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

Named in

3of 60 answers

by 3 of 4 assistants

Statement note

What this means for Aware

AI assistants name this fraud and identity company in 5% of answers, more often than the typical company in its sector at 1.7%. Its overall score is 38 out of 100, ranking 422nd of 2,219 overall and 127th of 574 in its sector. Its website passes 6 of 14 technical checks for AI tool readiness.

We asked 15 unbranded questions about its market, so no question mentioned Aware by name. AI assistants named it in 3 of the 60 answers. Counting only how often it is named, it ranks 192nd of 574 in its sector. The typical financial technology and data company is named in 1.7% of answers.

Overall score
38Overall score
  • Visibility12
  • Citations3
  • Sentiment83
  • Site quality80
  • AI readiness43
Summary of account
Named by AI in
5.0%
3 of 60 answers
Share of mentions
37.5%
of the mentions of it and its 5 peers
AI readiness
6/14
technical checks passed. Typical for its sector: 4
Rank in its sector
127 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

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

  1. 01Best practices for implementing biometric authentication in government agencies0/4
  2. 02Where to find configurable biometric platforms that avoid vendor lock-in3/4
  3. 03How can biometric intelligence improve fraud prevention in enterprise environments?0/4
  4. 04Alternatives to facial recognition for secure enterprise authentication0/4
  5. 05Are there scalable biometric authentication solutions suitable for government agencies with limited budgets?0/4
  6. 06What are the most secure biometric identity verification platforms for large enterprises?0/4
  7. 07Typical pricing models for enterprise biometric verification platforms in the US0/4
  8. 08Common reasons for biometric identity verification failures and how to resolve them0/4
  9. 09Is investing in biometric identity verification worth it for large corporations?0/4
  10. 10How to address false positives in biometric authentication systems0/4
  11. 11Face recognition vs fingerprint biometrics for enterprise-level authentication0/4
  12. 12How do multimodal biometric systems compare to single-modal solutions for identity management?0/4
  13. 13Non-biometric solutions for large-scale identity verification0/4
  14. 14Use-cases for biometric identity management in government digital services0/4
  15. 15What should enterprises look for when choosing a biometric authentication platform?0/4
Total namedOpenAI: 1/15Claude: 1/15Perplexity: 0/15Gemini: 1/15All 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

“Where to find configurable biometric platforms that avoid vendor lock-in”

I'll search for information about configurable biometric platforms that avoid vendor lock-in.Based on the search results, here are several configurable biometric platforms that help avoid vendor lock-in:

Commercial Platforms

Aware's Awareness Platform™ - This platform is configurable, flexible, and extendable—eliminating vendor lock-in. It serves as an intelligent control plane for identity, enabling biometric orchestration, identity verification, and decisioning workflows, allowing you to configure biometric workflows, orchestrate multiple ecosystem partners, and evaluate technologie …

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
    IDEMIA
    2
  2. 02
    Veridium
    1

Share of mentions

CompanyShare
  1. IDEMIA
    50.0%
  2. Aware
    37.5%
  3. Veridium
    12.5%
  4. NEC Biometrics
    0.0%
  5. Thales Biometric Solutions
    0.0%
  6. AuthenticID
    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
    sourceforge.net
    10
  2. 02
    image-ppubs.uspto.gov
    7
  3. 03
    aware.com (its own website)
    7
  4. 04
    oloid.com
    6
  5. 05
    nist.gov
    5
  6. 06
    arxiv.org
    5
  7. 07
    worldmetrics.org
    4
  8. 08
    zipdo.co
    4
  9. 09
    identy.io
    3
  10. 10
    idmanagement.gov
    3

Aware is in the study as a fraud and identity company.

By assistantSection 05

Which AI assistants named the company

Answers naming it, out of answers received

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

How Aware 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
12

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

Citations
3

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

Sentiment
83

How positively AI describes the company

Site quality
80

How well built the website's own pages are

AI readiness
43

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
38
Ranks 127 of 574. Typical company: 29
Named in answers
5.0%
Ranks 192 of 574. Typical company: 1.7%
AI readiness
43
Ranks 29 of 574. Typical company: 29
Against its categorySection 08

Compared with fraud and identity companies

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

Overall score
38
Ranks 45 of 171. Typical company: 30
Named in answers
5.0%
Ranks 56 of 171. Typical company: 1.7%
AI readiness
43
Ranks 11 of 171. 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. Aware 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
18 of 100
  • API CatalogNot met

    List public APIs in a linkset at /.well-known/api-catalog.

    study: 1.3%
  • OAuth discoveryPartly met

    Publish OpenID Connect or OAuth server metadata under /.well-known.

    study: 6.3%
  • OAuth Protected ResourcePassed

    Metadata telling agents which authorization server to use.

    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 42 of 100 · 97% of checks could run · checked on October 9, 2026 · www.aware.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

6 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%
  • 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%
  • PassedOAuth Authorization Server discoverystudy: 5.2%
  • PassedOAuth 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 38 of 100 badge for Aware

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/aware.com"><img src="https://aeo-audit.rezolve.com/finance/badges/aware.com.svg" alt="Overall score 38 of 100, Finance AI Visibility Study 2026" width="280" height="64"></a>
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