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

Q2

q2.com

Banking and fintech infrastructureFinancial technology and data

Money20/20 attendee

Statement period
October 9, 2026
when the AI assistants were asked
Account no.
787 of 2,219
its rank by overall score
Place in its category
100th of 315 fintech infrastructure companies
Closing balance

Named in

3of 60 answers

by 2 of 4 assistants

Statement note

What this means for Q2

AI assistants name Q2 in 5% of answers to market questions, which is more often than the typical company in its sector at 1.7%. Its overall score is 32 out of 100, ranking 787th of 2,219 companies. Q2's website passes 4 of 14 technical checks for readiness with AI tools.

We asked 15 unbranded questions about its market, so no question mentioned Q2 by name. AI assistants named it in 3 of the 60 answers. Counting only how often it is named, it ranks 192nd of 574 in its sector. The typical financial technology and data company is named in 1.7% of answers.

Overall score
32Overall score
  • Visibility7
  • Citations1
  • Sentiment67
  • Site quality80
  • AI readiness29
Summary of account
Named by AI in
5.0%
3 of 60 answers
Share of mentions
12.5%
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
229 of 574
by overall score, where the typical financial technology and data company scores 29

Get the full report on Q2 as a PDF

Enter your work email to download it.

We’ll use your email to follow up about this report. Privacy policy

Line itemsSection 01

Every question, and where the company came up

Q2 came up on 2 of 15 questions. Open any window to read that answer.

  1. 01How do pricing models for digital banking solutions differ by institution size and feature set in the US market?1/4
  2. 02Are there open-source or lower-cost options for credit unions looking to adopt digital lending technology?0/4
  3. 03What alternatives exist for banks seeking to modernize their digital banking services without a full platform migration?0/4
  4. 04What factors influence the cost of implementing an AI-driven digital banking platform for mid-sized financial institutions?0/4
  5. 05What other approaches can community banks take to accelerate digital transformation beyond purchasing a new platform?0/4
  6. 06How do digital banking and lending platforms impact customer engagement and retention?0/4
  7. 07How can financial institutions address integration issues between legacy systems and modern digital banking platforms?0/4
  8. 08Is investing in a next-generation digital banking platform worth it for regional credit unions?0/4
  9. 09What are common challenges banks face when migrating to a new digital banking solution?0/4
  10. 10How does a single unified banking platform compare to using multiple specialized digital tools for financial institutions?0/4
  11. 11What are the top digital banking platforms for financial institutions seeking advanced AI-powered solutions?2/4
  12. 12What are the biggest differences between digital banking solutions designed for SMBs versus enterprise banks?0/4
  13. 13What are effective ways for credit unions to use digital platforms to win more profitable business accounts?0/4
  14. 14Which digital lending technologies best enhance the customer experience for banks and credit unions?0/4
  15. 15How can banks leverage AI in digital platforms to improve commercial lending processes?0/4
Total namedOpenAI: 0/15Claude: 1/15Perplexity: 0/15Gemini: 2/15All assistants: 3/60
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 Gemini actually said

An answer that names the company

Gemininamed the company

“How do pricing models for digital banking solutions differ by institution size and feature set in the US market?”

In the US market, pricing models for digital banking solutions vary significantly based on the size of the institution and the breadth of features included. Generally, the shift is towards subscription-based models, offering more predictable costs for financial institutions.

Pricing Models and How They Differ:

Subscription-Based & Tiered Pricing: Many digital banking platforms, like Creatio and Q2, offer subscription-based models, often with tiered pricing. This allows institutions to pay for the products and capabilities they need. Tiers can be based on factors like asset size o …

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
    nCino
    8
  2. 02
    Temenos
    7

Share of mentions

CompanyShare
  1. nCino
    25.0%
  2. Fiserv (Digital Banking Solutions)
    25.0%
  3. Temenos
    20.8%
  4. Q2
    12.5%
  5. Jack Henry (Banno Digital Platform)
    8.3%
  6. Alkami Technology
    8.3%
ReferrersSection 04

Websites the AI used as sources

How many times each website was listed as a source in these answers

  1. 01
    backbase.com
    22
  2. 02
    jackhenry.com
    10
  3. 03
    creatio.com
    10
  4. 04
    meniga.com
    7
  5. 05
    fisglobal.com
    7
  6. 06
    fusefinance.com
    7
  7. 07
    guideflow.com
    6
  8. 08
    thefinancialbrand.com
    5
  9. 09
    abbacustechnologies.com
    5
  10. 10
    meridianlink.com
    4

Q2 is in the study as a fintech infrastructure company.

By assistantSection 05

Which AI assistants named the company

Answers naming it, out of answers received

Gemini
2/15
Claude
1/15
Perplexity
0/15
OpenAI
0/15
Market comparisonSection 06

How Q2 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
7

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

Citations
1

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

Sentiment
67

How positively AI describes the company

Site quality
80

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 financial technology and data companies

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

Overall score
32
Ranks 229 of 574. Typical company: 29
Named in answers
5.0%
Ranks 192 of 574. Typical company: 1.7%
AI readiness
29
Ranks 224 of 574. Typical company: 29
Against its categorySection 08

Compared with fintech infrastructure companies

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

Overall score
32
Ranks 99 of 315. Typical company: 28
Named in answers
5.0%
Ranks 85 of 315. Typical company: 1.7%
AI readiness
29
Ranks 117 of 315. 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. Q2 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.q2.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.

Take it with you

Get the full report on Q2 as a PDF

Every number on this page in a PDF you can keep and share with your team. Enter your work email to download it.

We’ll use your email to follow up about this report. Privacy policy

ShareSection 12

Share this result

Overall score 32 of 100 badge for Q2

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/q2.com"><img src="https://aeo-audit.rezolve.com/finance/badges/q2.com.svg" alt="Overall score 32 of 100, Finance AI Visibility Study 2026" width="280" height="64"></a>
End of statement
All 2,219 companiesHow we measured this
Next step

Want AI assistants to name your company?

Book a 30-minute demo to see Rezolve Ai in action and what it can do for your business.

Request a demo