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.
1,746 of 2,219
its rank by overall score
Closing balance

Named in

1of 60 answers

by 1 of 4 assistants

Statement note

What this means for WTSS B.V.

AI assistants rarely name this fintech infrastructure company, with the company appearing in only 1 of 60 answers. It has an overall score of 25 out of 100, placing it 1,746th of 2,219 companies in the study. Its website passes 4 of 14 technical checks for readiness with AI tools.

We asked 15 unbranded questions about its market, so no question mentioned WTSS B.V. by name. AI assistants named it in 1 of the 60 answers. Counting only how often it is named, it ranks 267th 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
25Overall score
  • Visibility4
  • Citations0
  • Sentiment50
  • Site quality67
  • AI readiness29
Summary of account
Named by AI in
1.7%
1 of 60 answers
Share of mentions
10.0%
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
486 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

WTSS B.V. came up on 1 of 15 questions. Open any window to read that answer.

  1. 01Best practices for monitoring customer experience in financial institutions?0/4
  2. 02How important is real-time service monitoring for financial institutions?0/4
  3. 03How do pricing models for transaction verification platforms for banks usually work?0/4
  4. 04What are the top solutions for production verification and monitoring in banking?1/4
  5. 05What common issues can occur in production monitoring for banking services?0/4
  6. 06Is investing in automated production verification for banks worthwhile?0/4
  7. 07How to troubleshoot missed service outages in a financial institution's monitoring setup?0/4
  8. 08How does synthetic monitoring compare to real-user monitoring for banks?0/4
  9. 09Options for financial institutions seeking lightweight monitoring instead of full-scale verification solutions?0/4
  10. 10Which features are most valuable in monitoring tools for the banking industry?0/4
  11. 11What are alternatives to traditional production verification for financial services?0/4
  12. 12What are some examples of monitoring solutions improving customer experience in finance?0/4
  13. 13End-to-end transaction verification vs. basic service health checks in financial services—what's more effective?0/4
  14. 14What is the typical cost range for enterprise-level monitoring solutions in banking?0/4
  15. 15How can banks use production verification tools during migration projects?0/4
Total namedOpenAI: 0/15Claude: 0/15Perplexity: 0/15Gemini: 1/15All assistants: 1/60
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

An answer that names the company

Gemininamed the company

“What are the top solutions for production verification and monitoring in banking?”

… WTSS: Offers verification solutions for bank transfers, cross-border payments, information services, e-commerce, card payments, customer authentication, and client migration. They emphasize pilot testing and "shift right" solutions, focusing on testing after deployment.

Automated NAV Validations: Solutions like BNY Mellon's NAV Control provide automated validations of funds, real-time operational transparency, and exception management workflows.

Best Practices:

Implement robust testing strategies that include both staging (pre-production) and productio

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.

Also named

CompanyTimes named
  1. 01
    Dynatrace
    3
  2. 02
    New Relic
    2

Share of mentions

CompanyShare
  1. Dynatrace
    40.0%
  2. Splunk (Observability Cloud)
    20.0%
  3. New Relic
    20.0%
  4. WTSS B.V.
    10.0%
  5. AppDynamics (Cisco)
    10.0%
  6. Everest Group (Quantcast/transaction verification services)
    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
    linkedin.com
    5
  2. 02
    logicmonitor.com
    4
  3. 03
    fintechspecs.com
    4
  4. 04
    five9.com
    4
  5. 05
    cubeapm.com
    4
  6. 06
    gitnux.org
    4
  7. 07
    fluxforce.ai
    3
  8. 08
    github.com
    3
  9. 09
    catchpoint.com
    3
  10. 10
    ir.com
    3

WTSS B.V. is in the study as a fintech infrastructure company.

By assistantSection 05

Which AI assistants named the company

Answers naming it, out of answers received

Gemini
1/15
Claude
0/15
OpenAI
0/15
Perplexity
0/15
Market comparisonSection 06

How WTSS B.V. 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
4

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
67

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
25
Ranks 486 of 574. Typical company: 29
Named in answers
1.7%
Ranks 267 of 574. Typical company: 1.7%
AI readiness
29
Ranks 224 of 574. Typical company: 29
Against its categorySection 08

Compared with fintech infrastructure companies

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

Overall score
25
Ranks 256 of 315. Typical company: 28
Named in answers
1.7%
Ranks 123 of 315. Typical company: 1.7%
AI readiness
29
Ranks 117 of 315. 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. WTSS B.V. 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

The website runs on WooCommerce.

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
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 25 of 100 · 97% of checks could run · checked on October 9, 2026 · wtss.eu · 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%
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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