Body Details Appears in 0.0% 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

  • Body Details had 0 mentions and 0 recommendations in the November 2026 benchmark, with no Top 3 or first-place placements.
  • The monthly record shows one earlier mention in July 2026, but all later samples stayed at zero.
  • The first priority is to verify core company facts and define what Body Details should be known for in this category.
  • The 90-day plan focuses on baseline measurement, source and page review, and buyer-question content briefs.

Answer Capsule

In November 2026, Body Details appeared in 0 of 216 AI answers included in the Medical Spas benchmark (0.00%). 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 Body Details mentions, recommendations and placements compare with its Medical Spas peers?

Body Details recorded 0 mentions (0.00%) and 0 recommendations (0.00%) across 216 answers included in the benchmark. Top 3 placements numbered 0; first-place recommendations numbered 0. Framing among its mentions was 0 positive, 0 neutral and 0 negative.

Rank

Company

Mentions

Mention rate

Recommendations

Recommendation rate

Top 3

Top 3 rate

#1

#1 rate

1

LaserAway

173

80.09%

93

43.06%

77

35.65%

22

10.19%

2

Milan Laser Hair Removal

127

58.80%

77

35.65%

72

33.33%

53

24.54%

3

SEV Laser

64

29.63%

20

9.26%

18

8.33%

6

2.78%

4

SkinSpirit

17

7.87%

15

6.94%

14

6.48%

9

4.17%

5

Ideal Image

25

11.57%

10

4.63%

8

3.70%

1

0.46%

6

Skin Laundry

7

3.24%

7

3.24%

4

1.85%

0

0.00%

7

Ever/Body

5

2.31%

5

2.31%

3

1.39%

0

0.00%

8

Restore Hyper Wellness

5

2.31%

3

1.39%

1

0.46%

0

0.00%

8

Sono Bello

3

1.39%

3

1.39%

2

0.93%

0

0.00%

10

Body Details

0

0.00%

0

0.00%

0

0.00%

0

0.00%

Body Details does not appear in the answers captured for this benchmark, so there is no recommendation record to improve yet. The companies that do appear show that AI answers concentrate on a small set of names, and the leaders are endorsed repeatedly rather than occasionally. That is the competitive reality the client is working against.

Before setting a performance target, the useful step is to establish what the company wants to be known for in these answers and which questions it should plausibly appear in. A target built on an empty baseline would be arbitrary. We would propose a baseline scorecard that records where the company stands and what a realistic first appearance looks like.

The absence of recorded mentions is an observation about this sample, not proof that the company is invisible everywhere. It does mean the current answer set gives the client nothing to defend and nothing to build on until presence is established.

What changed since last month

Questions This Section Answers

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

Metric

Jul 2026

Aug 2026

Sep 2026

Oct 2026

Nov 2026

Mentions

1 / 295 (0.34%)

0 / 149 (0.00%)

0 / 325 (0.00%)

0 / 278 (0.00%)

0 / 216 (0.00%)

Recommendations

0 / 295 (0.00%)

0 / 149 (0.00%)

0 / 325 (0.00%)

0 / 278 (0.00%)

0 / 216 (0.00%)

Top 3 recommendations

0 / 295 (0.00%)

0 / 149 (0.00%)

0 / 325 (0.00%)

0 / 278 (0.00%)

0 / 216 (0.00%)

#1 recommendations

0 / 295 (0.00%)

0 / 149 (0.00%)

0 / 325 (0.00%)

0 / 278 (0.00%)

0 / 216 (0.00%)

From October 2026 to November 2026, Body Details' mention count stayed at 0, while its recommendation count stayed at 0. The recommendation rate was 0.00% and 0.00% respectively.

Across the monthly samples, the company's recorded mentions and recommendations remain at zero, with a single earlier mention the only departure from that pattern. The counts move between months, but the company's own position does not change in any recorded month.

The samples differ in size from month to month, so the monthly figures are separate selected samples rather than a continuous trend line. A different answer total does not explain why the company's counts stayed where they did, and we should not read one into the other.

The practical reading is that nothing in the recorded history suggests momentum the client could amplify. Any future movement would need to be judged against a fresh sample, not against this flat record.

AI recommendation trend

Observed recommendation rate (%), Jul 2026 to Nov 2026

0%0.03%0.05%0.08%0.1%Jul 2026Aug 2026Sep 2026Oct 2026Nov 20260.00%Jul 2026: 0.00%0.00%Aug 2026: 0.00%0.00%Sep 2026: 0.00%0.00%Oct 2026: 0.00%0.00%Nov 2026: 0.00%
  • Body Details0.00%

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

/ Take the next step

Want to Understand Your AI Citation Footprint?

We start every engagement with a full audit of how AI systems reference your brand today.

Measurable, Repeatable Programme

Build a durable foundation of credible citations that compounds over time and continues to influence AI answers as new queries emerge

Citation Architecture Review

Identify which high-authority community sources are and aren't working in your favour across AI platforms.

AI Visibility Audit

Understand exactly how LLMs are referencing your brand today and which sources are shaping those answers.

Step 1. Measure

Questions This Section Answers

  • What should the agency measure and verify first for Body Details?

AI answers contained 0 conflicting claims about Body Details: 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 Body Details, there is nothing to fact-check in this cycle, and that is itself useful information. It means the first measurement task is not correction but orientation: understanding which questions the benchmark asks and which companies answer them.

We would propose a visibility comparison that sets the company's position beside the names that recur in these answers, so the client can see the shape of the field rather than a single zero. A claims-to-check list would start empty and be maintained as new answers are captured.

This keeps effort directed at building presence rather than defending against claims that have not appeared.

Step 2. Make the website consistent

Questions This Section Answers

  • Which recorded claim differences about Body Details 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 topics were supplied for this cycle, so there is no claim to verify and no page to correct. We would not manufacture a correction queue from an absence of evidence.

What we can propose is a verification step: confirm the company's core service facts, locations and positioning against primary sources, then hold those approved facts ready for page briefs. If a disputed claim appears in a later sample, the same approved facts become the reference for deciding whether a correction is warranted.

Any correction would require current primary-source verification first, and ambiguous company matches would stay outside the queue.

Step 3. Align the external narrative

Questions This Section Answers

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

No results are available for this part of the analysis.

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.

No external pages were selected for review in this cycle, so there is no publisher request to make and no source to approach. We would not treat that gap as evidence that publishers have ignored the company.

A sensible next step is a scoped source review: identify the pages that shape answers in this category, check their current contents, and note where the company is absent or described incompletely. Only after that check would we consider evidence-backed requests.

A citation does not establish that a source influenced an answer, and a page linked to a claim is not necessarily its cause. Requests would be framed accordingly.

Step 4. Optimize priority company pages

Questions This Section Answers

  • Which observed Body Details 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.

The company's website was not cited in the captured answers, and no pages were selected for review. That gives us a clean starting point rather than a list of underperforming pages.

We would propose page briefs built on approved facts and the questions buyers actually ask, using the supplied page list once it is confirmed. Each brief would state the audience question it answers and the evidence behind each claim, so writers are not guessing.

A cited page is not automatically a page that accompanied a recommendation, so page work would be judged on its own terms rather than tied to a recommendation outcome.

Step 5. Publish around buyer questions

Questions This Section Answers

  • Which observed buyer questions support content briefs for Body Details?

No results are available for this part of the analysis.

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

No buyer questions were recorded for this cycle, so content briefs cannot yet be tied to observed questions. We would not invent demand from an empty list.

The proposal is to build a buyer-question set from the questions that appear in the benchmark and from the client's own sales conversations, then check existing coverage before commissioning anything new. Where coverage exists, the brief is a refresh; where it does not, the brief is a new page.

These would be recorded answer observations, not search volume or proof of missing website content.

Our 90-day action plan

Questions This Section Answers

  • How should the agency sequence the proposed Body Details 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 produces a baseline scorecard, a scoped fact-review register and a page and source shortlist, each with an owner and a review status. This is groundwork, and its completion is judged by records being assigned and reviewed, not by visibility movement.

The second month turns approved facts into page briefs and, where justified, publisher requests, with confirmed edits, no-change decisions and responses recorded separately. The third month adds buyer-content briefs and a fresh scorecard.

Delivered work would be reported separately from measured answer outcomes, and no gain would be promised from completing the plan.

Methodology

Questions This Section Answers

  • How are Body Details 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 216 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 5691 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

Jul 2026

295

6

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

Aug 2026

149

6

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

Sep 2026

325

6

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

Oct 2026

278

6

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

Nov 2026

216

6

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

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

The answers were captured for a single reporting month, and the exact collection dates 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 mention several companies, so company counts cannot be summed into a number of distinct answers. The monthly samples differ, and comparisons show what was recorded, not why it changed. Cited pages and disputed claims still need independent verification.

Our first three priorities

The first priority is to confirm the company's core facts against primary sources and agree what it should be known for in this category. Without that reference, page work and any later correction decisions have no anchor.

The second is a scoped source and page review, using the supplied lists once confirmed, to see where the company is absent and which pages are worth a brief. The third is a buyer-question set drawn from observed questions and sales conversations, checked against existing coverage before any new page is proposed.

Together these give the client a defensible starting position and a clear basis for judging later samples.

/ Learn More

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.

Archived editions

Browse earlier published research snapshots for this article. The latest edition remains at the current URL.

VIEW ALL CASE STUDIESREQUEST AN AI VISIBILITY AUDIT