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.
1,730 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 ZYNK

AI assistants do not name this cross-border payments provider in any of their 60 answers, while others like Nium and Wise Platform appear. Its overall score is 26 out of 100, which places it 1,730th of 2,219 companies, close to the typical company in its sector. The website passes 5 of 14 technical checks for readiness for AI tools.

We asked 15 unbranded questions about its market, so no question mentioned ZYNK by name. AI assistants named it in none of the 60 answers. Counting only how often it is named, it ranks 179th of 375 in its sector. The typical cards and payments company is named in 0.0% 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
26Overall score
  • Visibility0
  • Citations0
  • Sentiment50
  • Site quality73
  • AI readiness36
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
5/14
technical checks passed. Typical for its sector: 4
Rank in its sector
229 of 375
by overall score, where the typical cards and payments company scores 27

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Line itemsSection 01

Every question, and where the company came up

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

  1. 01What are common use-cases for 24x7 on-demand liquidity in global B2B payments?0/4
  2. 02How can businesses troubleshoot failed instant global payments to certain countries?0/4
  3. 03Which instant international payment solutions work best for US-based companies with global clients?0/4
  4. 04Are real-time global payment infrastructures worth it for small businesses expanding internationally?0/4
  5. 05Real-time payment rails vs legacy wire transfers: what's better for B2B global transactions?0/4
  6. 06How can US businesses leverage instant settlement infrastructure for international payroll or supplier payments?0/4
  7. 07What are alternatives to instant global payment infrastructure for cross-border transactions?0/4
  8. 08Which global payment networks offer immediate liquidity without the need for underwriting?0/4
  9. 09Options for US companies who can't access T+0 settlement but need faster international payments?0/4
  10. 10How much do real-time global payment platforms typically charge per transaction?0/4
  11. 11How does T+0 settlement compare to traditional cross-border payment processing methods?0/4
  12. 12Why do some international payments still get delayed even with real-time settlement infrastructures?0/4
  13. 13Are there monthly or annual fees associated with instant cross-border settlement services for enterprises?0/4
  14. 14What are the best real-time global payment infrastructures for businesses needing instant settlements?0/4
  15. 15Is investing in instant global settlement platforms a smart move for companies dealing with high transaction volumes?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 common use-cases for 24x7 on-demand liquidity in global B2B payments?”

24x7 on-demand liquidity in global B2B payments is transforming how businesses manage their finances by enabling instant, round-the-clock access to funds. This capability addresses many pain points associated with traditional payment systems, offering significant advantages across various use cases.

Common use cases for 24x7 on-demand liquidity in global B2B payments include:

Real-time Cross-Border Payments: Businesses can make international payments that settle in seconds, rather than days, eliminating delays and reducing reliance on traditional banking hours and time zones. This is …

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
    Nium
    5
  2. 02
    Ripple
    3
  3. 03
    Wise Platform
    3

Share of mentions

CompanyShare
  1. Stripe (Global Payments & Treasury Network)
    41.7%
  2. Nium
    22.2%
  3. Ripple
    19.4%
  4. Wise Platform
    16.7%
  5. ZYNK
    0.0%
  6. Solifi (formerly OpenPayd)
    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
    airwallex.com
    23
  2. 02
    stripe.com
    17
  3. 03
    wise.com
    9
  4. 04
    lightspark.com
    7
  5. 05
    nium.com
    6
  6. 06
    tipalti.com
    6
  7. 07
    ripple.com
    6
  8. 08
    swift.com
    6
  9. 09
    jpmorgan.com
    5
  10. 10
    spark.money
    5

ZYNK is in the study as a cross-border payments provider.

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 ZYNK 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
73

How well built the website's own pages are

AI readiness
36

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 cards and payments companies

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

Overall score
26
Ranks 229 of 375. Typical company: 27
Named in answers
0.0%
Ranks 179 of 375. Typical company: 0.0%
AI readiness
36
Ranks 41 of 375. Typical company: 29
Against its categorySection 08

Compared with cross-border payments providers

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

Overall score
26
Ranks 47 of 84. Typical company: 27
Named in answers
0.0%
Ranks 33 of 84. Typical company: 0.0%
AI readiness
36
Ranks 10 of 84. Typical company: 29
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 1: Basic web presence. Two of robots.txt, a sitemap and Link headers are in place.

To reach level 2:
  • Content Signals
Can an AI agent check out here?

Not by itself. ZYNK 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
77 of 100
  • robots.txtPassed

    A valid robots.txt with crawl rules at the site root.

    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
50 of 100
  • Markdown negotiationPassed

    Requests sent with Accept: text/markdown receive a markdown version of the page.

    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 36 of 100 · 97% of checks could run · checked on October 9, 2026 · www.zynk.money · 2,200 companies scanned

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

AI readinessSection 10

The checks behind the AI readiness score

5 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

  • PassedMarkdown 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%
AI crawlersSection 11

What AI crawlers may read (robots.txt)

This website sets no rules for any AI crawler by name, like 86% of the websites whose rules we could read. Every AI crawler follows its general rules.

Training AI

  • GPTBotallowed
  • ClaudeBotallowed
  • Anthropic-AIallowed
  • Google-Extendedallowed
  • Applebot-Extendedallowed
  • Meta-ExternalAgentallowed
  • FacebookBotallowed
  • Bytespiderallowed
  • CCBotallowed
  • Diffbotallowed
  • Omgilibotallowed

AI search

  • OAI-SearchBotallowed
  • Claude-SearchBotallowed
  • PerplexityBotallowed
  • Applebotallowed
  • Amazonbotallowed
  • YouBotallowed

Answering questions

  • ChatGPT-Userallowed
  • Claude-Userallowed
  • Perplexity-Userallowed
  • DuckAssistBotallowed
  • MistralAI-Userallowed

Read from the website’s own rules file (robots.txt). An asterisk marks a crawler the file names. The rest follow its general rules. “Partly” means only some pages are blocked.

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