Memrise Appears in 28.4% of AI Answers. Here's Our Five-Step Plan to Improve Its AI Visibility.

Answer Capsule

In November 2026, Memrise appeared in 150 of 529 AI answers included in the Language Learning Software benchmark (28.36%). It received 129 recommendations (24.39%), with 32 Top 3 placements and 2 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 Memrise mentions, recommendations and placements compare with its Language Learning Software peers?

Memrise recorded 150 mentions (28.36%) and 129 recommendations (24.39%) across 529 answers included in the benchmark. Top 3 placements numbered 32; first-place recommendations numbered 2. Framing among its mentions was 129 positive, 20 neutral and 1 negative.

Memrise appears in roughly a quarter of the benchmark answers and is recommended in nearly as many, so its reach is real rather than incidental. The gap between mentions and recommendations is narrow, which suggests that when the company surfaces, the answer usually treats it favourably rather than merely listing it.

Position is the weaker dimension. Memrise rarely lands in the top three and almost never takes first place, so it is present in the conversation but seldom the answer's leading choice. Reach and rank should be tracked as separate goals, because widening presence and climbing position call for different work.

We would build a scorecard that keeps mention, recommendation and placement distinct, so the client can see which one is moving and avoid reading a recommendation ratio as conversion.

What changed since last month

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

From October 2026 to November 2026, Memrise's mention count fell from 161 to 150, while its recommendation count fell from 138 to 129. The recommendation rate was 25.60% and 24.39% respectively.

The monthly record shows Memrise's presence easing across the recent samples, with both mentions and recommendations lower in the latest month than in the one before. The top-three count also moved, while first-place appearances held steady at a very small number.

The samples differ between months, so these are separate observations rather than a like-for-like trend, and the movement should not be assigned a cause. What matters for planning is that the direction is consistent across the headline measures.

We would treat the latest month as the working baseline and watch the next samples before drawing conclusions, keeping the comparability limit beside any month-to-month statement.

Step 1. Measure

Questions This Section Answers

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

AI answers contained 0 conflicting claims about Memrise: 0 critical, 0 high, 0 medium, 0 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.

No conflicting claims about Memrise were recorded in the captured answers, so there is nothing in the fact-check queue that carries a critical or high rating. That is a clean starting position, but it is a statement about the answers sampled, not a guarantee that every claim elsewhere is accurate.

A visibility comparison would show where Memrise sits against the leaders on presence and position, giving the client a clear sense of the distance to close. A scoped list of claims worth checking would then direct verification effort where it can actually change an answer.

We would combine both into one baseline scorecard, with each item assigned an owner and an evidence requirement.

Step 2. Make the website consistent

Questions This Section Answers

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

No results are available for this part of the analysis.

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.

This part of the analysis returned no results, so there are no disputed topics to work through and no page briefs can be justified from the evidence at hand. We would not manufacture corrections to fill the gap.

Instead, the proposed work is a verification pass: confirm the entity, the date and the source behind any claim before deciding whether a page needs attention. Different dates can make two statements look contradictory when they are not.

Where verification does support a change, we would prepare a conditional page brief using approved facts, and leave everything unverified outside the queue.

Step 3. Align the external narrative

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

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.

The cited pages range from a widely referenced review site to smaller app round-ups, and several of them appear in answers that mention Memrise. Citation volume is concentrated in a few sources, which makes them the sensible first places to look.

A citation does not show that a source shaped the answer, and none of these pages is linked to a conflicting claim. So the work here is review, not accusation: check what each page currently says, then decide whether an evidence-backed request is warranted.

We would start with the most frequently cited pages and record confirmed edits, no-change decisions and any publisher responses separately.

Step 4. Optimize priority company pages

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• Which observed Memrise website pages should receive review briefs?

The company's website was cited 7 times across 4 distinct page addresses; the table shows 4 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.

Memrise's own site was cited a handful of times across a small set of page addresses, with the homepage and a few language pages among them. The counts are modest, and a cited page is not automatically one that accompanied a recommendation.

Page briefs would draw only on approved company facts and the questions buyers actually ask, so each page answers a real need rather than restating marketing copy. Existing coverage should be checked before any new page is proposed.

We would prioritise the pages that already attract citations, since improving them builds on demonstrated relevance rather than starting from nothing.

Step 5. Publish around buyer questions

Questions This Section Answers

• Which observed buyer questions support content briefs for Memrise?

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

The recorded questions cluster around a few themes: which app is best overall, which suits Spanish learners, and which teaches a specific language such as Farsi or French. Memrise appears in answers to most of these, and is recommended in nearly all of those appearances.

That pattern suggests the company is already part of the consideration set for these questions, so the opportunity is depth rather than introduction. These are observed answers, not search volume, and they do not prove the website lacks coverage.

We would check existing pages first, then draft buyer-question briefs for the themes where Memrise is mentioned but not consistently recommended.

Our 90-day action plan

Questions This Section Answers

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

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. Nothing is corrected at this stage; the point is to know what is being judged and against what evidence.

The second month turns verified findings into approved page briefs and justified publisher requests, with confirmed edits, no-change decisions, requests and responses logged separately. The third month adds buyer-content briefs and a fresh scorecard.

Delivered work will be reported apart from measured answer outcomes, because completing a brief does not establish that visibility improved.

Methodology

Questions This Section Answers

• How are Memrise 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:

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 at dates that have not been verified, and the monthly samples differ, so comparisons describe recorded results rather than causes. A company counts as recommended only when an answer endorses it with positive wording, and top-three and first-place placements sit inside those recommendations rather than adding to them.

Answers can mention several companies, so company counts do not sum to distinct answers. Citation appearances across the wider capture are a different measure from benchmark answers citing a page. Cited pages and disputed claims still need independent verification.

Our first three priorities

First, confirm the facts. With no conflicting claims recorded, the immediate task is a verification pass on the most-cited sources and the company's own pages, so any later correction rests on current primary evidence rather than an AI statement.

Second, strengthen position rather than presence. Memrise already appears and is usually treated favourably, but it rarely leads an answer, so page briefs should target the buyer questions where it is mentioned without being the recommended choice.

Third, keep the scorecard honest. Mention, recommendation and placement stay separate measures, and completed work is reported apart from answer outcomes so the client can see what was delivered without mistaking it for a visibility gain.