DISA Global Solutions Appears in 4.5% of AI Answers. Here's Our Five-Step Plan to Improve Its AI Visibility.

Mark HuntleyBy Mark HuntleyFounder and CEO
10 minutes read

Key Takeaways

  • DISA appears in 4.55% of benchmark answers, but recommendations are much lower at 1.05%.
  • Mentions are mostly neutral, suggesting the company is named more often than it is endorsed.
  • The strongest opportunities are the company pages and third-party sources already cited in answers.
  • Buyer questions center on trust, employer choice, and background verification, which can guide new content briefs.

Answer Capsule

In November 2026, DISA Global Solutions appeared in 13 of 286 AI answers included in the Background Checks benchmark (4.55%). It received 3 recommendations (1.05%), with 1 Top 3 placement and 1 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 DISA Global Solutions mentions, recommendations and placements compare with its Background Checks peers?

DISA Global Solutions recorded 13 mentions (4.55%) and 3 recommendations (1.05%) across 286 answers included in the benchmark. Top 3 placements numbered 1; first-place recommendations numbered 1. Framing among its mentions was 3 positive, 10 neutral and 0 negative.

Rank

Company

Mentions

Mention rate

Recommendations

Recommendation rate

Top 3

Top 3 rate

#1

#1 rate

1

Checkr

279

97.55%

204

71.33%

169

59.09%

135

47.20%

2

GoodHire

229

80.07%

160

55.94%

116

40.56%

44

15.38%

3

HireRight

186

65.03%

117

40.91%

79

27.62%

24

8.39%

4

First Advantage

121

42.31%

70

24.48%

45

15.73%

17

5.94%

5

Accurate Background

116

40.56%

38

13.29%

19

6.64%

13

4.55%

6

Certn

18

6.29%

7

2.45%

3

1.05%

2

0.70%

7

Justifacts

6

2.10%

4

1.40%

3

1.05%

1

0.35%

8

DISA Global Solutions

13

4.55%

3

1.05%

1

0.35%

1

0.35%

9

ClearStar

3

1.05%

2

0.70%

2

0.70%

1

0.35%

10

PreCheck

2

0.70%

1

0.35%

1

0.35%

1

0.35%

11

Asurint

1

0.35%

0

0.00%

0

0.00%

0

0.00%

11

InfoMart

3

1.05%

0

0.00%

0

0.00%

0

0.00%

11

SecurTest

0

0.00%

0

0.00%

0

0.00%

0

0.00%

11

Universal Background Screening

1

0.35%

0

0.00%

0

0.00%

0

0.00%

11

Verified Credentials

2

0.70%

0

0.00%

0

0.00%

0

0.00%

DISA Global Solutions appears in a small share of the benchmark answers, and affirmative recommendations are rarer still. Its mentions lean neutral rather than negative, which suggests the company is being named in passing more often than it is being put forward as a preferred choice. That gap between being seen and being chosen is the practical issue.

The leaders sit far ahead on both mentions and recommendations, so DISA is competing for attention against firms that answers treat as default options. The useful first move is a scorecard that separates where DISA is named from where it is actually endorsed, so effort goes toward the answers that could plausibly shift.

What changed since last month

Questions This Section Answers

  • How did DISA Global Solutions counts and coverage differ across the available monthly snapshots?

Metric

Jul 2026

Aug 2026

Sep 2026

Oct 2026

Nov 2026

Mentions

27 / 503 (5.37%)

22 / 539 (4.08%)

20 / 513 (3.90%)

18 / 504 (3.57%)

13 / 286 (4.55%)

Recommendations

16 / 503 (3.18%)

13 / 539 (2.41%)

12 / 513 (2.34%)

12 / 504 (2.38%)

3 / 286 (1.05%)

Top 3 recommendations

4 / 503 (0.80%)

3 / 539 (0.56%)

1 / 513 (0.19%)

1 / 504 (0.20%)

1 / 286 (0.35%)

#1 recommendations

1 / 503 (0.20%)

0 / 539 (0.00%)

0 / 513 (0.00%)

0 / 504 (0.00%)

1 / 286 (0.35%)

From October 2026 to November 2026, DISA Global Solutions' mention count fell from 18 to 13, while its recommendation count fell from 12 to 3. The recommendation rate was 2.38% and 1.05% respectively.

Across the monthly samples, mentions and recommendations both moved lower in the latest month, and the recommendation count fell more sharply than mentions. The samples differ in size between months, so the two months are not directly comparable and the movement should be read as a recorded change rather than a trend.

Because the answer sets are separate samples, nothing here shows why the counts moved. The sensible response is to keep the monthly record as a baseline and watch whether the pattern holds across comparable samples before treating it as a signal worth acting on.

AI recommendation trend

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

0%1%2%3%4%Jul 2026Aug 2026Sep 2026Oct 2026Nov 20263.18%Jul 2026: 3.18%2.41%Aug 2026: 2.41%2.34%Sep 2026: 2.34%2.38%Oct 2026: 2.38%1.05%Nov 2026: 1.05%
  • DISA Global Solutions1.05%

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 DISA Global Solutions?

AI answers contained 0 conflicting claims about DISA Global Solutions: 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 DISA Global Solutions 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 is not proof that every statement about the company is accurate; it means nothing in this sample warranted a correction review.

The proposed work is a visibility comparison that sets DISA's placement against the leading firms, paired with a short list of claims worth verifying against primary sources. That gives the client a clear sense of where attention is thin and which statements deserve a second look before any outreach.

Step 2. Make the website consistent

Questions This Section Answers

  • Which recorded claim differences about DISA Global Solutions 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 brief against yet. Rather than guess at page changes, the proposed work is to confirm the company's basic facts first: legal entity, service names and dates, checked against DISA's own materials.

Once those facts are settled, page briefs can be drafted only for topics the evidence actually supports. Any apparent conflict should be resolved by checking the entity, date and source before deciding whether a correction is warranted, since differing dates alone can explain an apparent mismatch.

Step 3. Align the external narrative

Questions This Section Answers

  • Which cited third-party pages are relevant to fact review for DISA Global Solutions?

Cited page selected for review

Citation appearances in all captured answers

Answers mentioning the company

Conflicting claims linked to this page

https://expertinsights.com/compliance/the-top-background-check-solutions

9

1

0

https://fadv.com/solutions/pre-employment-drug-screening/

1

1

0

https://questdiagnostics.com/business-solutions/employers/drug-screening

1

1

0

https://questdiagnostics.com/business-solutions/employers/drug-screening/employer-solutions-portal

1

1

0

https://goodhire.com/resources/articles/best-background-check-services/

64

5

0

https://accusourcehr.com/blog/best-background-check-companies

30

5

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 include industry roundups and drug-screening resources, several of which appear repeatedly across captured answers. Citation frequency shows where answers are drawing from, not that those pages influenced a recommendation or contain errors, so each needs a current read before any request is made.

The proposed work is a source-by-source review that records what each page currently says about DISA, flags anything inaccurate, and prepares evidence-backed outreach only where a correction is justified. Pages that already describe the company fairly would be left alone.

Step 4. Optimize priority company pages

Questions This Section Answers

  • Which observed DISA Global Solutions 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://disa.com/news/best-background-check-companies-for-employers-2026/

11

8

1

https://disa.com/background-checks/fcra-compliance/

2

2

0

https://disa.com/

1

1

0

The company's website was cited 14 times across 3 distinct page addresses; the table shows 3 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.

DISA's own pages were cited across a small set of addresses, with one employer-focused article drawing most of the attention. That article is the natural starting point for a page brief, since it already appears in answers and can be checked against approved facts rather than rebuilt from scratch.

The proposed work is to brief priority pages using confirmed company facts and the questions buyers actually ask, then review each page for accuracy and clarity. Pages with no recorded citations should be assessed on their own merits before any rewrite is proposed.

Step 5. Publish around buyer questions

Questions This Section Answers

  • Which observed buyer questions support content briefs for DISA Global Solutions?

Question observed in the research

Benchmark answers to this question

Company mentions

Company recommendations

What is the most trusted background check?

5

1

1

employment background check companies

2

1

1

employment screening services

1

1

1

Which company is best for background verification?

5

2

0

Who do most employers use for background checks?

5

2

0

What is the best software for background checks?

5

1

0

employee background check service

1

1

0

employee drug testing companies

1

1

0

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

The recorded questions cluster around trust, employer choice and drug testing, with DISA appearing in several but recommended in only a few. The trust and verification questions are where the company is mentioned without endorsement, which makes them the most useful place to look for content that answers the question directly.

The proposed work is to check whether DISA already covers these questions on its site, then draft briefs for the gaps. These are recorded answer observations, not search volume, so the briefs should be treated as content candidates rather than confirmed demand.

Our 90-day action plan

Questions This Section Answers

  • How should the agency sequence the proposed DISA Global Solutions 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 a clear evidence requirement. Nothing is corrected or requested until that groundwork is in place.

The second month produces approved page briefs and any publisher requests that survive verification, with confirmed edits, no-change decisions and responses recorded separately. The third month adds buyer-question content briefs and a fresh scorecard. Completed work will be reported separately from measured answer outcomes, since visibility can move for reasons outside the agency's control.

Methodology

Questions This Section Answers

  • How are DISA Global Solutions 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 286 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 4999 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

503

6

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

Aug 2026

539

6

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

Sep 2026

513

6

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

Oct 2026

504

6

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

Nov 2026

286

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 counts require both an endorsement and positive wording, and top placements sit inside those recommendations rather than adding to them. An answer can name several companies, so company counts do not sum to distinct answers.

Monthly samples differ in size and composition, so comparisons describe recorded movement without explaining it. Cited pages and any disputed claims still need independent verification before action.

Our first three priorities

First, build the baseline scorecard and fact-review register so DISA can see where it is named versus genuinely endorsed, and which statements about the company deserve verification. This is groundwork, and it keeps later outreach tied to evidence rather than assumption.

Second, brief the employer-focused article that answers already cite, using approved facts and the trust and verification questions buyers ask. Third, review the frequently cited industry pages and prepare publisher requests only where a current read shows something inaccurate. Each step depends on the one before it, and none guarantees a change in 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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