Propella Appears in 1.0% of AI Answers. Here's Our Five-Step Plan to Improve Its AI Visibility.

Mark HuntleyBy Mark HuntleyFounder and CEO
9 minutes read

Key Takeaways

  • Propella is mentioned in the benchmark but is never recommended, so the main issue is weak endorsement rather than complete absence.
  • There are no recorded conflicting claims for Propella, which makes primary-source verification the first priority.
  • No Propella website pages were cited in the captured answers, pointing to an owned-content gap rather than a correction need.
  • The recorded buyer questions center on U.S. e-bike makers and business status, which can guide future content briefs.

Answer Capsule

In November 2026, Propella appeared in 4 of 408 AI answers included in the Direct to Consumer Electric Bikes benchmark (0.98%). It received 0 recommendations (0.00%), with 0 Top 3 placements and 0 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 Propella mentions, recommendations and placements compare with its Direct to Consumer Electric Bikes peers?

Propella recorded 4 mentions (0.98%) and 0 recommendations (0.00%) across 408 answers included in the benchmark. Top 3 placements numbered 0; first-place recommendations numbered 0. Framing among its mentions was 0 positive, 4 neutral and 0 negative.

Rank

Company

Mentions

Mention rate

Recommendations

Recommendation rate

Top 3

Top 3 rate

#1

#1 rate

1

Sixthreezero

127

31.13%

43

10.54%

35

8.58%

25

6.13%

2

Ancheer

64

15.69%

19

4.66%

14

3.43%

13

3.19%

3

Blix

39

9.56%

5

1.23%

5

1.23%

5

1.23%

4

NAKTO

19

4.66%

4

0.98%

4

0.98%

4

0.98%

5

Ariel Rider

51

12.50%

3

0.74%

2

0.49%

2

0.49%

6

Biktrix

16

3.92%

2

0.49%

2

0.49%

2

0.49%

6

Luna Cycle

107

26.23%

2

0.49%

1

0.25%

1

0.25%

8

Propella

4

0.98%

0

0.00%

0

0.00%

0

0.00%

8

Surface604

7

1.72%

0

0.00%

0

0.00%

0

0.00%

Propella appears in the conversation but is never put forward as a choice. Its mentions are all neutral, so the brand registers as present without being endorsed. That gap between being seen and being selected is the real starting point, not the raw mention count.

Competitors show that affirmative endorsement is achievable in this category, and several earn it across a meaningful share of their appearances. Propella currently earns none. Because no recommendations were recorded, there is no baseline to protect, only one to build. We would treat this as a positioning problem to diagnose before setting any performance target, and avoid promising a specific lift in answer outcomes.

What changed since last month

Questions This Section Answers

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

Metric

Nov 2026

Mentions

4 / 408 (0.98%)

Recommendations

0 / 408 (0.00%)

Top 3 recommendations

0 / 408 (0.00%)

#1 recommendations

0 / 408 (0.00%)

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

There are insufficient usable monthly observations for a prior-period comparison.

This is the first verified month for Propella, so there is no prior period to compare against. The absence of an earlier reading is a gap in the record, not evidence of a decline or improvement. The current figures stand as a starting snapshot only.

Because the monthly samples differ and earlier months measured different company sets, any future movement should be read as a change in recorded results rather than a proven trend. We would establish a clean baseline now so later months have something honest to compare against.

AI recommendation trend

Observed recommendation rate (%), Nov 2026

  • Propellano change
    Nov 20260.00%

Separate selected monthly samples. Missing points are unavailable, not zero. Provider/prompt equivalence is unverified; rate movement does not demonstrate causation.

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Step 1. Measure

Questions This Section Answers

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

AI answers contained 0 conflicting claims about Propella: 0 critical, 0 high, 0 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.

With no conflicting claims recorded about Propella, the immediate fact-checking burden is light. That is useful, but it also means the visibility problem is unlikely to be a misinformation problem. The more valuable measurement is a structured comparison of how Propella appears against the brands that do earn endorsements.

We would build a visibility scorecard that tracks mentions, recommendations and framing side by side, plus a short list of claims worth verifying against primary sources. This gives the client a clear picture of where it stands and a defensible basis for deciding what to fix first.

Step 2. Make the website consistent

Questions This Section Answers

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

No results are available for this part of the analysis.

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.

No disputed claims surfaced for Propella this month, so there is nothing to correct yet. Rather than invent work, we would confirm the company's core facts against its own primary sources and hold any page brief until a real discrepancy appears. This keeps effort focused and avoids chasing problems that may not exist.

If a conflict does emerge later, the first step is to resolve the entity, date and source before deciding whether a correction is warranted. Different dates can explain an apparent conflict, so verification comes before any change to the website.

Step 3. Align the external narrative

Questions This Section Answers

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

Cited page selected for review

Citation appearances in all captured answers

Answers mentioning the company

Conflicting claims linked to this page

https://bloomberg.com/news/articles/2026-01-14/what-s-killing-off-all-the-e-bike-companies

2

1

0

https://intechopen.com/chapters/87225

2

1

0

https://reddit.com/r/ebikes/comments/1o1jopz/electric_bike_company_files_for_bankruptcyand/

2

1

0

https://batteryswapcabinet.com/top-10-e-bike-manufacturers-in-usa/

1

1

0

https://batteryswapstation.com/top-5-best-e-bike-manufacturers-in-the-us/

1

1

0

https://ctsnet.org/article-video/transfer-technique-femoro-jugular-veno-venous-ecmo-veno-pulmonary-ecmo-protekduo-cannula/

1

1

0

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 linked to Propella are a mixed set, including general manufacturer roundups and broader industry coverage. None carries a recorded conflicting claim, so there is no correction case to pursue. The useful work is reviewing what these pages actually say now and whether they represent the brand accurately.

We would check each page's current contents against primary sources before making any request. Where a page is accurate, no action is needed; where it is outdated or unclear, an evidence-backed note to the publisher may be appropriate. A citation alone does not show that a page influenced an answer.

Step 4. Optimize priority company pages

Questions This Section Answers

  • Which observed Propella website pages should receive review briefs?

No results are available for this part of the analysis.

The company's website was cited 0 times across 0 distinct page addresses; the table shows 0 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.

No Propella pages were cited in the captured answers, which points to an owned-content opportunity rather than a repair. The priority is to give the site clear, verifiable material that answers the questions buyers actually ask, using only approved facts.

We would draft page briefs built around confirmed product details and audience questions, then check existing coverage before proposing anything new. The goal is content that is accurate and easy for both buyers and answer engines to use, not a rewrite for its own sake.

Step 5. Publish around buyer questions

Questions This Section Answers

  • Which observed buyer questions support content briefs for Propella?

Question observed in the research

Benchmark answers to this question

Company mentions

Company recommendations

What companies make e-bikes in the US?

2

1

0

What ebike company went out of business?

1

1

0

classic ebike

1

1

0

protek duo

1

1

0

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

The recorded questions cluster around who makes e-bikes in the US and which companies have gone out of business. Propella appears in answers to these questions but is never recommended, which suggests the content around them is where perception is formed.

We would turn these observations into buyer-question briefs, checking existing pages first. These are recorded answer observations, not search volume or proof of missing content, so the briefs should be treated as hypotheses to validate against the site and primary sources.

Our 90-day action plan

Questions This Section Answers

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

Period

Deliverable

Completion criterion

Days 1–30

Baseline scorecard, scoped fact-review register and page/source shortlist

Each record has an owner, evidence requirement and review status

Days 31–60

Approved page briefs and justified publisher requests

Separate confirmed edits, no-change decisions, requests and responses

Days 61–90

Buyer-content briefs and a fresh scorecard

Record work completed separately from measured answer outcomes and comparability limits

The first month establishes the baseline: a visibility scorecard, a scoped fact-review list and a shortlist of pages and sources worth examining. Each record needs an owner, an evidence requirement and a review status so nothing rests on assumption.

The second month produces approved page briefs and any justified publisher requests, with confirmed edits, no-change decisions and responses kept separate. The third month adds buyer-content briefs and a fresh scorecard. Completed work is reported separately from measured answer outcomes, and no visibility gain is promised.

Methodology

Questions This Section Answers

  • How are Propella 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 408 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 798 captured answers and 5164 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:

Reporting month

Answers included

AI services covered

Services

Nov 2026

408

6

ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, Perplexity

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. Reliable results for October 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.

This report covers a single month, and the exact collection dates for the original answers are unverified. Rates rest on answers that met the benchmark's inclusion rules, and a company counts as recommended only when an answer endorses it with positive wording. Mentions and recommendations are separate measures, and an answer can cover several companies, so counts cannot be summed into distinct answers.

Monthly samples differ, and earlier months measured different company sets, so comparisons show what was recorded, not why it changed. Cited pages and disputed claims still need independent verification.

Our first three priorities

First, confirm Propella's core facts against primary sources so any later page or publisher work rests on verified information. Second, review the cited pages for current accuracy and decide, page by page, whether any evidence-backed request is warranted. Third, draft buyer-question briefs around the recorded questions, checking existing coverage before proposing new content.

These steps build a trustworthy foundation before any visibility target is set. They do not guarantee recommendations, but they reduce the risk of acting on unverified claims and give the client a clear, honest starting point for the months ahead.

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Understanding AI search visibility.

AI search experiences create answers by pulling information from many places online and summarizing it into a single response.

What Is AI Citation Intelligence?
AI citation intelligence is the process of measuring where AI platforms source their information and how frequently a brand is mentioned or referenced in AI-generated responses. Because LLMs synthesize across multiple sources, the sites and brands that appear repeatedly tend to influence how a topic or company is framed. This practice focuses on identifying which sources shape AI outputs and tracking brand visibility across different AI systems.
What Is Citation Architecture?
Citation architecture describes the set of sources that consistently inform how AI systems talk about a brand, product, or topic. LLMs draw from websites, articles, forums, and public discussion, and the sources they rely on most often become the backbone of their answers. Building strong citation architecture means ensuring that accurate, credible, high authority sources are the ones most likely to shape the way AI tools summarize and recommend a brand.
What Is Generative Engine Optimization?
Generative engine optimization (GEO) is the practice of improving the chances that AI systems use and cite your brand or content when generating answers. While traditional SEO is centered on ranking pages in search results, GEO focuses on how LLMs retrieve, interpret, and combine information when responding to a question. The objective is to strengthen the content and sources AI systems rely on, so your brand is treated as a trusted reference in AI responses.
What Is AI Share of Voice?
AI share of voice tracks how often a brand appears in AI-generated answers compared with competitors in the same category. It reflects visibility across AI platforms such as ChatGPT, Gemini, Claude, and Perplexity. Monitoring AI share of voice helps organizations see whether AI systems consistently include and recommend their brand for key queries or whether competitor brands are showing up more often.

About The Author

Mark Huntley

Mark Huntley

Founder and CEO

Mark Huntley, J.D. is founder of CiteWorks Studio, a strategic advisory focused on visibility, authority, and recommendation presence in AI-shaped search environments. His work centers on embedding-level GEO, vector optimization, and cosine gap engineering — helping brands align their digital presence with the retrieval systems that increasingly shape discovery, interpretation, and choice.

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