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.
208 of 2,219
its rank by overall score
Place in its category
14th of 203 payment processors
Closing balance

Named in

3of 60 answers

by 2 of 4 assistants

Statement note

What this means for Hudson Technology Systems

AI assistants name this payment processor in 5% of answers, more often than the typical company in its sector, which is named in 0%. Hudson Technology Systems has an overall score of 41 out of 100 and ranks 208th of 2,219. Its website passes 5 out of 14 technical checks, showing some readiness for AI tools.

We asked 15 unbranded questions about its market, so no question mentioned Hudson Technology Systems by name. AI assistants named it in 3 of the 60 answers. Counting only how often it is named, it ranks 106th of 375 in its sector. The typical cards and payments company is named in 0.0% of answers.

Overall score
41Overall score
  • Visibility15
  • Citations5
  • Sentiment100
  • Site quality73
  • AI readiness36
Summary of account
Named by AI in
5.0%
3 of 60 answers
Share of mentions
60.0%
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
31 of 375
by overall score, where the typical cards and payments company scores 27

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Line itemsSection 01

Every question, and where the company came up

Hudson Technology Systems came up on 2 of 15 questions. Open any window to read that answer.

  1. 01What should I do if a specialty deposit platform fails to process a payment routing instruction?0/4
  2. 02Which use cases are best suited for virtual account management within specialty deposits?1/4
  3. 03What are alternatives to using automated specialty deposit platforms for managing complex commercial deposits?0/4
  4. 04Can banks use general treasury management systems as an alternative to specialty deposit automation?0/4
  5. 05How do US banks use automation to manage escrow and milestone-triggered deposits?0/4
  6. 06How can compliance issues be minimized when using automated servicing for specialty deposits?0/4
  7. 07What pricing models are common for specialty deposit automation platforms in the US?0/4
  8. 08What are the best specialty deposit automation solutions for banks in the US?2/4
  9. 09What is the difference between traditional deposit servicing platforms and specialty deposit automation systems?0/4
  10. 10How can US credit unions automate commercial deposit origination and servicing?0/4
  11. 11What features should US banks look for in a specialty deposit origination and servicing system?0/4
  12. 12How much do commercial deposit automation tools typically cost for US credit unions?0/4
  13. 13Do specialty deposit automation platforms justify their cost for commercial deposit accounts?0/4
  14. 14Is investing in deposit origination automation worth it for mid-sized banks?0/4
  15. 15Escrow deposit automation vs. manual processing—what are the pros and cons?0/4
Total namedOpenAI: 1/15Claude: 2/15Perplexity: 0/15Gemini: 0/15All 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 Claude actually said

An answer that names the company

Claudenamed the company

“Which use cases are best suited for virtual account management within specialty deposits?”

… Running an escrow and specialty deposits book for commercial clients traditionally took armies of personnel and a patchwork of makeshift systems to onboard deals, track the terms, and release payments. Hudson handles it all, from origination through servicing. Virtual accounts spin up as many routable sub-accounts as the client requires.

2. Receivables Management

Receivables management is perhaps the most common application, with virtual accounts making it easy to match incoming payments to the correct customer or business line. By assigning each payer a unique virtual account, reco …

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. Hudson Technology Systems
    60.0%
  2. Temenos (U.S. region)
    40.0%
  3. StoneCastle Cash Management
    0.0%
  4. Q2 PrecisionLender (a division of Q2 Holdings)
    0.0%
  5. Finastra Fusion Deposit Manager
    0.0%
  6. Ncontracts (formerly QuestSoft)
    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
    hudson.co (its own website)
    13
  2. 02
    alogent.com
    10
  3. 03
    mantl.com
    9
  4. 04
    fiserv.com
    7
  5. 05
    jackhenry.com
    7
  6. 06
    financialprofessionals.org
    5
  7. 07
    umbrex.com
    4
  8. 08
    zipdo.co
    4
  9. 09
    hudsontechnologysystems2.webflow.io
    4
  10. 10
    castler.com
    4

Hudson Technology Systems is in the study as a payment processor.

By assistantSection 05

Which AI assistants named the company

Answers naming it, out of answers received

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

How Hudson Technology Systems 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
15

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

Citations
5

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

Sentiment
100

How positively AI describes the company

Site quality
73

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 cards and payments companies

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

Overall score
41
Ranks 31 of 375. Typical company: 27
Named in answers
5.0%
Ranks 106 of 375. Typical company: 0.0%
AI readiness
36
Ranks 41 of 375. Typical company: 29
Against its categorySection 08

Compared with payment processors

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

Overall score
41
Ranks 10 of 203. Typical company: 27
Named in answers
5.0%
Ranks 47 of 203. Typical company: 0.0%
AI readiness
36
Ranks 23 of 203. 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. Hudson Technology Systems 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
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 36 of 100 · 97% of checks could run · checked on October 9, 2026 · www.hudson.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%
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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ShareSection 12

Share this result

Overall score 41 of 100 badge for Hudson Technology Systems

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/hudson.co"><img src="https://aeo-audit.rezolve.com/finance/badges/hudson.co.svg" alt="Overall score 41 of 100, Finance AI Visibility Study 2026" width="280" height="64"></a>
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