Sesame Appears in 60.4% of AI Answers. Here's Our Five-Step Plan to Improve Its AI Visibility.

Answer Capsule

In November 2026, Sesame appeared in 241 of 399 AI answers included in the Online Doctors benchmark (60.40%). It received 198 recommendations (49.62%), with 115 Top 3 placements and 46 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 Sesame mentions, recommendations and placements compare with its Online Doctors peers?

Sesame recorded 241 mentions (60.40%) and 198 recommendations (49.62%) across 399 answers included in the benchmark. Top 3 placements numbered 115; first-place recommendations numbered 46. Framing among its mentions was 202 positive, 39 neutral and 0 negative.

Sesame sits near the top of this online-doctor set on both mention and recommendation rates, and its answer wording is almost entirely positive. The gap worth noting is placement depth: it is recommended often but named first far less often than the leader, so the practical question is how to turn warm mentions into stronger positioning.

That pattern suggests the brand is familiar enough to be included but not always the obvious pick. Proposed work would compare the questions where Sesame is named first against those where it is only listed, then brief content and source reviews around the difference.

What changed since last month

Questions This Section Answers

• How did Sesame 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, Sesame's mention count fell from 339 to 241, while its recommendation count fell from 280 to 198. The recommendation rate was 49.56% and 49.62% respectively.

Between the two reporting months, Sesame's mention and recommendation counts both fell while its recommendation rate held essentially flat. Top-three and first-place counts also came in lower. These are separate selected samples, so the movement describes what was recorded, not why it happened.

The stable recommendation rate is the more useful signal: the share of answers that endorsed Sesame did not weaken even as the sample changed. Proposed work would keep tracking that rate month to month and treat placement depth as a separate measure to watch.

Step 1. Measure

Questions This Section Answers

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

AI answers contained 11 conflicting claims about Sesame: 0 critical, 0 high, 7 medium, 4 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.

The fact-check list is the practical starting point. Most of the conflicting claims about Sesame were rated medium priority, with the rest low, and none critical or high. That means the work is careful tidying rather than crisis response, and the ratings only order the checking, not prove anyone erred.

A visibility comparison would show where Sesame is named first versus merely listed, so effort goes to the questions that matter. Proposed work pairs that comparison with a scoped claim register, each entry carrying an owner and the evidence needed to confirm or dismiss it.

Step 2. Make the website consistent

Questions This Section Answers

• Which recorded claim differences about Sesame 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 cluster tightly around visit pricing, with several different figures appearing across answers. Both sides of each conflict are AI statements, not verified company facts, so the first job is to establish which price applies to which service and date before anything is called wrong.

Proposed work is a verification pass against current primary sources, followed by page briefs only where the approved facts differ from what answers say. Where dates or service tiers explain the gap, no correction is proposed.

Step 3. Align the external narrative

Questions This Section Answers

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

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 are third-party health and telehealth roundups plus a competitor site, and several carry more than one conflicting claim. A citation does not establish that a page shaped the answer, and the same answer can touch more than one row, so these are leads rather than culprits.

Proposed work checks each page's current contents first, then prepares evidence-backed requests only where a genuine inaccuracy is confirmed. Pages that are simply dated or generic would get a no-change decision recorded instead.

Step 4. Optimize priority company pages

Questions This Section Answers

• Which observed Sesame website pages should receive review briefs?

The company's website was cited 152 times across 50 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.

Sesame's own site is cited widely, but the selected pages show uneven involvement: some appear in benchmark answers, others in none. The urgent-care complaint page and the doctor's-note complaint page draw the most citations, while several medication and blog pages were cited without matching answers in the recorded set.

Proposed work writes page briefs for the highest-citation pages using approved facts and real audience questions, and checks existing coverage before proposing anything new. A cited page is not automatically one that accompanied a recommendation, so briefs focus on clarity rather than chasing placement.

Step 5. Publish around buyer questions

Questions This Section Answers

• Which observed buyer questions support content briefs for Sesame?

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

The recorded questions are almost all broad best-provider and top-company prompts, and Sesame was mentioned and recommended in nearly every answer to them. That is a strong position to defend rather than a gap to fill, and these observations are not search volume or proof of missing content.

Proposed work turns the observed questions into buyer-question briefs that check existing pages first, then strengthen comparison and selection content where coverage is thin. The aim is to keep Sesame the easy answer when buyers ask who is best.

Our 90-day action plan

Questions This Section Answers

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

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

The third month delivers buyer-content briefs and a fresh scorecard. Completed work is reported apart from measured answer outcomes, and no visibility gain is promised, since the samples and outside factors limit what any single intervention can show.

Methodology

Questions This Section Answers

• How are Sesame 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 399 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 5497 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:

July 2026 measured a different set of companies, so it is excluded from this comparison. August 2026 measured a different set of companies, so it is excluded from this comparison. September 2026 measured a different set of companies, so it is excluded from this comparison.

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

The monthly samples differ, so month-to-month comparisons show what was recorded, not why it changed. Recommendation requires both endorsement and positive wording, and top-three and first-place counts sit inside recommendations rather than adding to them. Answers can name several companies, so counts cannot be summed into distinct answers.

Cited pages and disputed claims still need independent verification, and citation appearances across the wider capture differ from benchmark answer counts. Sentiment describes answer wording, not customer satisfaction.

Our first three priorities

First, verify the pricing claims against current primary sources and resolve which service and date each figure belongs to before any correction is proposed. Second, brief the highest-citation company pages, starting with the urgent-care and doctor's-note complaint pages, using approved facts and real buyer questions.

Third, review the selected third-party pages for current accuracy and prepare evidence-backed requests only where a genuine error is confirmed, recording no-change decisions alongside them. Throughout, keep placement depth and recommendation rate as separate measures so progress is read honestly.