Semperis Appears in 1.4% 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

  • Semperis had low mention volume in the benchmark, but most appearances were affirmative recommendations.
  • Monthly results were uneven, with mentions and recommendations returning in November after a zero month in October.
  • No conflicting claims were recorded in the captured answers, so the first step is measurement and verification rather than correction.
  • The plan focuses on third-party ITDR sources, priority company pages, and buyer questions that already surface in answers.

Answer Capsule

In November 2026, Semperis appeared in 3 of 213 AI answers included in the Identity and Access Management benchmark (1.41%). It received 2 recommendations (0.94%), with 1 Top 3 placement 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 Semperis mentions, recommendations and placements compare with its Identity and Access Management peers?

Semperis recorded 3 mentions (1.41%) and 2 recommendations (0.94%) across 213 answers included in the benchmark. Top 3 placements numbered 1; first-place recommendations numbered 0. Framing among its mentions was 2 positive, 1 neutral and 0 negative.

Rank

Company

Mentions

Mention rate

Recommendations

Recommendation rate

Top 3

Top 3 rate

#1

#1 rate

1

Okta

206

96.71%

147

69.01%

117

54.93%

62

29.11%

2

SailPoint

172

80.75%

135

63.38%

57

26.76%

14

6.57%

3

BeyondTrust

104

48.83%

81

38.03%

31

14.55%

1

0.47%

4

RSA Security

4

1.88%

2

0.94%

0

0.00%

0

0.00%

4

Semperis

3

1.41%

2

0.94%

1

0.47%

0

0.00%

6

Axiad

0

0.00%

0

0.00%

0

0.00%

0

0.00%

6

CLEAR (by Alclear, LLC)

1

0.47%

0

0.00%

0

0.00%

0

0.00%

6

Cross Match Technologies

0

0.00%

0

0.00%

0

0.00%

0

0.00%

6

Keyfactor

1

0.47%

0

0.00%

0

0.00%

0

0.00%

6

Telos Corporation

0

0.00%

0

0.00%

0

0.00%

0

0.00%

Semperis appears in a small share of the benchmark answers, yet most of those appearances carry an affirmative recommendation. That gap between reach and endorsement is the useful signal: when the company does surface, the answer tends to back it rather than merely name it. The single shortlist placement sits inside the recommendation count, and no answer placed Semperis first.

The practical reading is that Semperis is rarely in the room but is treated credibly when it is. The dominant vendors occupy most answers, so the near-term opportunity is broader presence rather than stronger praise. We would build a scorecard that tracks reach and endorsement separately so the client can see which one moves.

What changed since last month

Questions This Section Answers

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

Metric

Jul 2026

Aug 2026

Sep 2026

Oct 2026

Nov 2026

Mentions

3 / 191 (1.57%)

2 / 243 (0.82%)

1 / 255 (0.39%)

0 / 219 (0.00%)

3 / 213 (1.41%)

Recommendations

3 / 191 (1.57%)

0 / 243 (0.00%)

1 / 255 (0.39%)

0 / 219 (0.00%)

2 / 213 (0.94%)

Top 3 recommendations

1 / 191 (0.52%)

0 / 243 (0.00%)

1 / 255 (0.39%)

0 / 219 (0.00%)

1 / 213 (0.47%)

#1 recommendations

0 / 191 (0.00%)

0 / 243 (0.00%)

0 / 255 (0.00%)

0 / 219 (0.00%)

0 / 213 (0.00%)

From October 2026 to November 2026, Semperis' mention count rose from 0 to 3, while its recommendation count rose from 0 to 2. The recommendation rate was 0.00% and 0.94% respectively.

The monthly record is uneven rather than directional. Mentions and recommendations were present in some months, absent in others, and returned in the latest sample. First-place placements stayed at zero throughout, so the company has not yet been the leading answer in any recorded month.

Because each month draws a different set of answers, the swings should be read as separate observations, not as a trend line. We would keep the monthly scorecard in place and resist drawing conclusions from any single month until several comparable samples point the same way.

AI recommendation trend

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

0%0.5%1%1.5%2%Jul 2026Aug 2026Sep 2026Oct 2026Nov 20261.57%Jul 2026: 1.57%0.00%Aug 2026: 0.00%0.39%Sep 2026: 0.39%0.00%Oct 2026: 0.00%0.94%Nov 2026: 0.94%
  • Semperis0.94%

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 Semperis?

AI answers contained 0 conflicting claims about Semperis: 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.

No conflicting claims about Semperis were recorded in the captured answers, so there is no immediate fact-check queue to work through. That is a clean starting position, but it also means the client has little evidence about how its own facts are being represented when it does appear.

We would pair a visibility comparison against the leading vendors with a scoped list of claims worth verifying, each with an owner and an evidence requirement. This gives the client a defensible basis for deciding where to spend effort rather than reacting to isolated answer text.

Step 2. Make the website consistent

Questions This Section Answers

  • Which recorded claim differences about Semperis 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.

This part of the analysis returned no results, so there are no disputed topics to resolve for Semperis at present. The absence is not a clean bill of health; it simply means nothing was flagged in this sample.

We would still prepare conditional page briefs that state approved facts plainly and resolve entity, date and source questions before any correction is considered. Different dates can make two statements look contradictory when they are not, so verification comes first and edits follow only when the primary source supports them.

Step 3. Align the external narrative

Questions This Section Answers

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

Cited page selected for review

Citation appearances in all captured answers

Answers mentioning the company

Conflicting claims linked to this page

https://beyondtrust.com/solutions/itdr

2

2

0

https://gartner.com/reviews/market/identity-threat-detection-and-response-itdr

2

2

0

https://microsoft.com/en-us/security/business/solutions/identity-threat-detection-response

2

2

0

https://paloaltonetworks.com/cyberpedia/identity-threat-detection-and-response-itdr

2

2

0

https://vectra.ai/products/itdr

2

2

0

https://aembit.io/blog/5-security-considerations-for-managing-ai-agents-and-their-identities/

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 selected for review are mostly third-party ITDR explainers and review sites, with no conflicting claims linked to any of them. Citation alone does not show that a page shaped an answer, and the current contents of each page still need checking before any request is made.

We would review the most relevant sources directly, confirm what they currently say about Semperis, and prepare evidence-backed requests only where a genuine gap exists. Publishers control their own pages, so the work is a well-founded ask, not an assumption of error.

Step 4. Optimize priority company pages

Questions This Section Answers

  • Which observed Semperis website pages should receive review briefs?

Company website page

Citation appearances in all captured answers

Benchmark answers citing the page

Recommending answers citing the page

https://semperis.com/purple-knight/

3

0

0

https://semperis.com/

2

0

0

https://semperis.com/blog/what-organizations-want-from-expert-itdr-solutions/

2

2

2

https://semperis.com/company/

2

0

0

https://semperis.com/official-information-about-semperis-for-ai/

2

0

0

https://semperis.com/es/purple-knight/videos/?wchannelid=fhtn5lw49b&wmediaid=9n7you31d8

1

0

0

https://semperis.com/press-release/purple-knight-delivers-security-assessments-for-gcc/

1

0

0

https://semperis.com/purple-knight/request-form/

1

0

0

The company's website was cited 16 times across 10 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.

The company site was cited across a spread of pages, but only one of them, the blog post on what organizations want from expert ITDR solutions, appeared in answers that also recommended Semperis. The rest, including the Purple Knight pages and the homepage, were cited without accompanying recommendations in the recorded answers.

We would write page briefs for the priority pages using approved facts and the questions buyers actually ask, then check existing coverage before proposing anything new. The aim is to make the pages that already attract citations clearer about what Semperis offers.

Step 5. Publish around buyer questions

Questions This Section Answers

  • Which observed buyer questions support content briefs for Semperis?

Question observed in the research

Benchmark answers to this question

Company mentions

Company recommendations

itdr solutions

1

1

1

itdr vendors

1

1

1

ai identity security

1

1

0

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

The recorded questions cluster around ITDR solutions, ITDR vendors and AI identity security. Semperis was mentioned and recommended in the first two, but appeared without a recommendation on the AI identity security question.

We would turn these into content briefs that answer the buyer question directly, with approved facts and clear positioning. Before commissioning new pages, we would check whether existing pages already cover the question well enough to be strengthened instead.

Our 90-day action plan

Questions This Section Answers

  • How should the agency sequence the proposed Semperis 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 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 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, so the client can see what was done without mistaking activity for a visibility gain.

Methodology

Questions This Section Answers

  • How are Semperis 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 213 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 5017 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

191

6

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

Aug 2026

243

6

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

Sep 2026

255

6

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

Oct 2026

219

6

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

Nov 2026

213

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 report covers a single month, and the exact dates the answers were collected have not been verified. Recommendation requires both an endorsement and positive wording, and shortlist 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.

Monthly samples differ, so comparisons show what changed, not why. Cited pages and any disputed claims still need independent verification before action.

Our first three priorities

First, confirm how Semperis is described on the third-party ITDR sources that answers cite, since those pages carry the most external weight and none currently shows a linked conflict. Second, strengthen the owned pages that attract citations but not recommendations, starting with the ITDR buyer-question content the site already publishes.

Third, keep the monthly scorecard running so reach and endorsement can be watched separately across comparable samples. Each step depends on verified facts and named owners, and none of it guarantees a change in answer visibility.

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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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