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, 2026
when the AI assistants were asked
Account no.
185 of 2,219
its rank by overall score
Closing balance

Named in

9of 60 answers

by 3 of 4 assistants

Statement note

What this means for Treasury Prime

AI assistants name Treasury Prime in 15% of answers, which is much more often than the typical company in its sector at 1.7%. It has an overall score of 41 out of 100, ranking 185th of 2,219. Its website passes 5 of 14 technical checks for readiness for AI tools.

We asked 15 unbranded questions about its market, so no question mentioned Treasury Prime by name. AI assistants named it in 9 of the 60 answers. Counting only how often it is named, it ranks 76th of 574 in its sector. The typical financial technology and data company is named in 1.7% of answers.

Overall score
41Overall score
  • Visibility20
  • Citations2
  • Sentiment83
  • Site quality87
  • AI readiness36
Summary of account
Named by AI in
15.0%
9 of 60 answers
Share of mentions
33.3%
of the mentions of it and its 5 peers
AI readiness
5/14
technical checks passed. Typical for its sector: 4
Rank in its sector
54 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

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

  1. 01Are there usage-based fees or monthly subscriptions for embedded finance platforms?0/4
  2. 02Is investing in a multi-bank embedded finance network worth it for a new fintech company?1/4
  3. 03How can banks expand their fintech programs using embedded finance infrastructure?0/4
  4. 04Ways fintechs use embedded banking solutions to accelerate account opening0/4
  5. 05Common integration issues with embedded finance APIs and how to resolve them0/4
  6. 06What are the top embedded finance platforms for fintech startups in the United States?3/4
  7. 07Best multi-bank network solutions for launching fintech products2/4
  8. 08Alternatives to embedded finance for launching digital banking products2/4
  9. 09How to address delays when connecting fintech workflows to partner banks0/4
  10. 10Open banking vs embedded finance: which is suitable for US fintechs?0/4
  11. 11How much does it cost to use a banking-as-a-service platform in the US?1/4
  12. 12API-based banking platforms vs middleware solutions for embedded finance: pros and cons0/4
  13. 13What should fintechs look for when choosing an embedded finance platform?0/4
  14. 14What other solutions exist besides multi-bank networks for fintech-bank partnerships?0/4
  15. 15Embedded finance vs traditional banking integration: which is better for fintech launch?0/4
Total namedOpenAI: 3/15Claude: 0/15Perplexity: 3/15Gemini: 3/15All assistants: 9/60
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

“Best multi-bank network solutions for launching fintech products”

For a U.S. fintech launching products across multiple sponsor banks, Treasury Prime is the clearest multi-bank specialist: it advertises a network of 16 bank partners plus product partners for cards, compliance, and related services. The best alternative depends on whether you need payments, cards, international coverage, or a full banking stack.

Recommended shortlist

Provider · Best fit · Why consider it · Main diligence point

Treasury Prime · U.S. deposit accounts, wallets, lending, and multi-bank redundancy · Offers a marketplace of 16 bank partner …

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
    Unit
    15
  2. 02
    Synctera
    5
  3. 03
    Bond
    1
  4. 04
    Green Dot
    1

Share of mentions

CompanyShare
  1. Unit
    40.7%
  2. Treasury Prime
    33.3%
  3. Synctera
    18.5%
  4. Bond
    3.7%
  5. Green Dot
    3.7%
  6. Solid
    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
    connectpay.com
    15
  2. 02
    stripe.com
    12
  3. 03
    lendflow.com
    10
  4. 04
    alloy.com
    10
  5. 05
    treasuryprime.com (its own website)
    9
  6. 06
    openbankingtracker.com
    9
  7. 07
    pwc.com
    8
  8. 08
    airwallex.com
    7
  9. 09
    whop.com
    7
  10. 10
    dashdevs.com
    7

Treasury Prime 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

Gemini
3/15
Perplexity
3/15
OpenAI
3/15
Claude
0/15
Market comparisonSection 06

How Treasury Prime 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
20

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

Citations
2

How often AI uses the company's own website as a source

Sentiment
83

How positively AI describes the company

Site quality
87

How well built the website's own pages are

AI readiness
36

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
41
Ranks 54 of 574. Typical company: 29
Named in answers
15.0%
Ranks 76 of 574. Typical company: 1.7%
AI readiness
36
Ranks 83 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
41
Ranks 1 of 19. Typical company: 27
Named in answers
15.0%
Ranks 1 of 19. Typical company: 0.0%
AI readiness
36
Ranks 2 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 2: Bot-aware. Explicit AI bot rules and declared Content Signals.

To reach level 3:
  • Markdown negotiation
Can an AI agent check out here?

Not by itself. Treasury Prime 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.txtPublished
  • 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
50 of 100
  • Markdown negotiationNot met

    Return a markdown version when asked with Accept: text/markdown, for example at the CDN.

    study: 6.2%
  • llms.txtPassed

    An llms.txt that tells language models what the site offers.

    study: 31.0%
Bot access control
100 of 100
  • AI bot rulesPassed

    Explicit robots.txt rules for AI crawlers such as GPTBot, ClaudeBot and PerplexityBot.

    study: 92.7%
  • Content SignalsPassed

    Content-Signal lines in robots.txt declaring search, AI input and AI training preferences.

    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 51 of 100 · 97% of checks could run · checked on October 9, 2026 · www.treasuryprime.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

5 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%
  • Not metHTTP Link headers (RFC 8288)study: 42.5%

Content Accessibility

  • Not metMarkdown content negotiationstudy: 7.4%
  • Passedllms.txt publishedstudy: 28.1%
  • PassedToken budget (page weight)study: 97.3%

Bot Access Control

  • PassedExplicit 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 rules for 8 AI crawlers by name. All others follow its general rules.

Training AI

  • GPTBot*blocked
  • ClaudeBot*blocked
  • Anthropic-AIallowed
  • Google-Extended*blocked
  • Applebot-Extendedallowed
  • Meta-ExternalAgentallowed
  • FacebookBotallowed
  • Bytespiderallowed
  • CCBot*blocked
  • Diffbotallowed
  • Omgilibotallowed

AI search

  • OAI-SearchBot*allowed
  • Claude-SearchBot*allowed
  • PerplexityBot*allowed
  • Applebotallowed
  • Amazonbotallowed
  • YouBotallowed

Answering questions

  • ChatGPT-Userallowed
  • Claude-User*allowed
  • 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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ShareSection 12

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Overall score 41 of 100 badge for Treasury Prime

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/treasuryprime.com"><img src="https://aeo-audit.rezolve.com/finance/badges/treasuryprime.com.svg" alt="Overall score 41 of 100, Finance AI Visibility Study 2026" width="280" height="64"></a>
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