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.

SWIFT47/60TRANSUNION46/60EXPERIAN42/60COINSTAR41/60NAVY FEDERAL CREDIT UNION41/60ZELLE41/60BLOOMBERG L.P.38/60TRADINGVIEW38/60BANCO POPULAR DE PUERTO RICO37/60PAYPAL37/60VENMO37/60CHEXSYSTEMS33/60EQUIFAX33/60MORNINGSTAR32/60S&P GLOBAL31/60ALPACA MARKETS30/60COINBASE30/60PINWHEEL29/60STRIPE29/60XERO29/60ACORNS28/60AFFIRM28/60PAYFAST (PAYGATE)28/60PAYONEER28/60INTERACTIVE BROKERS26/60CHARLES SCHWAB19/60THE HARTFORD9/60MOODY'S21/60CME GROUP19/60ACCENTURE11/60CHUBB10/60MORGAN STANLEY6/60AMERICAN EXPRESS14/60SYNCHRONY FINANCIAL12/60FACTSET20/60NASDAQ19/60ALLSTATE11/60WELLS FARGO18/60PAYCHEX10/60ARES MANAGEMENT3/60
Rezolve Ai
MethodDemo
Back to the study
AI visibility statementFinance AI Visibility Study 2026Issued October 14, 2026
Account holder
K

Kunstrux

kunstrux.com

Payment processors, acquirers and point of saleCards and payments

Money20/20 attendee

Statement period
October 9 to 14, 2026
when the AI assistants were asked
Account no.
2,176 of 2,219
its rank by overall score
Place in its category
195th of 203 payment processors
Closing balance

Named in

0of 60, its tower is dark

by 0 of 4 assistants

Statement note

What this means for Kunstrux

AI assistants do not name Kunstrux in any of the 60 answers, while some answers mention Land Gorilla, Rabbet, or Built Technologies. Kunstrux has an overall score of 20 out of 100, ranking 2,176th of 2,219 companies. Its website passes 1 of 14 technical checks for AI tool readiness.

We asked 15 unbranded questions about its market, so no question mentioned Kunstrux 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
20Overall score
  • Visibility0
  • Citations0
  • Sentiment50
  • Site quality47
  • AI readiness21
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
1/14
technical checks passed. Typical for its sector: 4
Rank in its sector
366 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

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

  1. 01How do digital construction lending platforms compare to traditional manual loan management?0/4
  2. 02Typical pricing structures for construction loan management software in the US0/4
  3. 03What are the top features to look for in a construction lending platform for builders and contractors?0/4
  4. 04Construction loan software vs general project management tools: what's the difference?0/4
  5. 05Tips for builders dealing with payment delays on digital construction lending platforms0/4
  6. 06How can construction lenders resolve issues with delayed draw approvals on loan platforms?0/4
  7. 07Best construction loan management solutions for lenders in the United States0/4
  8. 08Other ways to manage construction loans without specialized lending software0/4
  9. 09Is investing in a specialized construction lending platform worth it for a mid-sized contractor?0/4
  10. 10Popular mobile apps for managing construction loan draws and payments0/4
  11. 11Affordable alternatives to digital construction lending platforms for small builders0/4
  12. 12Ways construction loan platforms can help streamline compliance for lenders0/4
  13. 13How do instant pay features benefit trade contractors in construction lending platforms?0/4
  14. 14Are there platforms for construction lending that don’t charge transaction fees for trades?0/4
  15. 15Do automated construction draw management platforms really improve project speed?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

“Typical pricing structures for construction loan management software in the US”

In the US, pricing structures for construction loan management software vary significantly based on the vendor, the features offered, and the size and needs of the lending institution. These structures generally fall into a few common models:

1. Per-Loan or Portfolio-Based Subscription Models:

Many construction loan management software providers, particularly those catering to lenders, utilize subscription models based on the number of active construction loans or the total portfolio size.

Costs can range from $150 to $800 per active loan annually, depending on the depth of featur …

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
    Land Gorilla
    4
  2. 02
    Rabbet
    3
  3. 03
    Built Technologies
    1

Share of mentions

CompanyShare
  1. Built Technologies
    40.0%
  2. Land Gorilla
    35.0%
  3. Rabbet
    25.0%
  4. Kunstrux
    0.0%
  5. Construct Lending
    0.0%
  6. BankLabs - Construct
    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
    getbuilt.com
    41
  2. 02
    rabbet.com
    16
  3. 03
    finantrix.com
    9
  4. 04
    wifitalents.com
    9
  5. 05
    themortgageoffice.com
    7
  6. 06
    landgorilla.com
    6
  7. 07
    abrigo.com
    6
  8. 08
    gitnux.org
    6
  9. 09
    worldmetrics.org
    5
  10. 10
    capterra.com
    5

Kunstrux is in the study as a payment processor.

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 Kunstrux 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
47

How well built the website's own pages are

AI readiness
21

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
20
Ranks 366 of 375. Typical company: 27
Named in answers
0.0%
Ranks 179 of 375. Typical company: 0.0%
AI readiness
21
Ranks 216 of 375. Typical company: 29
Against its categorySection 08

Compared with payment processors

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

Overall score
20
Ranks 194 of 203. Typical company: 27
Named in answers
0.0%
Ranks 91 of 203. Typical company: 0.0%
AI readiness
21
Ranks 113 of 203. 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 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. Kunstrux 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
0 of 100
  • robots.txtNot met

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

    study: 86.1%
  • SitemapNot met

    Serve an XML sitemap and reference it 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
0 of 100
  • AI bot rulesNot 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 0 of 100 · 97% of checks could run · checked on October 9, 2026 · kunstrux.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

1 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

  • Not metrobots.txt publishedstudy: 85.1%
  • Not metXML sitemapstudy: 79.4%
  • Not metHTTP 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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End of statement
All 2,219 companiesHow we measured this
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