Tower 28 Appears in 51.4% of AI Answers. Here's Our Five-Step Plan to Improve Its AI Visibility.

Answer Capsule

In November 2026, Tower 28 appeared in 147 of 286 AI answers included in the Clean Makeup Brands benchmark (51.40%). It received 87 recommendations (30.42%), with 38 Top 3 placements and 21 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 Tower 28 mentions, recommendations and placements compare with its Clean Makeup Brands peers?

Tower 28 recorded 147 mentions (51.40%) and 87 recommendations (30.42%) across 286 answers included in the benchmark. Top 3 placements numbered 38; first-place recommendations numbered 21. Framing among its mentions was 94 positive, 53 neutral and 0 negative.

Tower 28 sits near the top of this clean makeup set on both mentions and recommendations, and its answer wording is almost entirely positive or neutral. That combination suggests the brand is already part of the shortlist conversation rather than fighting for recognition.

The gap worth noting is between being named and being endorsed. Tower 28 appears in many answers but is recommended in a smaller share, and first-place finishes are rarer still. The practical work is to identify which questions draw an affirmative endorsement and to strengthen the evidence those answers rely on. Because the benchmark sample is a selected set of answers, these rates describe this comparison only; we would pair the scorecard with a fact-check list so the client can see where visibility rests on claims that still need verification.

What changed since last month

Questions This Section Answers

• How did Tower 28 counts and coverage differ across the available monthly snapshots?

From October 2026 to November 2026, Tower 28's mention count fell from 302 to 147, while its recommendation count fell from 256 to 87. The recommendation rate was 40.89% and 30.42% respectively.

The monthly record shows a smaller answer sample in the latest month alongside lower mention and recommendation counts. The recommendation rate also moved down while the mention rate held roughly steady. These are observed movements in separate samples, and the comparability limit should be stated plainly rather than read as a cause.

What stands out is the first-place rate reaching its highest recorded level in the same month. That is a placement observation, not evidence that anything the brand did produced it, and it should not be presented as a trend until more months are compared.

We would treat the latest month as one reading and set up a scorecard that tracks each measure separately, so future movement can be described without implying that a smaller sample explains it.

Step 1. Measure

Questions This Section Answers

• What should the agency measure and verify first for Tower 28?

AI answers contained 1 conflicting claim about Tower 28: 0 critical, 0 high, 1 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.

A visibility comparison tells the client where Tower 28 stands relative to named competitors on mentions, recommendations and placement. On its own, though, it says little about whether the answers are resting on accurate brand facts.

The recorded conflict about cruelty-free certification is rated medium, so it does not demand emergency action but does deserve a place on a fact-check list. We would build that list with an owner and an evidence requirement for each item, then confirm the current position from primary sources before anything is changed.

This keeps measurement and verification as separate workstreams, so a priority list is not mistaken for a finding that someone made an error.

Step 2. Make the website consistent

Questions This Section Answers

• Which recorded claim differences about Tower 28 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 topic is cruelty-free certification, where captured answers name different certifying bodies. Both recorded claims are quotations from AI answers, not verified company facts, so the first task is to establish the entity, the date and the source behind each version.

Different dates can make an apparent conflict disappear, and a certification can change over time. We would verify the current status directly with the certifying body and the company before drafting anything.

Only if that check confirms a genuine inconsistency would we prepare a conditional page brief. The brief would use approved facts and would not assert which recorded claim is correct.

Step 3. Align the external narrative

Questions This Section Answers

• Which cited third-party pages are relevant to fact review for Tower 28?

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 selected for review include retailer guides, roundups and social posts, and several are linked to the same certification conflict. A citation does not establish that a page influenced an answer, and the same answer can touch more than one page.

We would check the current contents of the most relevant pages first, starting with the ones that appear across many answers and mention the company. Where a page states something the client can verify as outdated, we would prepare a specific, evidence-backed request rather than a general correction. Publisher control is never assumed, and responses would be logged separately from confirmed edits.

Step 4. Optimize priority company pages

Questions This Section Answers

• Which observed Tower 28 website pages should receive review briefs?

The company's website was cited 20 times across 12 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 company site is cited across a range of pages, but most citations are thin and several pages appear in no recommending answers. That pattern suggests the site is present in the conversation without strongly supporting endorsement.

We would prepare page briefs for the pages already selected, using approved facts and the questions buyers actually ask. Each brief would state what the page should establish and which claims need a source before publication.

Before proposing any new page, we would check whether existing coverage already answers the question. A cited page is not automatically a page that accompanied a recommendation, so the briefs focus on substance rather than citation counts.

Step 5. Publish around buyer questions

Questions This Section Answers

• Which observed buyer questions support content briefs for Tower 28?

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

The recorded questions cluster around clean, cruelty-free, paraben-free and natural makeup, plus a Sephora-specific version. Tower 28 is mentioned in most of these answers and recommended in slightly fewer, which makes the endorsement gap the interesting part rather than simple absence.

We would turn the strongest questions into content briefs that answer them directly and back each claim with approved evidence. Existing pages would be checked first, since some questions may already be covered and need strengthening rather than a new page.

These are recorded answer observations, not search volume, so the briefs should be treated as editorial priorities rather than proof of demand.

Our 90-day action plan

Questions This Section Answers

• How should the agency sequence the proposed Tower 28 work and judge completion?

The first month establishes the baseline scorecard, a scoped fact-review register and a shortlist of pages and sources. Each record needs an owner, an evidence requirement and a review status, so nothing moves forward on an unverified claim.

The second month produces approved page briefs and justified publisher requests. Confirmed edits, no-change decisions, requests and responses are logged separately, which keeps a request distinct from a correction.

The third month delivers buyer-question content briefs and a fresh scorecard. Completed work is recorded apart from measured answer outcomes, and no visibility gain is promised, since the samples and their comparability limits remain in view.

Methodology

Questions This Section Answers

• How are Tower 28 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 286 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 5527 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 report covers a single month, and the exact collection dates for the original answers have not been verified. Visibility rates rest on answers that met the benchmark's inclusion rules, and a recommendation requires both an endorsement and positive wording.

Answers can mention several companies, so company counts cannot be added to find distinct answers. Sentiment describes answer wording, not customer satisfaction. Citation appearances across all captured answers differ from benchmark citations and from recommending answers.

Monthly samples differ, so comparisons show how recorded results changed, not why. Cited page contents and disputed claims still need independent verification.

Our first three priorities

First, verify the cruelty-free certification position from primary sources and record the outcome before any page or publisher work begins. The medium rating means this can be handled in sequence rather than as an emergency, but it should not sit unresolved while other content is drafted.

Second, prepare page briefs for the selected company pages, prioritising those that appear in answers but not alongside recommendations. Each brief should use approved facts and state which claims need a source.

Third, build buyer-question content around the clean and cruelty-free themes, checking existing coverage first. A fresh scorecard would then track these measures separately, with completed work reported apart from answer outcomes.