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 and 10, 2026
when the AI assistants were asked
Account no.
1,805 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 Ionia

AI assistants do not name this embedded finance provider in any of their 60 answers to everyday market questions. Its overall score is 25 out of 100, which ranks 1,805th of 2,219 companies, slightly below the typical company in its sector. Its website passes 2 of 14 technical checks for readiness for AI tools.

We asked 15 unbranded questions about its market, so no question mentioned Ionia by name. AI assistants named it in none of the 60 answers. Counting only how often it is named, it ranks 334th 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
  • Visibility0
  • Citations0
  • Sentiment50
  • Site quality80
  • AI readiness14
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
2/14
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

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

  1. 01Is it worth investing in a unified payment platform for reseller operations?0/4
  2. 02What are the best payment processing solutions for resellers serving multiple merchants?0/4
  3. 03What are credible alternatives to modular payment processing platforms designed for resellers?0/4
  4. 04Payment orchestration vs direct acquiring: which is better for resellers managing diverse merchant portfolios?0/4
  5. 05Are there any notable alternatives to instant direct payment platforms for managing multiple merchants?0/4
  6. 06How can resellers troubleshoot chargeback spikes with integrated payment platforms?0/4
  7. 07Are instant fraud risk elimination tools essential for payment resellers?0/4
  8. 08Which instant funding options are favored by merchant service providers in the US?0/4
  9. 09What are the key benefits of using a modular payment stack for fast-growing merchant portfolios?0/4
  10. 10How does modular payment processing differ from traditional all-in-one platforms?0/4
  11. 11How can a reseller optimize approval rates for merchants using smart payment orchestration?0/4
  12. 12What common causes lead to funding delays for payment resellers, and how can they be addressed?0/4
  13. 13What solutions can replace traditional acquiring for resellers seeking more control over merchant payments?0/4
  14. 14What is the typical pricing structure for instant payment processing platforms tailored to resellers?0/4
  15. 15How much do unified payment and fraud management solutions usually cost for merchant service providers?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

“What are the best payment processing solutions for resellers serving multiple merchants?”

I'll search for current information about payment processing solutions designed for resellers.Let me search for more specific information about reseller-focused payment platforms.Based on the latest information, here are the best payment processing solutions for resellers serving multiple merchants:

Platform-Based Solutions for Multi-Merchant Resellers

White-Label Payment Platforms

The leading providers span global platforms-as-a-service (Nuvei, Adyen for Platforms, Worldpay for Platforms), PayFac-as-a-Service specialists (Finix, Maverick Payments), and orchestration or embedded opti …

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
    NMI
    4
  2. 02
    Payrix
    2

Share of mentions

CompanyShare
  1. NMI
    45.5%
  2. Payrix
    18.2%
  3. Paysafe for ISOs & Agents
    18.2%
  4. Worldpay for Platforms (formerly Payrix/Worldpay Integrated Payments)
    9.1%
  5. PayFac-as-a-Service by Infinicept
    9.1%
  6. Ionia
    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
    nmi.com
    15
  2. 02
    solidgate.com
    10
  3. 03
    stripe.com
    8
  4. 04
    decta.com
    8
  5. 05
    connectpay.com
    6
  6. 06
    mypayadvisor.com
    5
  7. 07
    nuvei.com
    5
  8. 08
    whop.com
    5
  9. 09
    payrails.com
    5
  10. 10
    worldmetrics.org
    4

Ionia is in the study as an embedded finance 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 Ionia 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
80

How well built the website's own pages are

AI readiness
14

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
0.0%
Ranks 334 of 574. Typical company: 1.7%
AI readiness
14
Ranks 504 of 574. Typical company: 29
Against its categorySection 08

Compared with embedded finance providers

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

Overall score
25
Ranks 16 of 19. Typical company: 27
Named in answers
0.0%
Ranks 10 of 19. Typical company: 0.0%
AI readiness
14
Ranks 16 of 19. 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. Ionia 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
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 · www.ioniapay.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

2 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%
  • 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%
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