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 and 10, 2026
when the AI assistants were asked
Account no.
1,366 of 2,219
its rank by overall score
Closing balance

Named in

0of 60, its tower is dark

by 0 of 4 assistants

Statement note

What this means for Loop Financial

AI assistants do not name Loop Financial in any of the 60 answers reviewed, while companies like FourKites and project44 are named. Its overall score is 27 out of 100, matching the typical company in its sector, ranking 1,366th of 2,219. The website passes 4 out of 14 technical checks for AI tool readiness.

We asked 15 unbranded questions about its market, so no question mentioned Loop Financial by name. AI assistants named it in none of the 60 answers. Counting only how often it is named, it ranks 179th 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
27Overall score
  • Visibility0
  • Citations4
  • Sentiment50
  • Site quality80
  • AI readiness29
Summary of account
Named by AI in
0.0%
0 of 60 answers
Share of mentions
0.0%
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
186 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

Loop Financial came up on 0 of 15 questions. Open any window to read that answer.

  1. 01What are alternative solutions to a logistics data platform for improving supply chain visibility?0/4
  2. 02What are the top logistics data platforms for large shippers seeking unified supply chain data?0/4
  3. 03How are logistics data platforms typically priced for enterprise users?0/4
  4. 04How does a logistics data platform differ from a traditional transportation management system?0/4
  5. 05How do large shippers use data platforms to optimize payments and reconciliation in logistics?0/4
  6. 06Unified data layer vs. point solution approach for supply chain analytics—pros and cons?0/4
  7. 07How can enterprises leverage a logistics data platform to improve freight audit accuracy?0/4
  8. 08Best ways to gain real-time visibility across an enterprise supply chain0/4
  9. 09Why is my supply chain data still fragmented after integrating multiple solutions?0/4
  10. 10Enterprise cost factors for implementing a supply chain data unification platform0/4
  11. 11Common issues when automating freight audit with logistics data platforms0/4
  12. 12Do logistics data platforms actually help control freight spend in practice?0/4
  13. 13What features should an enterprise look for when evaluating logistics data platforms?0/4
  14. 14Is investing in a unified logistics data foundation worth it for enterprise supply chain operations?0/4
  15. 15Other technology options for automating freight payments besides using a logistics data platform0/4
Total namedOpenAI: 0/15Claude: 0/15Perplexity: 0/15Gemini: 0/15All assistants: 0/60
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 Gemini actually said

The company is not named in this answer

Geminidid not name the company

“What are alternative solutions to a logistics data platform for improving supply chain visibility?”

While logistics data platforms are highly effective for improving supply chain visibility, several alternative solutions and strategies can also enhance transparency and efficiency. These alternatives often involve a combination of technology, process improvements, and strong collaboration.

Here are some alternative solutions to a logistics data platform for improving supply chain visibility:

Data Analytics and Business Intelligence Tools Utilizing data analytics can help identify pain points, predict disruptions, and improve decision-making within the supply chain. These platforms …

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.

Named instead

CompanyTimes named
  1. 01
    FourKites
    9
  2. 02
    project44
    9

Share of mentions

CompanyShare
  1. project44
    41.2%
  2. FourKites
    41.2%
  3. Transporeon (A Trimble Company)
    17.6%
  4. Loop Financial
    0.0%
  5. MercuryGate
    0.0%
  6. SAP Business Network for Logistics
    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
    locus.sh
    21
  2. 02
    loop.com (its own website)
    16
  3. 03
    gartner.com
    12
  4. 04
    freehand.ai
    11
  5. 05
    traxtech.com
    9
  6. 06
    lumenalta.com
    7
  7. 07
    gpx.co
    7
  8. 08
    glideappsagency.com
    5
  9. 09
    sysgenpro.com
    5
  10. 10
    inboundlogistics.com
    4

Loop Financial is in the study as a business payments provider.

By assistantSection 05

Which AI assistants named the company

Answers naming it, out of answers received

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

How Loop Financial 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
0

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

Citations
4

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
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 cards and payments companies

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

Overall score
27
Ranks 186 of 375. Typical company: 27
Named in answers
0.0%
Ranks 179 of 375. Typical company: 0.0%
AI readiness
29
Ranks 106 of 375. Typical company: 29
Against its categorySection 08

Compared with business payments providers

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

Overall score
27
Ranks 20 of 30. Typical company: 29
Named in answers
0.0%
Ranks 17 of 30. Typical company: 1.7%
AI readiness
29
Ranks 13 of 30. 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. Loop Financial 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.loop.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

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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