Sesame AI Market Strategy report — ED Treatment Pills

Answer Capsule

Sesame has one of the strongest AI pricing-layer positions in this ED-treatment benchmark, but that visibility is still neutral rather than recommendation-led. In the May 2026 dataset, Sesame surfaced repeatedly through telehealth affordability and healthcare-marketplace pricing prompts, giving it a broad evaluation-stage footprint. Its clearest win is affordable-care and telehealth pricing visibility. Its clearest weakness is that those appearances do not convert into valid recommendation credit. The biggest opportunity is to turn Sesame’s broad affordability retrieval into recommendation-ready comparison, trust, and decision-stage coverage.

Want this analysis for your company? CiteWorks Studio produces AI Market Strategy reports showing where your brand appears, disappears, or gets recommended across ChatGPT, Gemini, Copilot, Perplexity, Google AI Mode, and Google AI Overviews. Request an AI Visibility Audit

Who This Report Is For

This report is for CMOs, growth leaders, founders, investor relations teams, agency partners, and communications teams tracking how AI systems shape buyer choice in telehealth, affordable-care, and ED-treatment-adjacent categories.

Report Card

• Report type: AI Market Strategy report
• Target company: Sesame
• Category / market studied: ED treatment pills and adjacent telehealth pricing / affordability evaluation
• Reporting month: May 2026
• AI platforms tracked: Gemini-led extraction dataset
• Public high-intent clusters: 3 public clusters referenced in the benchmark; the populated measured cluster here is pricing / affordability evaluation
• AI observations analyzed: 21
• Competitors tracked: Hims, BlueChew, Lemonaid Health, LifeMD, Maximus Tribe, Optum Perks, Ro, Rugiet Men, ZipHealth

Executive Summary

Sesame is one of the most visible brands in this public benchmark slice. Across the 21 observed pricing and evaluation prompts, Sesame appeared in 6 observations, giving it a 28.57% raw mention presence rate. All of those appearances were neutral factual or pricing references rather than positive recommendations.

That matters because Sesame’s visibility is not centered on narrow ED-product prompts alone. It is tied to broader telehealth affordability, online doctor visit costs, Costco-linked healthcare programs, and healthcare-marketplace pricing explanations. That gives Sesame a wider evaluation-stage footprint than some more category-specific brands.

The strongest cluster for Sesame is the pricing layer itself. Among the visible brands, Ro had the highest raw presence overall, Sesame was second, and BlueChew held the clearest ED-specific pricing footprint. Sesame’s advantage is broader affordable-care framing rather than specialist ED-specific concentration.

The weakness is recommendation conversion. No tracked brand in this packet earned valid recommendation credit, top-three recommendation coverage, or modeled captured recommendation value. Sesame is highly present, but not being advanced into shortlist-quality treatment.

The clearest platform signal is that Sesame is retrievable across the Gemini-led pricing prompts in the dataset. The clearest gap is that affordability visibility has not turned into trust, comparison, or rank-one recommendation ownership.

What Sesame Is Winning

Sesame’s clearest win is broad telehealth-affordability visibility.

The benchmark surfaced Sesame in prompts such as “How much does an online doctor visit cost?”, “How much does a telehealth doctor visit cost?”, “How much does Sesame Care cost?”, “What is the Costco Ozempic program?”, “What is the Sesame program at Costco?”, and “What is the Costco $179 3 month subscription weight loss program?” That repeated presence matters because it places Sesame inside practical buyer-feasibility and affordable-access moments.

Sesame also benefits from a marketplace model that AI systems can summarize easily: healthcare access, transparent pricing, telehealth visit costs, and partnership-led program framing. The public benchmark explicitly points to that explainability as part of Sesame’s visibility advantage.

Where Sesame Has the Clearest AI Visibility Gaps

The clearest gap is recommendation conversion. Sesame is present, but not preferred. Its appearances are neutral affordability and pricing explanations rather than positive shortlist treatment, which means visibility without shortlist control.

The second gap is ED-specific concentration. Sesame is visible through broad telehealth and affordable-care framing, but the packet does not show the same degree of direct ED-specific pricing concentration that BlueChew has. That widens Sesame’s visibility but can weaken specialist-category ownership.

The third gap is decision-stage advancement. Sesame is already in the answer set when users ask affordability and care-access questions. The next challenge is becoming the recommended option when users move from “What does this cost?” to “Which option should I choose?”

Biggest Opportunity

The biggest opportunity is to convert Sesame’s broad affordability retrieval into recommendation-ready comparison and trust visibility.

Sesame already has repeated presence in commercial-intent prompts. The next step is to make the public evidence layer easier for AI systems to use when they need to compare providers, explain care-model tradeoffs, clarify service boundaries, and contextualize safety, legitimacy, and fit. That is how Sesame moves from neutral affordability presence to recommendation-stage inclusion.

Prompt Evidence

**Gemini / Pricing ** Prompt: **How much does an online doctor visit cost? ** Result: Sesame appeared through general telehealth-affordability framing and marketplace-style pricing explanation.

**Gemini / Pricing ** Prompt: **How much does a telehealth doctor visit cost? ** Result: Sesame surfaced in low-cost telehealth visit framing, reinforcing affordable-care visibility.

**Gemini / Pricing ** Prompt: **How much does Sesame Care cost? ** Result: Sesame appeared through marketplace pricing logic rather than a flat-fee model.

**Gemini / Pricing ** Prompt: **What is the Sesame program at Costco? ** Result: Sesame surfaced through partnership-based healthcare-access explanation, extending its affordability footprint beyond direct telehealth visit pricing.

What CiteWorks Studio Would Do Next

**Phase 1: AI Market Discovery Audit ** Map the full set of telehealth, affordability, ED-adjacent, comparison, subscription, and trust prompts where Sesame appears as a reference but not a recommendation. Prioritize the prompts closest to buyer choice.

**Phase 2: Recommendation Readiness Plan ** Separate neutral affordability visibility from recommendation quality. The goal is to preserve Sesame’s retrieval strength while improving whether AI systems can justify advancing it as a shortlist option.

**Phase 3: Owned Answer Layer Buildout ** Build comparison-ready pages around pricing structure, service boundaries, care workflow, marketplace model, partnership logic, and provider differences so AI systems have clearer material to synthesize.

**Phase 4: Citation / Authority Layer Development ** Strengthen the public source layer around trust, telehealth legitimacy, pricing transparency, service scope, and decision-stage comparisons so Sesame is easier to summarize safely and persuasively.

**Phase 5: Monthly AI Visibility and Recommendation Tracking ** Track whether Sesame moves from strong neutral affordability presence into broader comparison inclusion, top-three recommendation treatment, and stronger platform-level recommendation conversion.

Why This Matters

ED treatment and telehealth are becoming AI-assisted evaluation categories. Buyers are using AI systems to compress pricing, access, and provider choice into a single answer flow. In that environment, repeated affordability visibility is valuable because it shapes shortlist familiarity before explicit recommendation language appears.

Sesame already has a meaningful foothold in those high-intent moments. That makes this a more advanced problem than simple visibility. The next move is targeted correction of the prompt, page, and citation layers that help AI systems explain not just what Sesame costs or how it works, but why it should be considered.

Core Metrics

• Mentions: 6
• Valid recommendations: 0
• Top 3 recommendation count: 0
• Rank #1 recommendation count: 0
• Average recommended rank: N/A
• Positive mentions: 0
• Neutral mentions: 6
• Negative mentions: 0
• Raw mention presence rate: 28.57%
• Valid recommendation coverage: 0%
• Top 3 recommendation rate: 0%
• Rank #1 recommendation rate: 0%

Sentiment Score

Sentiment Score = (positive mentions × 1 + neutral mentions × 0 + negative mentions × -1) / total mentions

This matters because unclassified mention totals are misleading. Share of voice alone is a diagnostic metric, not a business KPI. A positive recommendation, a neutral pricing reference, and a displaced comparison mention are not equal, and treating them as equal inflates performance. Presence must be separated from recommendation quality.

For Sesame, the sentiment score is 0.00 because all observed mentions were neutral. That does not mean Sesame is weakly positioned. It means the brand is currently being used by AI systems as an affordable-care and telehealth reference rather than as a positively advanced recommendation. That distinction is the core strategic signal in this packet.

Sentiment by Platform

Methodology Note

This is a company-specific public report evaluating Sesame against a fixed competitor set in the May 2026 ED-treatment pricing benchmark. QA note: some downstream files contain inherited or stale category labels, so this report uses the actual prompt text, company universe, report title, and benchmark framing as the safer source of truth. This is an independent public analysis by CiteWorks Studio / LLM Authority Index. It is not affiliated with, endorsed by, or sponsored by Sesame unless explicitly stated. This report is not medical advice.

Methodology

• Report orientation. This is a one-company public report focused on Sesame relative to the fixed competitor set in the uploaded ED-treatment benchmark.
• Reporting window. The public benchmark reflects the May 2026 reporting window; the structured dataset was extracted on May 20, 2026.
• Platforms tracked. The observed dataset here is Gemini-led. The public benchmark explicitly describes this as a Gemini-oriented extraction dataset.
• Observation count. The structured pricing dataset contains 21 extracted pricing and evaluation observations.
• Competitor universe. The measured ten-brand universe is Hims, BlueChew, Lemonaid Health, LifeMD, Maximus Tribe, Optum Perks, Ro, Rugiet Men, Sesame, and ZipHealth.
• Public clusters used. The public benchmark references three clusters, but the populated structured cluster in this dataset is the pricing / affordability / subscription evaluation layer. Some inherited labels in downstream packets were normalized using prompt text and benchmark framing.
• Stage 0 role. Stage 0 is the extraction and normalization layer recording prompt text, platform, cluster, buyer stage, framing, sentiment, recommendation flags, and company presence.
• Definition of a mention. A brand counts as present when it appears in an AI-generated answer, including neutral factual references and pricing explanations.
• Definition of a valid recommendation. A valid recommendation requires positive shortlist-quality recommendation framing. Neutral factual references do not qualify. In this packet, no brand received valid recommendation credit.
• Limitations. This is a point-in-time, pricing-oriented benchmark, not a full market census. It is narrow in prompt type, limited in observation count, and concentrated in one platform, so findings should be interpreted as directional rather than definitive.