the entity, date and source behind each claim, then prepare conditional page briefs. If the approved facts differ from what answers say, the brief sets out the wording to use; if they match, the record closes with a no-change decision.

Step 3. Align the external narrative

Questions This Section Answers

  • Which cited third-party pages are relevant to fact review for Busuu?

Cited page selected for review

Citation appearances in all captured answers

Answers mentioning the company

Conflicting claims linked to this page

https://actualfluency.com/busuu

1

0

1

https://aicourseverdict.com/languages/busuu-premium-language-learning

1

0

1

https://fluentu.com/blog/reviews/busuu/

1

0

1

https://getslova.com/blog/busuu-review/

1

0

1

https://iaintfluent.com/blog/busuu-review

1

0

1

https://pcmag.com/reviews/busuu

1

0

1

These pages were selected because they relate to the company in the captured answers. Their current contents still need checking before any correction request. A page linked to a conflicting claim is not necessarily its cause, and the same answer may contribute to more than one row.

Six third-party pages were selected for review because they relate to Busuu in the captured answers. Each is linked to a conflicting claim, but a link is not proof of cause, and none of these pages has been checked for its current contents. The citation counts are low, so this is a focused review rather than a broad outreach program.

The agency would read each page against approved facts and decide whether a polite, evidence-backed request is justified. Where the page is accurate or the claim cannot be substantiated, the outcome is a documented no-change decision rather than a request.

Step 4. Optimize priority company pages

Questions This Section Answers

  • Which observed Busuu website pages should receive review briefs?

Company website page

Citation appearances in all captured answers

Benchmark answers citing the page

Recommending answers citing the page

https://busuu.com/en/spanish/best-apps-to-learn-spanish

23

18

17

https://busuu.com/

20

16

16

https://help.busuu.com/hc/en-us/articles/15936615354641-What-is-Busuu

9

5

5

https://busuu.com/en/course/learn-spanish-online

5

3

3

https://business.busuu.com/our-method

3

2

2

https://busuu.com/en/course/learn-turkish-online

3

3

3

https://blog.busuu.com/busuu-efficacy-study/

2

2

2

https://busuu.com/en/course/learn-french-online

2

2

2

The company's website was cited 81 times across 20 distinct page addresses; the table shows 8 pages selected for review. Zeros in the answer columns mean no matching answers were recorded. A cited page is not automatically a page that accompanied a recommendation.

Busuu's own site is cited widely, and the Spanish best-apps page and homepage account for most of that activity. The help article explaining what Busuu is also appears, which makes it a natural reference point for basic facts. Citation does not mean the page drove a recommendation, so these are candidates for review, not proven assets.

The agency would write page briefs for the priority pages using approved facts and the buyer questions observed in the research. Each brief would state what the page should answer clearly, leaving current page contents to be checked before any edit is proposed.

Step 5. Publish around buyer questions

Questions This Section Answers

  • Which observed buyer questions support content briefs for Busuu?

Question observed in the research

Benchmark answers to this question

Company mentions

Company recommendations

What app will make me fluent in French?

4

4

4

What are the best apps to use to learn Spanish?

5

4

4

What is the best app to use to learn Spanish?

6

4

4

What is the most efficient app to learn Spanish?

5

4

4

What app is better for learning French?

4

3

3

What app makes you fluent in Spanish?

6

3

3

What are the best apps to use to learn French?

4

3

3

What are the best programs to learn a language?

5

3

3

These are recorded answer observations, not search volume, verified demand, or proof of missing website content.

The recorded questions cluster around Spanish and French, with buyers asking which app is best, most efficient, or will make them fluent. Busuu is mentioned and recommended in most of these answers, so the opportunity is to be the clearest answer to a question the company already appears in, not to chase unfamiliar topics.

The agency would check whether existing pages already address these questions before proposing anything new. Where coverage is thin, a brief would set out the buyer question, the approved facts to include and the comparison points a learner would want.

Our 90-day action plan

Questions This Section Answers

  • How should the agency sequence the proposed Busuu work and judge completion?

Period

Deliverable

Completion criterion

Days 1–30

Baseline scorecard, scoped fact-review register and page/source shortlist

Each record has an owner, evidence requirement and review status

Days 31–60

Approved page briefs and justified publisher requests

Separate confirmed edits, no-change decisions, requests and responses

Days 61–90

Buyer-content briefs and a fresh scorecard

Record work completed separately from measured answer outcomes and comparability limits

The first month establishes the baseline: a scorecard, a scoped fact-review register and a shortlist of pages and sources, each with an owner and a review status. The second month turns confirmed facts into page briefs and justified publisher requests, with confirmed edits, no-change decisions and responses recorded separately.

The third month adds buyer-question content briefs and a fresh scorecard. Completed work is reported apart from measured answer outcomes, so the client can see what was delivered without treating delivery as proof of a visibility gain.

Methodology

Questions This Section Answers

  • How are Busuu answer metrics and citation populations defined?

This report covers 2026-11. The exact dates when the original AI answers were collected have not been verified. The visibility rates use 529 answers that met the benchmark's inclusion rules. A company is recommended only when the answer both endorses it and uses positive wording. Top 3 and first-place counts are part of those recommendations, not extra recommendations.

An answer can mention or recommend several companies, so company counts cannot be added to find the number of distinct answers. The net sentiment score is positive minus negative mentions, divided by all company mentions; it describes answer wording, not customer satisfaction.

The wider citation analysis covers 800 captured answers and 5437 appearances of cited URLs. Citation appearances across all captured answers differ from the number of benchmark answers citing a page and the number that also recommend a company. The current contents of cited pages and disputed company claims still need independent verification.

Monthly sample coverage:

Reporting month

Answers included

AI services covered

Services

Jul 2026

520

6

ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, Perplexity

Aug 2026

522

6

ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, Perplexity

Sep 2026

558

6

ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, Perplexity

Oct 2026

539

6

ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, Perplexity

Nov 2026

529

6

ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, Perplexity

The answer samples differ between months. These comparisons show how the recorded results changed; they do not establish why they changed.

The answers were collected on dates that have not been verified, and each month is a separate sample, so month-to-month movement shows what was recorded rather than why it changed. A recommendation requires both an endorsement and positive wording, and top-three and first-place counts sit inside recommendations rather than adding to them.

Cited pages and disputed claims still need independent checking. The wider citation figures cover a larger set of answers than the benchmark, so the two should not be read as the same measure.

Our first three priorities

First, verify the two disputed claims against primary sources and close each with a confirmed position or a documented no-change decision. This is small, bounded work that everything else depends on, because page briefs and publisher requests should rest on approved facts.

Second, prepare briefs for the priority company pages, starting with the Spanish best-apps page and the help article, using the buyer questions already observed. Third, review the selected third-party pages and decide, page by page, whether an evidence-backed request is warranted.

Position is the theme running through all three. Busuu is recommended often but rarely leads, so the agency would keep the scorecard focused on where Busuu sits in answers, not only on whether it appears.

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Understanding AI search visibility.

AI search experiences create answers by pulling information from many places online and summarizing it into a single response.

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

Mark Huntley

Founder and CEO

Mark Huntley, J.D. is 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.

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