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.
963 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 Matter Labs

AI assistants rarely name Matter Labs in their answers: it is named just once out of 60 answers. Its overall score is 29 out of 100, placing it 963rd of 2,219 companies. The website passes 5 of 14 technical checks for readiness with AI tools.

We asked 15 unbranded questions about its market, so no question mentioned Matter Labs by name. AI assistants named it in 1 of the 60 answers. Counting only how often it is named, it ranks 50th 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
29Overall score
  • Visibility2
  • Citations0
  • Sentiment50
  • Site quality87
  • AI readiness36
Summary of account
Named by AI in
1.7%
1 of 60 answers
Share of mentions
3.8%
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
53 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

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

  1. 01Common issues when integrating digital asset management platforms into bank infrastructure0/4
  2. 02Typical costs for private, permissioned blockchain implementations for US banks0/4
  3. 03Are there affordable compliant blockchain solutions for smaller financial institutions?0/4
  4. 04What are the best blockchain platforms for banks focused on privacy and compliance?1/4
  5. 05What other approaches exist for privacy and compliance in digital asset settlement besides blockchain?0/4
  6. 06Is a privacy-focused blockchain solution worth it for a midsize US bank?0/4
  7. 07Are compliant digital asset management platforms a good investment for financial institutions?0/4
  8. 08Best practices for ensuring privacy when settling assets on a regulated blockchain network0/4
  9. 09Why is my transaction not showing up on a permissioned financial blockchain network?0/4
  10. 10Private, permissioned blockchains vs hybrid blockchains for banking — which is better for compliance?0/4
  11. 11How can US banks use blockchain platforms for digital securities issuance?0/4
  12. 12Alternatives to permissioned blockchain platforms for secure digital asset management0/4
  13. 13How does a permissioned blockchain compare to a public blockchain for secure asset settlement?0/4
  14. 14Which digital asset management solutions are designed for regulated financial institutions?0/4
  15. 15Non-blockchain solutions for regulated institutions needing private digital transactions0/4
Total namedOpenAI: 0/15Claude: 0/15Perplexity: 0/15Gemini: 1/15All 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 Gemini actually said

An answer that names the company

Gemininamed the company

“What are the best blockchain platforms for banks focused on privacy and compliance?”

For banks and financial institutions prioritizing privacy and compliance in blockchain applications, several platforms offer specialized solutions, often leveraging zero-knowledge proofs (ZKPs) and modular designs:

Platforms Emphasizing Zero-Knowledge Proofs (ZKPs) for Privacy and Compliance:

Prividium by Matter Labs is a full ZK-based blockchain platform built on ZKsync technology and anchored to Ethereum. It's purpose-built for institutions demanding privacy, compliance, and control over their data. Key features include user-level privacy, built-in compliance with role-based ac …

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
    R3 Corda
    6
  2. 02
    ConsenSys Quorum
    2
  3. 03
    Hyperledger Besu
    2

Share of mentions

CompanyShare
  1. Hyperledger Besu
    53.8%
  2. R3 Corda
    30.8%
  3. ConsenSys Quorum
    11.5%
  4. Matter Labs
    3.8%
  5. Symbiont Assembly
    0.0%
  6. SETL
    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
    blockchain-council.org
    15
  2. 02
    arxiv.org
    10
  3. 03
    chain.link
    6
  4. 04
    fireblocks.com
    6
  5. 05
    cdn.prod.website-files.com
    6
  6. 06
    sec.gov
    6
  7. 07
    image-ppubs.uspto.gov
    6
  8. 08
    f6s.com
    5
  9. 09
    occ.gov
    5
  10. 10
    ripple.com
    4

Matter Labs 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

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

How Matter Labs 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
87

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
29
Ranks 53 of 102. Typical company: 30
Named in answers
1.7%
Ranks 50 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
29
Ranks 39 of 80. Typical company: 29
Named in answers
1.7%
Ranks 37 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. Matter Labs 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
63 of 100
  • AI bot rulesPassed

    Explicit robots.txt rules for AI crawlers such as GPTBot, ClaudeBot and PerplexityBot.

    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 43 of 100 · 97% of checks could run · checked on October 9, 2026 · matterlabs.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

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%
  • 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

  • PassedExplicit 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 rules for 8 AI crawlers by name. All others follow its general rules.

Training AI

  • GPTBot*allowed
  • ClaudeBot*allowed
  • Anthropic-AI*allowed
  • Google-Extended*allowed
  • Applebot-Extended*allowed
  • Meta-ExternalAgentallowed
  • FacebookBotallowed
  • Bytespiderallowed
  • CCBotallowed
  • Diffbotallowed
  • Omgilibotallowed

AI search

  • OAI-SearchBot*allowed
  • Claude-SearchBotallowed
  • PerplexityBot*allowed
  • Applebotallowed
  • Amazonbotallowed
  • YouBotallowed

Answering questions

  • ChatGPT-User*allowed
  • 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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