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

Equipifi

equipifi.com

Buy now, pay laterCards and payments

Money20/20 attendee

Statement period
October 9, 2026
when the AI assistants were asked
Account no.
293 of 2,219
its rank by overall score
Place in its category
6th of 15 buy now, pay later providers
Closing balance

Named in

10of 60 answers

by 4 of 4 assistants

Statement note

What this means for Equipifi

AI assistants name this buy now, pay later provider in 16.7% of answers, while the typical company in its sector is not named at all. Its overall score is 39 out of 100, ranking 293rd 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 Equipifi by name. AI assistants named it in 10 of the 60 answers. Counting only how often it is named, it ranks 49th of 375 in its sector. The typical cards and payments company is named in 0.0% of answers.

Overall score
39Overall score
  • Visibility35
  • Citations3
  • Sentiment50
  • Site quality87
  • AI readiness29
Summary of account
Named by AI in
16.7%
10 of 60 answers
Share of mentions
90.9%
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
48 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

Equipifi came up on 5 of 15 questions. Open any window to read that answer.

  1. 01Are there pay-per-use pricing models for embedded BNPL in digital banking?1/4
  2. 02Are pay-over-time platforms a good investment for community banks in the U.S.?0/4
  3. 03How can flexible payments improve engagement for credit union members?0/4
  4. 04Common integration issues when launching pay-over-time features in digital banking apps?0/4
  5. 05What affects the cost of implementing a white-label BNPL platform for U.S. financial institutions?0/4
  6. 06How do you address risk management challenges in white-label BNPL offerings for banks?0/4
  7. 07What are the key features to look for in a white-label BNPL platform for financial institutions?0/4
  8. 08Examples of banks using BNPL to increase customer retention and satisfaction?0/4
  9. 09Who are the main providers of white-label BNPL technology for U.S. banks and credit unions?4/4
  10. 10What are other ways banks can offer flexible payment options without using BNPL platforms?0/4
  11. 11White-label BNPL vs in-house financing: Which is better for digital banking platforms?0/4
  12. 12Best embedded pay-over-time solutions for banks and credit unions?3/4
  13. 13How do API-based BNPL solutions compare to fully integrated post-purchase payment options for financial institutions?0/4
  14. 14Is it worth it for credit unions to implement a white-label buy now, pay later solution?1/4
  15. 15Alternatives to embedded pay-over-time capabilities for financial institutions?1/4
Total namedOpenAI: 2/15Claude: 4/15Perplexity: 1/15Gemini: 3/15All assistants: 10/60
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 Perplexity actually said

An answer that names the company

Perplexitynamed the company

“Who are the main providers of white-label BNPL technology for U.S. banks and credit unions?”

The main white-label BNPL technology providers for U.S. banks and credit unions are:

Equipifi — The clearest bank- and credit-union-native provider. It embeds debit-card and installment BNPL into existing checking accounts, digital-banking apps, and core systems; partnerships include Jack Henry and Velera.

Amount — Provides cloud-based, white-label digital lending and point-of-sale installment infrastructure for banks, including integrations with bank systems and merchant checkout experiences.

FIS — Offers BNPL capabilities through its broad banking techno …

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
    Amount
    3

Share of mentions

CompanyShare
  1. Equipifi
    90.9%
  2. Amount
    9.1%
  3. Q2 Biller Direct (formerly ClickSWITCH BNPL)
    0.0%
  4. Alkami
    0.0%
  5. Happy Money (BNPL for FIs)
    0.0%
  6. Dovly
    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
    equipifi.com (its own website)
    12
  2. 02
    thefinancialbrand.com
    8
  3. 03
    ezbob.com
    8
  4. 04
    gratifypay.com
    8
  5. 05
    chargeafter.com
    7
  6. 06
    repay.com
    6
  7. 07
    jifiti.com
    6
  8. 08
    paymentsdive.com
    6
  9. 09
    f6s.com
    6
  10. 10
    americanbanker.com
    5

Equipifi is in the study as a buy now, pay later provider.

By assistantSection 05

Which AI assistants named the company

Answers naming it, out of answers received

Claude
4/15
Gemini
3/15
OpenAI
2/15
Perplexity
1/15
Market comparisonSection 06

How Equipifi 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
35

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

Citations
3

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 cards and payments companies

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

Overall score
39
Ranks 48 of 375. Typical company: 27
Named in answers
16.7%
Ranks 49 of 375. Typical company: 0.0%
AI readiness
29
Ranks 106 of 375. Typical company: 29
Against its categorySection 08

Compared with buy now, pay later providers

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

Overall score
39
Ranks 5 of 15. Typical company: 37
Named in answers
16.7%
Ranks 7 of 15. Typical company: 16.7%
AI readiness
29
Ranks 6 of 15. 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. Equipifi 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
72 of 100
  • robots.txtPassed

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

    study: 86.1%
  • SitemapPartly 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
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 24 of 100 · 97% of checks could run · checked on October 9, 2026 · www.equipifi.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%
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

  • GPTBotpartly
  • ClaudeBotpartly
  • Anthropic-AIpartly
  • Google-Extendedpartly
  • Applebot-Extendedpartly
  • Meta-ExternalAgentpartly
  • FacebookBotpartly
  • Bytespiderpartly
  • CCBotpartly
  • Diffbotpartly
  • Omgilibotpartly

AI search

  • OAI-SearchBotpartly
  • Claude-SearchBotpartly
  • PerplexityBotpartly
  • Applebotpartly
  • Amazonbotpartly
  • YouBotpartly

Answering questions

  • ChatGPT-Userpartly
  • Claude-Userpartly
  • Perplexity-Userpartly
  • DuckAssistBotpartly
  • MistralAI-Userpartly

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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ShareSection 12

Share this result

Overall score 39 of 100 badge for Equipifi

A badge of this result that you can show on your own website. Copy the code below.

<a href="https://aeo-audit.rezolve.com/us/finance/equipifi.com"><img src="https://aeo-audit.rezolve.com/finance/badges/equipifi.com.svg" alt="Overall score 39 of 100, Finance AI Visibility Study 2026" width="280" height="64"></a>
End of statement
All 2,219 companiesHow we measured this
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