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.
2,190 of 2,219
its rank by overall score
Closing balance

Named in

1of 60 answers

by 1 of 4 assistants

Statement note

What this means for Warrant

AI assistants rarely name this fintech infrastructure company, mentioning it in 1 of 60 answers. In the same answer, Permit.io and Oso are also named. Its overall score is 18 out of 100, ranking 2,190th of 2,219 in the study. The company's website passes 2 of 14 technical checks for AI tool readiness.

We asked 15 unbranded questions about its market, so no question mentioned Warrant by name. AI assistants named it in 1 of the 60 answers. Counting only how often it is named, it ranks 267th 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
18Overall score
  • Visibility2
  • Citations0
  • Sentiment50
  • Site quality40
  • AI readiness14
Summary of account
Named by AI in
1.7%
1 of 60 answers
Share of mentions
1.6%
of the mentions of it and its 5 peers
AI readiness
2/14
technical checks passed. Typical for its sector: 4
Rank in its sector
569 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

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

  1. 01What common migration issues arise when switching from legacy authorization services to new FGA platforms?0/4
  2. 02What is the typical cost structure for cloud-hosted fine-grained authorization services?0/4
  3. 03What options exist for teams seeking to replace a retiring authorization engine that offered detailed permissions?0/4
  4. 04How do you handle permission migration after deprecating a fine-grained authorization engine?0/4
  5. 05What are some real-world use-cases for implementing fine-grained authorization in multi-tenant SaaS environments?0/4
  6. 06Are there any affordable fine-grained authorization solutions for small teams or solo developers?0/4
  7. 07Which fine-grained authorization platforms offer strong community support and documentation for US-based developers?0/4
  8. 08What are reliable alternatives to deprecated fine-grained authorization frameworks?0/4
  9. 09Is it worth integrating a dedicated fine-grained authorization service for a growing startup?0/4
  10. 10Should enterprises invest in modern fine-grained authorization tools or stick with traditional access controls?0/4
  11. 11How can fine-grained authorization help streamline permissions for collaborative platforms?0/4
  12. 12Fine-grained authorization APIs vs. role-based access control: which is better for modern apps?0/4
  13. 13Which platforms offer end-to-end fine-grained authorization for SaaS applications?1/4
  14. 14What are the best fine-grained authorization solutions for developers who need robust access control?0/4
  15. 15How does fine-grained authorization differ from coarse-grained authorization in terms of flexibility and scalability?0/4
Total namedOpenAI: 1/15Claude: 0/15Perplexity: 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 OpenAI actually said

An answer that names the company

OpenAInamed the company

“Which platforms offer end-to-end fine-grained authorization for SaaS applications?”

… Warrant, PlainID, and several others — smaller/commercial players that target SaaS/tenant models and PBAC workflows. (bersyn.com)

How to pick one (brief):

Authorization model needed: ReBAC/Zanzibar (AuthZed, OpenFGA/SpiceDB) vs policy/PBAC (OPA/Cerbos/Aserto/Styra) vs embedded library (Oso).

Latency/scale: Zanzibar-style stores relationships and scales for massive graphs; Cerbos and Oso emphasize sub‑ms local PDP checks. (archive.fosdem.org …

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
    Permit.io
    25
  2. 02
    Oso
    23

Share of mentions

CompanyShare
  1. Permit.io
    32.3%
  2. Auth0 Fine-Grained Authorization (now Okta)
    30.6%
  3. AuthZed SpiceDB
    19.4%
  4. Cedar (AWS Authorization with Cedar)
    16.1%
  5. Warrant
    1.6%
  6. Oso
    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
    osohq.com
    38
  2. 02
    workos.com
    27
  3. 03
    cerbos.dev
    23
  4. 04
    auth0.com
    22
  5. 05
    gbhackers.com
    18
  6. 06
    openfga.dev
    15
  7. 07
    permit.io
    13
  8. 08
    nhimg.org
    12
  9. 09
    cyberpress.org
    12
  10. 10
    okta.com
    12

Warrant 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

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

How Warrant 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
2

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
40

How well built the website's own pages are

AI readiness
14

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
18
Ranks 569 of 574. Typical company: 29
Named in answers
1.7%
Ranks 267 of 574. Typical company: 1.7%
AI readiness
14
Ranks 504 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
18
Ranks 313 of 315. Typical company: 28
Named in answers
1.7%
Ranks 123 of 315. Typical company: 1.7%
AI readiness
14
Ranks 277 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. Warrant 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
0 of 100
  • robots.txtNot met

    Publish a valid robots.txt with crawl rules and a Sitemap line.

    study: 86.1%
  • SitemapNot met

    Serve an XML sitemap and reference it 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
0 of 100
  • AI bot rulesNot 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 11 of 100 · 97% of checks could run · checked on October 9, 2026 · warrant.dev · 2,200 companies scanned

See how every company did, and how this was measured.

AI readinessSection 10

The checks behind the AI readiness score

2 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

  • Not metrobots.txt publishedstudy: 85.1%
  • Not metXML sitemapstudy: 79.4%
  • Not metHTTP 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%
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