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 to 14, 2026
when the AI assistants were asked
Account no.
56 of 2,219
its rank by overall score
Closing balance

Named in

12of 60 answers

by 4 of 4 assistants

Statement note

What this means for Quavo

AI assistants name Quavo in 20% of answers. This is much more often than the typical company in its sector, which is named in 1.7% of answers. Quavo has an overall score of 45 out of 100, ranking 56th of 2,219 companies. Its website passes 6 of 14 technical checks of readiness for AI tools.

We asked 15 unbranded questions about its market, so no question mentioned Quavo by name. AI assistants named it in 12 of the 60 answers. Counting only how often it is named, it ranks 46th of 574 in its sector. The typical financial technology and data company is named in 1.7% of answers.

Overall score
45Overall score
  • Visibility30
  • Citations6
  • Sentiment83
  • Site quality80
  • AI readiness43
Summary of account
Named by AI in
20.0%
12 of 60 answers
Share of mentions
54.5%
of the mentions of it and its 5 peers
AI readiness
6/14
technical checks passed. Typical for its sector: 4
Rank in its sector
20 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

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

  1. 01Features to look for in automated fraud and dispute management software for financial institutions?0/4
  2. 02Is investing in AI-driven dispute management platforms worth it for small banks?0/4
  3. 03Automated fraud monitoring vs. manual review for card issuers: what are the pros and cons?0/4
  4. 04How do financial institutions use dispute management software to stay compliant with regulations?0/4
  5. 05Are there affordable fraud management platforms for credit unions with fewer than 50,000 members?0/4
  6. 06Alternatives to AI-driven fraud management platforms for addressing high dispute volumes?1/4
  7. 07Why might automated dispute workflows fail to resolve claims quickly?0/4
  8. 08How much does automated fraud and dispute management software cost for community banks?3/4
  9. 09Best practices for reducing cardholder losses using automated fraud resolution tools?0/4
  10. 10What is the best fraud and dispute management software for mid-sized banks?3/4
  11. 11How does AI-powered dispute management compare to traditional manual systems in financial institutions?1/4
  12. 12Which automated fraud resolution tools are recommended for credit unions?4/4
  13. 13Do automated fraud management solutions really lower costs for financial organizations?0/4
  14. 14Common problems with implementing automated fraud resolution in card issuing banks?0/4
  15. 15What are the main alternatives to automated dispute resolution in financial services?0/4
Total namedOpenAI: 1/15Claude: 4/15Perplexity: 3/15Gemini: 4/15All assistants: 12/60
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 is the best fraud and dispute management software for mid-sized banks?”

For a mid-sized U.S. bank, Quavo Fraud & Disputes (QFD) is the strongest overall shortlist candidate. It is designed specifically for issuing banks, credit unions, and fintechs, combining fraud detection, dispute intake, investigation, automation, and case management in one platform.

Best options

Software · Best fit · Why consider it

Quavo Fraud & Disputes · Best overall for mid-sized banks · Purpose-built for financial institutions and combines fraud and dispute workflows in one platform.

Pega Smart Dispute · Banks needing complex workf …

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.

Share of mentions

CompanyShare
  1. Quavo
    54.5%
  2. Verifi (A Visa Solution)
    27.3%
  3. Fiserv Fraud Risk and Dispute Resolution
    9.1%
  4. ACI Worldwide Dispute Manager
    9.1%
  5. FIS CodeConnect Fraud & Dispute Management
    0.0%
  6. Informa Software (Informa FCRM)
    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
    quavo.com (its own website)
    16
  2. 02
    fintechspecs.com
    12
  3. 03
    fluxforce.ai
    8
  4. 04
    guideflow.com
    8
  5. 05
    ey.com
    7
  6. 06
    theretailexec.com
    6
  7. 07
    corporate.visa.com
    6
  8. 08
    alloy.com
    5
  9. 09
    thefinancialbrand.com
    5
  10. 10
    fraud.net
    5

Quavo is in the study as a fraud and identity company.

By assistantSection 05

Which AI assistants named the company

Answers naming it, out of answers received

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

How Quavo 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
30

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

Citations
6

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

Sentiment
83

How positively AI describes the company

Site quality
80

How well built the website's own pages are

AI readiness
43

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
45
Ranks 20 of 574. Typical company: 29
Named in answers
20.0%
Ranks 46 of 574. Typical company: 1.7%
AI readiness
43
Ranks 29 of 574. Typical company: 29
Against its categorySection 08

Compared with fraud and identity companies

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

Overall score
45
Ranks 7 of 171. Typical company: 30
Named in answers
20.0%
Ranks 7 of 171. Typical company: 1.7%
AI readiness
43
Ranks 11 of 171. Typical company: 36
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. Quavo 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
18 of 100
  • API CatalogNot met

    List public APIs in a linkset at /.well-known/api-catalog.

    study: 1.3%
  • OAuth discoveryPartly met

    Publish OpenID Connect or OAuth server metadata under /.well-known.

    study: 6.3%
  • OAuth Protected ResourcePassed

    Metadata telling agents which authorization server to use.

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

6 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%
  • PassedOAuth Authorization Server discoverystudy: 5.2%
  • PassedOAuth 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

  • GPTBotallowed
  • ClaudeBotallowed
  • Anthropic-AIallowed
  • Google-Extendedallowed
  • Applebot-Extendedallowed
  • Meta-ExternalAgentallowed
  • FacebookBotallowed
  • Bytespiderallowed
  • CCBotallowed
  • Diffbotallowed
  • Omgilibotallowed

AI search

  • OAI-SearchBotallowed
  • Claude-SearchBotallowed
  • PerplexityBotallowed
  • Applebotallowed
  • Amazonbotallowed
  • YouBotallowed

Answering questions

  • ChatGPT-Userallowed
  • Claude-Userallowed
  • 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 45 of 100 badge for Quavo

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/quavo.com"><img src="https://aeo-audit.rezolve.com/finance/badges/quavo.com.svg" alt="Overall score 45 of 100, Finance AI Visibility Study 2026" width="280" height="64"></a>
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