Smileie Appears in 35.3% of AI Answers. Here's Our Five-Step Plan to Improve Its AI Visibility.

Answer Capsule

In November 2026, Smileie appeared in 102 of 289 AI answers included in the Invisible Braces benchmark (35.29%). It received 53 recommendations (18.34%), with 37 Top 3 placements and 15 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 Smileie mentions, recommendations and placements compare with its Invisible Braces peers?

Smileie recorded 102 mentions (35.29%) and 53 recommendations (18.34%) across 289 answers included in the benchmark. Top 3 placements numbered 37; first-place recommendations numbered 15. Framing among its mentions was 53 positive, 49 neutral and 0 negative.

Smileie sits in the middle of the field: it appears in roughly a third of the answers and earns an endorsement in about half of those appearances. That is real reach, but the gap to the leaders is wide, and the company is endorsed far less often than it is mentioned. The practical question is not whether Smileie is known to these systems, but why so many mentions stop short of an endorsement.

Placement is the sharper weakness. Smileie's shortlist and first-place counts are modest relative to its mention volume, meaning it is often named as one option among many rather than as a leading choice. Positive framing is strong, with no negative wording recorded, so the raw material is favourable. The agency would treat the endorsement gap, not the mention gap, as the priority.

What changed since last month

Questions This Section Answers

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

From October 2026 to November 2026, Smileie's mention count rose from 94 to 102, while its recommendation count rose from 36 to 53. The recommendation rate was 9.94% and 18.34% respectively.

November's counts are higher than October's on mentions, recommendations and first-place placements, and the recommendation rate moved up alongside them. That is an encouraging direction, but the monthly samples are separate selections of answers, so the movement describes what was recorded rather than proving a trend or a cause.

The earlier months show how uneven this record is: strong summer figures, a weaker autumn, then a partial recovery. Smileie should read November as a better sample, not as evidence that anything it did worked. The agency would keep the monthly scorecard running and resist drawing conclusions from any single month until the pattern holds across several.

Step 1. Measure

Questions This Section Answers

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

AI answers contained 3 conflicting claims about Smileie: 0 critical, 2 high, 1 medium, 0 low. Of these, 2 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.

Before chasing visibility, Smileie needs a clear picture of what the AI answers actually say about it. The research flagged a small set of conflicting claims, two of them high priority, centred on professional supervision and price. These are the statements most likely to shape a buyer's confidence, so they deserve attention first.

The agency would build a visibility comparison that shows where Smileie stands against the named competitors, paired with a fact-check list covering each disputed claim. The list would record what was claimed, where, and what evidence would settle it. That gives the client a defensible order of work rather than a scatter of fixes.

Step 2. Make the website consistent

Questions This Section Answers

• Which recorded claim differences about Smileie 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 claims are not minor details. Whether a licensed orthodontist is involved in treatment, and what the aligners cost, are exactly the questions a cautious buyer asks before committing. The recorded answers disagree with each other on both points, and the report does not decide which version is right.

The agency would verify each claim against current primary sources before proposing any change. Where the company's own pages are the best evidence, we would draft conditional page briefs that state the approved position plainly and answer the supervision and price questions directly. Ambiguous matches would stay out of the correction queue until the entity and date are confirmed.

Step 3. Align the external narrative

Questions This Section Answers

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

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 cost and comparison articles are the pages most worth reviewing first, since they recur across captured answers and carry conflicting claims. Repetition in answers is not evidence that a page shaped those answers, and one answer can touch several pages, so this list orders review work rather than settling anything.

Before any outreach, the agency would check each selected page's current contents against primary sources. Correction requests would go only to publishers where a genuine, evidence-backed discrepancy exists, framed around accuracy rather than placement. Pages already correct get a recorded no-change decision, and ambiguous company matches stay out of the queue.

Step 4. Optimize priority company pages

Questions This Section Answers

• Which observed Smileie website pages should receive review briefs?

The company's website was cited 46 times across 22 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.

Smileie's homepage carries most of the citation weight, which makes it the natural first page to strengthen. The cost-focused blog posts are cited less often and rarely accompany an endorsement, suggesting they inform buyers without persuading them. The single-arch product page is small in volume but appears alongside recommendations, which is worth understanding.

The agency would prepare page briefs for the priority pages using approved facts and the questions buyers actually ask. Each brief would specify the claims the page must settle and the evidence behind them. Existing coverage would be checked before any new page is proposed, so effort goes where it can change an answer.

Step 5. Publish around buyer questions

Questions This Section Answers

• Which observed buyer questions support content briefs for Smileie?

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

The recorded questions cluster tightly around cost and the cheapest option, with a secondary thread on at-home kits and whether braces can be had for a modest budget. Smileie already appears in several of these answers and is sometimes endorsed, so the opportunity is to be the clearest, most credible answer rather than to introduce a new topic.

The agency would turn these observed questions into content briefs, checking first whether the site already answers them well. Where coverage exists, the brief would focus on sharpening the answer; where it does not, a new page would be proposed. These are recorded answer observations, not verified search demand.

Our 90-day action plan

Questions This Section Answers

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

The first month is about getting the facts straight: a baseline scorecard, a scoped fact-review register and a shortlist of pages and sources, each with an owner and a clear evidence requirement. Nothing gets corrected until the evidence is confirmed.

The second month turns confirmed findings into approved page briefs and, where justified, publisher requests, with responses tracked separately from decisions. The third month produces buyer-question content briefs and a fresh scorecard. Delivered work will be recorded separately from measured answer outcomes, and no visibility gain is promised.

Methodology

Questions This Section Answers

• How are Smileie 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 289 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 5881 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 visibility rates rest on the answers that met the benchmark's inclusion rules, and the exact collection dates for the original answers have not been verified. A company counts as recommended only when an answer endorses it with positive wording; shortlist and first-place counts sit inside those recommendations rather than adding to them.

Answers can name several companies, so counts cannot be summed into distinct answers. The wider citation analysis covers a larger set of captured answers than the benchmark, and citation appearances are not the same as benchmark citations or endorsements. Cited pages and disputed claims still need independent checking, and the monthly samples differ, so comparisons show what changed, not why.

Our first three priorities

First, settle the supervision and price claims. These are the high-priority conflicts, they touch the core purchase decision, and every other piece of work depends on having an approved position. The agency would verify against current primary sources and record the outcome before anything is published or requested.

Second, strengthen the homepage and the cost pages with briefs built on those approved facts, so the pages buyers and AI systems already consult give a consistent, confident answer. Third, turn the recurring cost and cheapest-option questions into buyer content, checking existing coverage first. Each step is proposed work, and results will be measured separately from delivery.