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.
881 of 2,219
its rank by overall score
Closing balance

Named in

2of 60 answers

by 2 of 4 assistants

Statement note

What this means for Skyfire

AI assistants rarely name this digital asset infrastructure company, appearing in just 2 out of 60 answers. Its overall score is 30 out of 100, ranking 881st of 2,219 companies. The company's website passes 5 of 14 technical checks for AI tool readiness.

We asked 15 unbranded questions about its market, so no question mentioned Skyfire by name. AI assistants named it in 2 of the 60 answers. Counting only how often it is named, it ranks 45th of 102 in its sector. The typical crypto and digital asset 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
30Overall score
  • Visibility13
  • Citations0
  • Sentiment50
  • Site quality73
  • AI readiness36
Summary of account
Named by AI in
3.3%
2 of 60 answers
Share of mentions
100.0%
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
49 of 102
by overall score, where the typical crypto and digital asset company scores 30

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Line itemsSection 01

Every question, and where the company came up

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

  1. 01AI agent authentication infrastructure vs traditional bot management solutions—how do they differ?0/4
  2. 02How much do AI agent trust stack services cost for small and mid-sized businesses?0/4
  3. 03What causes frequent 403 errors when deploying AI agents for web transactions?0/4
  4. 04How can unified authentication help AI agents handle checkout and payment tasks at scale?0/4
  5. 05What are alternatives to unified login and payment credential stacks for automated agent use?2/4
  6. 06Why do AI agents get blocked during online checkouts and how can I fix it?0/4
  7. 07Unified trust stacks versus separate identity management and payment systems for agents—pros and cons?0/4
  8. 08Is investing in AI agent transactional infrastructure worth it for e-commerce automation?0/4
  9. 09Are there decentralized options for managing identity and payments in agent-driven workflows?0/4
  10. 10What are examples of deploying verified identity solutions for agents accessing multiple online services?0/4
  11. 11What are the best solutions for giving AI agents secure identity and payment credentials?0/4
  12. 12How can I enable seamless login and checkout for AI agents on commercial websites?0/4
  13. 13What features should I look for in an authentication and payment platform for AI agents?0/4
  14. 14What are the main pricing models for authentication and transaction infrastructure for AI agents?0/4
  15. 15Do businesses benefit more from a combined identity and payment platform for AI agents?0/4
Total namedOpenAI: 0/15Claude: 1/15Perplexity: 1/15Gemini: 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 alternatives to unified login and payment credential stacks for automated agent use?”

… Per-agent wallets or virtual cards · Each agent receives an isolated spending instrument with limits, merchant restrictions, and revocation · Autonomous purchasing · Skyfire, Payman, Natural, Crossmint, stablecoin wallets

Stablecoin or protocol-native payments · Agents pay through wallets or payment protocols rather than traditional card credentials · Machine-to-machine payments and micropayments · USDC wallets, x402-style payment flows, AP2/UCP concepts

API-key delegation · A master credential is retained by the operator; the agent receives a scoped sub-key or …

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. Skyfire
    100.0%
  2. HumanSignal (formerly Snorkel AI) - AgentPay
    0.0%
  3. MindOS by Cresta
    0.0%
  4. Interlock AI
    0.0%
  5. OpenCopilot
    0.0%
  6. Persona
    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
    arxiv.org
    20
  2. 02
    nevermined.ai
    14
  3. 03
    dock.io
    6
  4. 04
    linkedin.com
    4
  5. 05
    nhimg.org
    4
  6. 06
    okta.com
    4
  7. 07
    workos.com
    4
  8. 08
    thoughtworks.com
    3
  9. 09
    dev.to
    3
  10. 10
    firecrawl.dev
    3

Skyfire is in the study as a digital asset infrastructure company.

By assistantSection 05

Which AI assistants named the company

Answers naming it, out of answers received

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

How Skyfire 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
13

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
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 crypto and digital asset companies

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

Overall score
30
Ranks 49 of 102. Typical company: 30
Named in answers
3.3%
Ranks 45 of 102. Typical company: 1.7%
AI readiness
36
Ranks 21 of 102. Typical company: 33
Against its categorySection 08

Compared with digital asset infrastructure companies

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

Overall score
30
Ranks 36 of 80. Typical company: 29
Named in answers
3.3%
Ranks 33 of 80. Typical company: 1.7%
AI readiness
36
Ranks 16 of 80. Typical company: 36
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. Skyfire 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 · skyfire.xyz · 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

  • 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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