Of 60 answers

The most named companies in the study, and S&P 500 financials, with how many of their 60 AI answers named them.

Back to the study
AI visibility statementFinance AI Visibility Study 2026Issued October 14, 2026
Account holder
Statement period
October 9 to 14, 2026
when the AI assistants were asked
Account no.
1,151 of 2,219
its rank by overall score
Closing balance

Named in

0of 60, its tower is dark

by 0 of 4 assistants

Statement note

What this means for iCOUNTER

AI assistants do not name this fraud and identity company in any of the 60 answers, while they do mention others like SecurityScorecard and BitSight. Its overall score is 28 out of 100, ranking 1,151st of 2,219 companies. The website passes 4 of 14 technical checks for readiness with AI tools.

We asked 15 unbranded questions about its market, so no question mentioned iCOUNTER by name. AI assistants named it in none of the 60 answers. Counting only how often it is named, it ranks 334th of 574 in its sector. The typical financial technology and data company is named in 1.7% of answers.

Named fewer than three times in total. Read this as a sign that AI assistants rarely bring this company up, not as an exact comparison with its peers.

Overall score
28Overall score
  • Visibility0
  • Citations0
  • Sentiment50
  • Site quality87
  • AI readiness29
Summary of account
Named by AI in
0.0%
0 of 60 answers
Share of mentions
0.0%
of the mentions of it and its 5 peers
AI readiness
4/14
technical checks passed. Typical for its sector: 4
Rank in its sector
302 of 574
by overall score, where the typical financial technology and data company scores 29

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Line itemsSection 01

Every question, and where the company came up

iCOUNTER came up on 0 of 15 questions. Open any window to read that answer.

  1. 01Why isn’t my third-party risk management platform detecting emerging ecosystem threats?0/4
  2. 02How do real-time threat intelligence platforms compare to traditional static scorecard tools for third-party risk?0/4
  3. 03Best features to look for in ecosystem risk intelligence solutions?0/4
  4. 04What are the best approaches besides automated threat feeds for managing third-party cyber risk?0/4
  5. 05What’s the best way to operationalize ecosystem risk intelligence within an existing security stack?0/4
  6. 06What influences the cost of a third-party cyber threat intelligence platform in the United States?0/4
  7. 07What to do if my cyber risk intelligence dashboard is missing real-time threat data?0/4
  8. 08Alternatives to traditional cyber risk scorecards for monitoring supply chain security?0/4
  9. 09What are the differences between operator-led and fully automated cyber threat intelligence services?0/4
  10. 10Typical subscription pricing tiers for operator-led ecosystem threat intelligence tools?0/4
  11. 11How can proactive threat intelligence help prevent targeted cyber and fraud attacks on partners?0/4
  12. 12Do third-party cyber threat intelligence platforms provide enough ROI for midsize organizations?0/4
  13. 13What are the top third-party cyber threat intelligence platforms for large organizations?0/4
  14. 14Is investing in an enterprise-level cyber risk intelligence solution worth it for growing security teams?0/4
  15. 15Where can enterprise security teams find up-to-date threat intelligence for extended ecosystems?0/4
Total namedOpenAI: 0/15Claude: 0/15Perplexity: 0/15Gemini: 0/15All assistants: 0/60
named the companyanswered without naming itdid not answer

Columns, left to right: OpenAI, Claude, Perplexity, Gemini. Across the study, on 50% of the questions where a company is named, only one AI assistant names it.

Transaction detailSection 02

What Gemini actually said

The company is not named in this answer

Geminidid not name the company

“What are the best approaches besides automated threat feeds for managing third-party cyber risk?”

Beyond automated threat feeds, best approaches for managing third-party cyber risk focus on a combination of strategic planning, continuous engagement, and smart automation.

Here are some key strategies:

1. Strategic Program Design and Tiering:

Tiering based on risk and criticality: Not all vendors pose the same risk. Classify vendors based on data sensitivity, service criticality, and the level of access they have to your systems. This allows for proportionate due diligence, preventing unnecessary burden and focusing resources where they matter most surecloud.com …

The AI’s answer as we received it, shortened and not checked for accuracy.

PayeesSection 03

Where the mentions went

The companies these answers named, and how the mentions were split between this company and its peers.

Named instead

CompanyTimes named
  1. 01
    SecurityScorecard
    2
  2. 02
    BitSight
    1

Share of mentions

CompanyShare
  1. BitSight
    50.0%
  2. SecurityScorecard
    42.9%
  3. RiskRecon (a Mastercard company)
    7.1%
  4. iCOUNTER
    0.0%
  5. CyberGRX
    0.0%
  6. UpGuard
    0.0%
ReferrersSection 04

Websites the AI used as sources

How many times each website was listed as a source in these answers

  1. 01
    bitsight.com
    22
  2. 02
    securityscorecard.com
    11
  3. 03
    recordedfuture.com
    9
  4. 04
    expertinsights.com
    8
  5. 05
    panorays.com
    6
  6. 06
    group-ib.com
    6
  7. 07
    upguard.com
    6
  8. 08
    safe.security
    5
  9. 09
    vectra.ai
    5
  10. 10
    cyware.com
    5

iCOUNTER is in the study as a fraud and identity company.

By assistantSection 05

Which AI assistants named the company

Answers naming it, out of answers received

Claude
0/15
Gemini
0/15
OpenAI
0/15
Perplexity
0/15
Market comparisonSection 06

How iCOUNTER compares with all 2,219 companies

Every tower is a company in the study, taller for a higher overall score and lit where AI assistants named it. Its own group stays lit. Hover a tower to see it, and click to open it.

What makes up the score

Each part is scored out of 100.

Visibility
0

How often AI assistants name the company, and how that compares with its peers

Citations
0

How often AI uses the company's own website as a source

Sentiment
50

How positively AI describes the company

Site quality
87

How well built the website's own pages are

AI readiness
29

How easily AI tools can read and use the website

Visibility is calculated from the answers on this page. How the score is built.

Against its sectorSection 07

Compared with financial technology and data companies

574 companies in this sector. The gold tick on each bar is the typical company.

Overall score
28
Ranks 302 of 574. Typical company: 29
Named in answers
0.0%
Ranks 334 of 574. Typical company: 1.7%
AI readiness
29
Ranks 224 of 574. Typical company: 29
Against its categorySection 08

Compared with fraud and identity companies

171 companies in this category. The gold tick on each bar is the typical company.

Overall score
28
Ranks 97 of 171. Typical company: 30
Named in answers
0.0%
Ranks 103 of 171. Typical company: 1.7%
AI readiness
29
Ranks 88 of 171. Typical company: 36
AI agentsSection 09

Ready for AI agents

What this website publishes for an AI agent to read, use and pay through, on a scale of six levels.

  1. 0
  2. 1
  3. 2
  4. 3
  5. 4
  6. 5

Level 0: Not yet agent-ready. Agents struggle to find basic information about the site.

To reach level 1:
  • 2 of robots.txt, Sitemap, Link headers
Can an AI agent check out here?

Not by itself. iCOUNTER publishes none of the agent payment protocols we look for. An agent would have to click through the website like a person.

Agent payment protocols
  • x402Not present
  • MPPNot present
  • AP2Not present
  • ACPNot present
  • UCPNot present
Interfaces for AI agents
  • MCP Server CardNot present
  • A2A Agent CardNot present
  • Agent SkillsNot present
  • API CatalogNot present
  • llms.txtNot present
  • WebMCPCould not check
Show every check, for your technical team
Discoverability
36 of 100
  • robots.txtNot met

    Publish a valid robots.txt with crawl rules and a Sitemap line.

    study: 86.1%
  • SitemapPassed

    An XML sitemap listing the pages, ideally referenced from robots.txt.

    study: 87.9%
  • Link headersNot met

    Send Link headers such as rel="api-catalog" or rel="describedby" on the homepage.

    study: 1.5%
  • DNS for AI DiscoveryNot present

    Advertise agent endpoints with SVCB records under _agents.

    study: 4.3%
Content accessibility
0 of 100
  • Markdown negotiationNot met

    Return a markdown version when asked with Accept: text/markdown, for example at the CDN.

    study: 6.2%
  • llms.txtNot met

    Publish an llms.txt with a short summary and the key links.

    study: 31.0%
Bot access control
32 of 100
  • AI bot rulesPartly met

    Add explicit robots.txt groups for the main AI crawlers.

    study: 92.7%
  • Content SignalsNot met

    Add a Content-Signal line for search, ai-input and ai-train to robots.txt.

    study: 2.3%
  • Web Bot AuthNot present

    Only relevant if you run your own agents or crawlers: publish a signing key directory.

    study: 0.3%
APIs, auth and MCP
0 of 100
  • API CatalogNot met

    List public APIs in a linkset at /.well-known/api-catalog.

    study: 1.3%
  • OAuth discoveryNot met

    Publish OpenID Connect or OAuth server metadata under /.well-known.

    study: 6.3%
  • OAuth Protected ResourceNot met

    Serve protected resource metadata at /.well-known/oauth-protected-resource.

    study: 5.3%
  • auth.mdNot met

    Publish an auth.md describing how agents sign in.

    study: 0.2%
  • MCP Server CardNot met

    Offer an MCP server and publish its server card under /.well-known.

    study: 0.8%
  • A2A Agent CardNot met

    Publish an agent card at /.well-known/agent-card.json.

    study: 0.2%
  • Agent SkillsNot met

    Publish a skills index with the main tasks agents can do.

    study: 1.0%
  • WebMCPCould not check

    Register key actions such as search or cart as WebMCP tools.

    study: 0.0%
  • ARD manifestNot met

    Publish an ai-catalog.json listing every agent interface.

    study: 0.2%

Score 15 of 100 · 97% of checks could run · checked on October 9, 2026 · icounter.com · 2,200 companies scanned

See how every company did, and how this was measured.

AI readinessSection 10

The checks behind the AI readiness score

4 of 14 passed. This list is for your technical team. The percentage is how many companies in the study pass each check.

These 14 checks ran during the audit and are what the AI readiness part of the overall score counts. The scan above ran later and is stricter on Link headers and Markdown, so a check can pass here and not there.

Show the 14 checks

Discoverability

  • Passedrobots.txt publishedstudy: 85.1%
  • PassedXML sitemapstudy: 79.4%
  • PassedHTTP Link headers (RFC 8288)study: 42.5%

Content Accessibility

  • Not metMarkdown content negotiationstudy: 7.4%
  • Not metllms.txt publishedstudy: 28.1%
  • PassedToken budget (page weight)study: 97.3%

Bot Access Control

  • Not metExplicit AI bot rulesstudy: 11.5%

API / Auth / MCP

  • Not metAPI Catalog (.well-known/api-catalog)study: 1.1%
  • Not metOAuth Authorization Server discoverystudy: 5.2%
  • Not metOAuth Protected Resource discoverystudy: 4.6%
  • Not metMCP Server Cardstudy: 0.2%
  • Not metA2A Agent Cardstudy: 0.0%
  • Not metWebMCP toolsstudy: 0.3%
  • Not metAgent Skills declaredstudy: 0.1%
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