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

Klavi

klavi.ai

Banking and fintech infrastructureFinancial technology and data

Money20/20 attendee

Statement period
October 9 to 14, 2026
when the AI assistants were asked
Account no.
933 of 2,219
its rank by overall score
Place in its category
130th of 315 fintech infrastructure companies
Closing balance

Named in

2of 60 answers

by 1 of 4 assistants

Statement note

What this means for Klavi

AI assistants rarely name this fintech infrastructure company in their answers, appearing in 2 of 60 answers. Perplexity names Klavi alongside companies such as Plaid and MX. Its overall score is 29 out of 100, ranking 933rd of 2,219 companies. The website passes 4 of 14 technical checks for readiness with AI tools.

We asked 15 unbranded questions about its market, so no question mentioned Klavi by name. AI assistants named it in 2 of the 60 answers. Counting only how often it is named, it ranks 223rd 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
29Overall score
  • Visibility3
  • Citations0
  • Sentiment50
  • Site quality87
  • AI readiness29
Summary of account
Named by AI in
3.3%
2 of 60 answers
Share of mentions
3.3%
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
270 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

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

  1. 01Banking data aggregation platforms vs. open finance intelligence solutions: what are the differences?0/4
  2. 02How do open finance data intelligence platforms compare to traditional credit bureaus for risk assessment?0/4
  3. 03What are alternatives to open finance data platforms for extracting actionable insights from banking data?1/4
  4. 04What should financial organizations consider before adopting an open finance intelligence tool?0/4
  5. 05Is investing in an open finance data platform worth it for optimizing payment and risk strategies?0/4
  6. 06How do financial institutions handle inaccurate or incomplete data within open finance intelligence solutions?0/4
  7. 07What are the best open finance data intelligence platforms for financial institutions seeking advanced banking analytics?1/4
  8. 08How can open finance intelligence platforms support more comprehensive credit scoring?0/4
  9. 09Most reliable open finance analytics solutions for market intelligence in the financial services industry0/4
  10. 10Common challenges when integrating multiple banking data sources into a single analytics platform0/4
  11. 11Options for financial firms that want to centralize customer payment and credit information without open finance platforms0/4
  12. 12Ways financial organizations use aggregated banking data to improve market intelligence0/4
  13. 13Which platforms help organizations transform complex banking data into actionable insights for credit decision-making?0/4
  14. 14Are there usage-based pricing models for open finance banking analytics platforms?0/4
  15. 15How much do open finance data intelligence platforms typically cost for mid-sized banks in the US?0/4
Total namedOpenAI: 0/15Claude: 0/15Perplexity: 2/15Gemini: 0/15All assistants: 2/60
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 best open finance data intelligence platforms for financial institutions seeking advanced banking analytics?”

The strongest options for financial institutions are Envestnet · Yodlee, Plaid, MX, Finicity, Fiserv, Flinks, and Klavi. The best choice depends on whether the priority is broad account connectivity, transaction enrichment, lending decisioning, embedded finance, or enterprise-grade bank analytics.

Platform · Best fit · Key strengths · Main consideration

Envestnet \ · Yodlee · Enterprise financial intelligence and personal financial management · Broad aggregation, data enrichment, analytics, and institution-focused financial data services; positioned as a platfor …

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
    Plaid
    16
  2. 02
    MX
    8
  3. 03
    Codat
    1

Share of mentions

CompanyShare
  1. Plaid
    31.7%
  2. Yodlee (Envestnet | Yodlee)
    21.7%
  3. MX
    21.7%
  4. Finicity (A Mastercard Company)
    20.0%
  5. Klavi
    3.3%
  6. Codat
    1.7%
ReferrersSection 04

Websites the AI used as sources

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

  1. 01
    sourceforge.net
    17
  2. 02
    mastercard.com
    11
  3. 03
    f6s.com
    7
  4. 04
    experian.com
    6
  5. 05
    oecd.org
    5
  6. 06
    openbankingtracker.com
    5
  7. 07
    belvo.com
    5
  8. 08
    plaid.com
    5
  9. 09
    backbase.com
    5
  10. 10
    bis.org
    4

Klavi 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

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

How Klavi 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
3

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
87

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
29
Ranks 270 of 574. Typical company: 29
Named in answers
3.3%
Ranks 223 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
29
Ranks 125 of 315. Typical company: 28
Named in answers
3.3%
Ranks 100 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 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. Klavi 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
68 of 100
  • AI bot rulesPartly met

    Add explicit robots.txt groups for the main AI crawlers.

    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 44 of 100 · 97% of checks could run · checked on October 9, 2026 · klavi.ai · 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%
  • 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

  • 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.

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End of statement
All 2,219 companiesHow we measured this
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