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.
1,065 of 2,219
its rank by overall score
Place in its category
82nd of 203 payment processors
Closing balance

Named in

1of 60 answers

by 1 of 4 assistants

Statement note

What this means for INTELICA

AI assistants rarely name INTELICA in their answers, doing so in just 1 of 60 responses reviewed. INTELICA’s overall score is 28 out of 100, compared to 27 for the typical company in its sector, ranking 1,065th of 2,219. Its website passes 3 out of 14 technical checks for AI tool readiness.

We asked 15 unbranded questions about its market, so no question mentioned INTELICA by name. AI assistants named it in 1 of the 60 answers. Counting only how often it is named, it ranks 133rd of 375 in its sector. The typical cards and payments company is named in 0.0% 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
28Overall score
  • Visibility7
  • Citations1
  • Sentiment50
  • Site quality80
  • AI readiness21
Summary of account
Named by AI in
1.7%
1 of 60 answers
Share of mentions
50.0%
of the mentions of it and its 5 peers
AI readiness
3/14
technical checks passed. Typical for its sector: 4
Rank in its sector
156 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

INTELICA came up on 1 of 15 questions. Open any window to read that answer.

  1. 01Do payment cost analysis tools actually improve the bottom line for credit unions?0/4
  2. 02Is investing in specialized card portfolio analytics worthwhile for community banks?0/4
  3. 03Alternatives to dedicated payment advisory platforms for analyzing cardholder profitability0/4
  4. 04Payment analytics platform vs incentive agreement advisory: which helps more with portfolio cost reduction?0/4
  5. 05How much should a financial institution budget for interchange analytics solutions annually?0/4
  6. 06Recommended solutions for real-time card transaction cost analysis1/4
  7. 07How can acquirers use payment analytics to negotiate better incentive terms with card networks?0/4
  8. 08Top analytics solutions for acquirers to optimize card portfolio profitability0/4
  9. 09Common pitfalls when using interchange fee analytics for issuer reporting0/4
  10. 10What are some low-cost tools to supplement payment analytics for acquirer operations?0/4
  11. 11What are the best payment analytics platforms for card issuers focused on reducing network fees?0/4
  12. 12How does a payment advisory platform compare to standard transaction monitoring tools for issuers?0/4
  13. 13What are best practices for leveraging card network billing analysis to identify cost savings?0/4
  14. 14Typical pricing models for payment analytics platforms aimed at card portfolio managers0/4
  15. 15Why am I seeing unexpected card network fee increases in my monthly statements?0/4
Total namedOpenAI: 0/15Claude: 1/15Perplexity: 0/15Gemini: 0/15All assistants: 1/60
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

“Recommended solutions for real-time card transaction cost analysis”

… Intelica's Approach: Banks can log in to a proprietary fee management platform that provides customizable dashboards, automatic fee updates, crucial alerts, performance indicators, and any changes in billing

Torus Analytics: Provides dynamic per-transaction card networks fee prediction calculated daily with near 100% accuracy, automated regular Visa & Mastercard invoices reconciliation, and transactional data enrichment

Payment Orchestration Platforms

Self-service smart payment routing with no-code configuration enables cost and performance optimization. Intelligent acceptance …

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. INTELICA
    50.0%
  2. Accenture Payments Analytics
    50.0%
  3. FIS Payments Optimization and Analytics
    0.0%
  4. First Annapolis (Accenture Payments Consulting)
    0.0%
  5. Oliver Wyman Payments Consulting
    0.0%
  6. The Strawhecker Group (TSG)
    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
    cardtraq.com
    12
  2. 02
    mastercard.com
    9
  3. 03
    torusadvisors.com
    8
  4. 04
    optimizedpayments.com
    8
  5. 05
    akurateco.com
    7
  6. 06
    f6s.com
    7
  7. 07
    fiserv.com
    6
  8. 08
    guideflow.com
    5
  9. 09
    sourceforge.net
    5
  10. 10
    solidgate.com
    5

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

How INTELICA 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
50

How positively AI describes the company

Site quality
80

How well built the website's own pages are

AI readiness
21

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
28
Ranks 156 of 375. Typical company: 27
Named in answers
1.7%
Ranks 133 of 375. Typical company: 0.0%
AI readiness
21
Ranks 216 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
28
Ranks 80 of 203. Typical company: 27
Named in answers
1.7%
Ranks 64 of 203. Typical company: 0.0%
AI readiness
21
Ranks 113 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. INTELICA 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 negotiationCould not check

    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 28 of 100 · 86% of checks could run · checked on October 9, 2026 · www.intelica.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

3 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%
  • 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

  • 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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