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.
2,031 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 Bitown

AI assistants do not name this cross-border payments provider in any of their 60 answers, even though they name companies like Stripe and Payoneer. Its overall score is 23 out of 100, ranking 2,031st of 2,219. The website passes 1 of 14 technical checks for AI tool readiness.

We asked 15 unbranded questions about its market, so no question mentioned Bitown 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
23Overall score
  • Visibility0
  • Citations0
  • Sentiment50
  • Site quality67
  • 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
329 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

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

  1. 01How do automated payment solutions help mid-sized businesses reduce operational friction?0/4
  2. 02How do transaction fees differ between payment infrastructure providers and traditional banks?0/4
  3. 03What’s a good substitute for businesses needing global money movement with automated workflows without using traditional banks?0/4
  4. 04How can a payment platform support batch payouts for a global marketplace business?0/4
  5. 05How can global businesses resolve delays in settlement using automated payment platforms?0/4
  6. 06Does switching to a cloud-based payment platform actually lower operational costs for enterprises?0/4
  7. 07Payment infrastructure platforms vs. standard merchant account providers: which is better for international settlements?0/4
  8. 08Are there low-code options for setting up programmable financial operations?0/4
  9. 09What’s the best way to automate recurring payouts for an international workforce?0/4
  10. 10Is investing in a payment infrastructure platform worthwhile for scaling cross-border payments?0/4
  11. 11What pricing models are typical for payment infrastructure platforms used by large businesses?0/4
  12. 12What are common issues when integrating payment infrastructure with existing ERP systems?0/4
  13. 13What alternatives exist to payment infrastructure platforms for automated B2B fund movement?0/4
  14. 14How does a programmable payment workflow platform compare to traditional banking for B2B transactions?0/4
  15. 15What are the best payment infrastructure platforms for global commerce businesses looking for fast settlement?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 Claude actually said

The company is not named in this answer

Claudedid not name the company

“How do transaction fees differ between payment infrastructure providers and traditional banks?”

I'll search for current information about how transaction fees differ between these two types of financial service providers.Based on current information, there are significant differences in how transaction fees are structured between payment infrastructure providers and traditional banks:

Card-Based Transactions

For card payments, interchange fees typically range from about 1.5% to 3% of the transaction value, and card networks like Visa and Mastercard charge additional fees for using their payment rails, usually a small percentage of each transaction. Payment processors often charge 2. …

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
    Stripe
    12
  2. 02
    Payoneer
    4
  3. 03
    Adyen
    3
  4. 04
    Rapyd
    3
  5. 05
    Checkout.com
    1

Share of mentions

CompanyShare
  1. Stripe
    50.0%
  2. Adyen
    20.0%
  3. Payoneer
    13.3%
  4. Rapyd
    10.0%
  5. Checkout.com
    6.7%
  6. Bitown
    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
    stripe.com
    16
  2. 02
    airwallex.com
    12
  3. 03
    ramp.com
    5
  4. 04
    connectpay.com
    5
  5. 05
    tipalti.com
    5
  6. 06
    wise.com
    5
  7. 07
    bvnk.com
    4
  8. 08
    xtransfer.com
    4
  9. 09
    jpmorgan.com
    4
  10. 10
    circle.com
    3

Bitown 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 Bitown 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
67

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
23
Ranks 329 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 cross-border payments providers

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

Overall score
23
Ranks 75 of 84. Typical company: 27
Named in answers
0.0%
Ranks 33 of 84. Typical company: 0.0%
AI readiness
21
Ranks 51 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 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. Bitown 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 · bitownx.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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