Kraken Appears in 97.7% of AI Answers. Here's Our Five-Step Plan to Improve Its AI Visibility.

Answer Capsule

In November 2026, Kraken appeared in 642 of 657 AI answers included in the Crypto Exchanges benchmark (97.72%). It received 319 recommendations (48.55%), with 237 Top 3 placements and 170 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 Kraken mentions, recommendations and placements compare with its Crypto Exchanges peers?

Kraken recorded 642 mentions (97.72%) and 319 recommendations (48.55%) across 657 answers included in the benchmark. Top 3 placements numbered 237; first-place recommendations numbered 170. Framing among its mentions was 344 positive, 297 neutral and 1 negative.

Kraken appears in nearly every answer in the benchmark, so reach is not the constraint. The gap sits between being mentioned and being endorsed: recommendations are a much smaller share of mentions, and first-place finishes are a smaller share again. That pattern suggests the brand is familiar to these systems but not consistently the preferred answer.

The lead over the next-ranked exchange is real but narrow on mentions, and wider on recommendations. That makes endorsement quality, not raw presence, the more useful competitive frame. Proposed work should therefore focus on how Kraken is described when it is chosen, not on chasing additional mentions.

Framing is overwhelmingly positive or neutral, with almost no negative wording. That is a healthy base, but positive wording alone does not equal a recommendation, so the team should treat sentiment and endorsement as separate scorecard lines rather than one blended score.

What changed since last month

Questions This Section Answers

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

Only months with verified results are shown. Monthly sample coverage is listed in Methodology.

From October 2026 to November 2026, Kraken's mention count fell from 660 to 642, while its recommendation count fell from 526 to 319. The recommendation rate was 78.16% and 48.55% respectively.

The November sample shows fewer mentions and a substantially lower recommendation rate than October. The movement is large enough to matter for planning, but the monthly samples are separate selections, so the drop should be read as an observed change rather than a diagnosed one.

Earlier months also moved without a settled pattern, which argues against treating any single month as a trend. The agency would present November as the current baseline and compare future months only after confirming that sample coverage and inclusion rules stayed comparable.

A useful next step is a short internal review of what changed in the answers themselves, such as which questions or competitors appeared alongside Kraken. That review is descriptive and does not assume a cause.

Step 1. Measure

Questions This Section Answers

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

AI answers contained 20 conflicting claims about Kraken: 0 critical, 10 high, 9 medium, 1 low. Of these, 10 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.

The conflicting claims give the team a concrete starting list rather than a vague concern about accuracy. Most sit at high or medium priority, which means a small number of topics can absorb the first fact-checking effort without spreading attention thinly.

A visibility comparison would show where Kraken is endorsed versus merely named, and pair that with the claim list so content and communications work on the same priorities. The agency would build a scorecard that tracks mentions, recommendations and placements separately, with the disputed topics flagged beside it.

Severity ratings order the work; they do not prove that Kraken or any publisher got something wrong. Each claim needs a current primary source before anyone acts on it.

Step 2. Make the website consistent

Questions This Section Answers

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

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 cluster around availability, fee levels and card details, all of which are factual and checkable. The New York availability conflict appears twice in different forms, which makes it the clearest candidate for early verification against Kraken's own current disclosures.

The fee conflict may reflect different products or different dates rather than an error, so the team should confirm which fee schedule each claim describes before drafting anything. The card network question is lower priority but easy to settle from an official source.

Proposed work is a verification pass on each topic, followed by page briefs only where the approved facts differ from what the site currently says. No correction should be requested until that check is complete.

Step 3. Align the external narrative

Questions This Section Answers

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

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 with the most appearances are broad comparison articles that also carry several conflicting claims, which makes them worth reading closely before any outreach. Their prominence in the answers does not show that they shaped the wording, so the review should start with what they currently say.

The agency would check each selected page against approved facts, note where it is accurate, and prepare evidence-backed requests only where a genuine discrepancy exists. Requests should be specific, cite primary sources, and accept that publishers may decline or may already be correct.

A page linked to a conflicting claim is a lead for review, not a finding of fault.

Step 4. Optimize priority company pages

Questions This Section Answers

• Which observed Kraken website pages should receive review briefs?

The company's website was cited 286 times across 131 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.

The cited company pages are mostly product and learning pages, with the fee schedule appearing in answers but rarely alongside a recommendation. That makes the fee page a natural candidate for a brief that states current pricing plainly and answers common comparison questions.

The learning pages about best exchanges and best apps are the closest match to the questions buyers ask, so briefs there should use approved facts and address the specific comparisons the answers raise. Product buy pages can carry short, accurate eligibility notes where availability is disputed.

Briefs should be written only after existing coverage is checked, so the team does not duplicate pages that already answer the question well.

Step 5. Publish around buyer questions

Questions This Section Answers

• Which observed buyer questions support content briefs for Kraken?

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

The recorded questions are mostly comparison and shortlist questions, which fits the pattern of Kraken being named often but endorsed less often. Content that directly addresses alternatives and best-exchange choices is the most relevant place to improve how Kraken is described.

Before commissioning anything, the team should check whether existing pages already answer these questions and simply need clearer, sourced statements. Where coverage is thin, briefs should answer the buyer question directly, name the trade-offs honestly, and avoid claims that cannot be supported.

These observations show what appeared in the answers; they are not evidence of search demand or of missing pages.

Our 90-day action plan

Questions This Section Answers

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

The first month establishes the baseline scorecard, a scoped fact-review list and a shortlist of pages and sources, each with an owner and an evidence requirement. Nothing is corrected in this phase; the goal is a clean, reviewable record.

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

Delivered work is tracked apart from answer outcomes. Completing a brief or sending a request does not by itself change visibility, and the scorecard will show both without implying that one caused the other.

Methodology

Questions This Section Answers

• How are Kraken 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 657 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 4672 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:

May 2026 measured a different set of companies, so it is excluded from this comparison. Reliable results for June 2026 are unavailable. This is a gap in the history, not a result of zero. Reliable results for August 2026 are unavailable. This is a gap in the history, not a result of zero.

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

Recommendations require both endorsement and positive wording, and placements sit inside recommendations rather than adding to them. Counts across companies cannot be summed into a number of answers, because one answer can cover several exchanges.

The monthly samples are separate selections, so comparisons describe what was recorded, not why it changed. Some months are missing from the history; those gaps are absent results, not zeros.

Cited pages and disputed claims still need independent verification against current primary sources before any action.

Our first three priorities

First, verify the New York availability and fee claims against Kraken's current official disclosures, since those topics carry the highest severity and appear in more than one form. Ambiguous matches should stay out of the correction queue until the entity and date are clear.

Second, prepare page briefs for the fee schedule and the main learning pages, using approved facts and answering the comparison questions the research surfaced. Check existing coverage first so the work fills real gaps.

Third, review the most-cited comparison articles for accuracy and prepare evidence-backed requests only where a genuine discrepancy is confirmed. Alongside this, build buyer-question briefs around alternatives and best-exchange choices, and track all of it on a scorecard that keeps completed work separate from measured visibility.