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

Heron Data

herondata.io

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.
432 of 2,219
its rank by overall score
Place in its category
60th of 315 fintech infrastructure companies
Closing balance

Named in

2of 60 answers

by 2 of 4 assistants

Statement note

What this means for Heron Data

AI assistants rarely name the fintech infrastructure company Heron Data in their answers, mentioning it in only 2 out of 60 answers. Heron Data has an overall score of 38 out of 100, ranking 432nd of 2,219 overall and 127th of 574 in its sector. Its website passes 4 of 14 technical checks for readiness for AI tools.

We asked 15 unbranded questions about its market, so no question mentioned Heron Data 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
38Overall score
  • Visibility8
  • Citations0
  • Sentiment100
  • Site quality80
  • AI readiness29
Summary of account
Named by AI in
3.3%
2 of 60 answers
Share of mentions
22.2%
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
127 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

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

  1. 01Is subscription pricing standard for bank statement data extraction platforms?0/4
  2. 02Alternatives to automated data extraction for commercial insurance underwriting?0/4
  3. 03How does automated underwriting differ from manual underwriting for SMB finance?0/4
  4. 04Most effective platforms for boosting loan approval speed in small business finance?0/4
  5. 05What other solutions exist besides underwriting automation for reducing manual review in SMB lending?0/4
  6. 06Common reasons automated loan decision systems produce inaccurate risk assessments?0/4
  7. 07Best solutions for automating data extraction from bank statements for commercial lending decisions?1/4
  8. 08What are the top underwriting automation platforms for small business finance providers?1/4
  9. 09How do financial institutions use data extraction platforms to streamline small business loan offers?0/4
  10. 10Are automated data extraction tools reliable for insurance underwriting?0/4
  11. 11Is investing in underwriting automation worth it for small business lenders with low deal volume?0/4
  12. 12Why is my underwriting automation not accurately parsing bank statement data?0/4
  13. 13What are typical costs for underwriting automation software serving SMB finance?0/4
  14. 14Ways brokers can improve submission quality for small business finance using automation tools?0/4
  15. 15Automated underwriting platforms vs traditional loan review systems for small business lenders?0/4
Total namedOpenAI: 0/15Claude: 1/15Perplexity: 0/15Gemini: 1/15All 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 Claude actually said

An answer that names the company

Claudenamed the company

“What are the top underwriting automation platforms for small business finance providers?”

… Heron Data is strongest in fintech lending and small business underwriting, where bank statement analysis drives most of the underwriting decision.

Document-Heavy Processing

Kolena is an AI-powered underwriting automation platform that differs from traditional loan origination systems by focusing on configurable AI agents for underwriting and document-heavy workflows.

The choice of platform depends on your specific needs, whether you need an end-to-end solution or specialized tools for particular aspects like document processing or cash flow analysis.

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
    Ocrolus
    6

Share of mentions

CompanyShare
  1. Ocrolus
    77.8%
  2. Heron Data
    22.2%
  3. SentiLink
    0.0%
  4. Nova Credit
    0.0%
  5. Persona
    0.0%
  6. Alloy
    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
    lendflow.com
    21
  2. 02
    kaaj.ai
    12
  3. 03
    docsumo.com
    9
  4. 04
    lido.app
    8
  5. 05
    clearstaq.com
    7
  6. 06
    blog.cobaltintelligence.com
    7
  7. 07
    canopyservicing.com
    6
  8. 08
    llamaindex.ai
    6
  9. 09
    aloan.ai
    6
  10. 10
    biz2x.com
    5

Heron Data 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

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

How Heron Data 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
8

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
100

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
38
Ranks 127 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
38
Ranks 54 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 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. Heron Data 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
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
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 15 of 100 · 97% of checks could run · checked on October 9, 2026 · www.herondata.io · 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%
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End of statement
All 2,219 companiesHow we measured this
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