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

Unit

unit.co

Embedded finance and banking as a serviceFinancial technology and data

Money20/20 attendee

Statement period
October 9, 2026
when the AI assistants were asked
Account no.
559 of 2,219
its rank by overall score
Place in its category
3rd of 19 embedded finance providers
Closing balance

Named in

7of 60 answers

by 4 of 4 assistants

Statement note

What this means for Unit

AI assistants name Unit in 11.7% of answers, which is more often than the typical company in its sector at 1.7%. Its overall score is 36 out of 100, ranking 559th of 2,219 across the study. Unit’s website passes 5 out of 14 technical checks measuring how ready it is for AI tools.

We asked 15 unbranded questions about its market, so no question mentioned Unit by name. AI assistants named it in 7 of the 60 answers. Counting only how often it is named, it ranks 100th of 574 in its sector. The typical financial technology and data company is named in 1.7% of answers.

Overall score
36Overall score
  • Visibility17
  • Citations0
  • Sentiment70
  • Site quality80
  • AI readiness36
Summary of account
Named by AI in
11.7%
7 of 60 answers
Share of mentions
29.2%
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
181 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

Unit came up on 2 of 15 questions. Open any window to read that answer.

  1. 01What features do US-based BaaS platforms offer for regulatory compliance and security?0/4
  2. 02Other ways to embed payment functionality into a fintech product without BaaS0/4
  3. 03Banking-as-a-service platforms vs. building a financial product in-house0/4
  4. 04Typical pricing models for banking-as-a-service providers in the United States0/4
  5. 05Are banking-as-a-service platforms worth it for scaling fintech startups?0/4
  6. 06How to resolve delays in fund transfers with embedded financial product infrastructure0/4
  7. 07Best ways for technology companies to add payments and lending to their app0/4
  8. 08What should fintech companies consider before choosing a BaaS provider?0/4
  9. 09What are the top banking-as-a-service platforms for launching embedded financial products in the US?4/4
  10. 10Common issues when integrating accounts and payments through a banking-as-a-service platform0/4
  11. 11How do account and card fees compare across different BaaS platforms?3/4
  12. 12Best use-cases for embedded wallets and debit card issuance through banking infrastructure platforms0/4
  13. 13How does banking-as-a-service differ from traditional banking APIs?0/4
  14. 14Alternatives to using a BaaS platform for launching a neobank0/4
  15. 15How can a SaaS business use BaaS to offer lending products to its users?0/4
Total namedOpenAI: 2/15Claude: 2/15Perplexity: 1/15Gemini: 2/15All assistants: 7/60
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

“What are the top banking-as-a-service platforms for launching embedded financial products in the US?”

The strongest US options are Unit, Treasury Prime, Stripe, Lithic, Marqeta, and Galileo. The best choice depends on whether the priority is an all-in-one launch, bank-partner flexibility, card issuing, or large-scale program management.

Platform · Best for · Key strengths

Unit · Fast, full-stack embedded banking · Deposit and wallet accounts, cards, ACH, payments, credit, ledgers, APIs, and ready-made interfaces; frequently positioned as a leading US all-in-one option.

Treasury Prime · Fintechs wanting bank-partner choice · API-first banking infrastru …

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
    Treasury Prime
    10
  2. 02
    Synctera
    9

Share of mentions

CompanyShare
  1. Synctera
    33.3%
  2. Treasury Prime
    33.3%
  3. Unit
    29.2%
  4. Railsr (formerly Railsbank)
    4.2%
  5. Bond
    0.0%
  6. Rize
    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
    20
  2. 02
    connectpay.com
    18
  3. 03
    ge.mba
    16
  4. 04
    backbase.com
    15
  5. 05
    fintechspecs.com
    12
  6. 06
    dashdevs.com
    9
  7. 07
    velmie.com
    6
  8. 08
    crassula.io
    6
  9. 09
    sdk.finance
    6
  10. 10
    treasuryprime.com
    6

Unit 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
2/15
Claude
2/15
OpenAI
2/15
Perplexity
1/15
Market comparisonSection 06

How Unit 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
17

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
70

How positively AI describes the company

Site quality
80

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
36
Ranks 181 of 574. Typical company: 29
Named in answers
11.7%
Ranks 100 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
36
Ranks 3 of 19. Typical company: 27
Named in answers
11.7%
Ranks 3 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 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. Unit 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
36 of 100
  • robots.txtNot met

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

    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
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 26 of 100 · 97% of checks could run · checked on October 9, 2026 · www.unit.co · 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%
  • PassedHTTP 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

  • 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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ShareSection 11

Share this result

Overall score 36 of 100 badge for Unit

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/unit.co"><img src="https://aeo-audit.rezolve.com/finance/badges/unit.co.svg" alt="Overall score 36 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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