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.
1,266 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 Hyperlayer

AI assistants rarely name Hyperlayer, mentioning it in only 1 of 60 answers. Its overall score is 27 out of 100, ranking 1,266th of 2,219 in the study. The website passes 4 of 14 technical checks for how ready it is for AI tools.

We asked 15 unbranded questions about its market, so no question mentioned Hyperlayer 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
27Overall score
  • Visibility2
  • Citations0
  • Sentiment50
  • Site quality80
  • AI readiness29
Summary of account
Named by AI in
1.7%
1 of 60 answers
Share of mentions
3.2%
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
360 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

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

  1. 01Best AI-powered banking solutions for rapid product customization in the US0/4
  2. 02What should US banks consider before switching to an AI-driven banking development platform?0/4
  3. 03Is it worth adopting a programmable banking platform for legacy banks in the US?0/4
  4. 04Alternatives to programmable platforms for launching new banking products without core system replacement1/4
  5. 05What are some options for banks to rapidly innovate new financial products besides modular banking platforms?0/4
  6. 06Typical pricing models for programmable banking platforms in the United States0/4
  7. 07Common challenges when integrating programmable banking platforms with existing systems0/4
  8. 08No-code vs low-code banking product platforms: which is better for financial institutions?0/4
  9. 09Troubleshooting customer-defined banking rules that don’t execute as expected0/4
  10. 10Examples of agentic banking for real-time product adjustments in financial services0/4
  11. 11How much should a financial institution expect to pay for a cloud-based banking platform with modular features?0/4
  12. 12What are the top programmable banking platforms for banks looking to quickly launch new financial products?0/4
  13. 13How can modular banking platforms help financial institutions offer personalized banking services?0/4
  14. 14Programmable banking platforms vs traditional core banking systems for product agility0/4
  15. 15Which banking technology solutions offer the fastest deployment for new product launches?0/4
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

“Alternatives to programmable platforms for launching new banking products without core system replacement”

… Hyperlayer: Connects to existing cores via event-driven APIs, reading balances and transactions in real-time. It then applies rules, decisioning, and orchestration as a separate intelligence layer, allowing for product definition, fees, rewards, and limits as modular building blocks. Product changes can be executed in minutes, not months hyperlayer.com.

additiv (addBanking): Offers an API-first platform to design, launch, and scale banking products, from savings to embedded finance, on top of an existing core. It aims for faster deployme …

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
    Mambu
    10
  2. 02
    Thought Machine
    7
  3. 03
    10x Banking
    4

Share of mentions

CompanyShare
  1. Mambu
    38.7%
  2. Thought Machine
    29.0%
  3. 10x Banking
    19.4%
  4. Finastra (FusionFabric.cloud)
    9.7%
  5. Hyperlayer
    3.2%
  6. SAP Fioneer
    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
    dashdevs.com
    8
  2. 02
    sdk.finance
    8
  3. 03
    backbase.com
    8
  4. 04
    deloitte.com
    7
  5. 05
    sourceforge.net
    5
  6. 06
    temenos.com
    4
  7. 07
    quickbooks.intuit.com
    4
  8. 08
    appinventiv.com
    4
  9. 09
    creatio.com
    4
  10. 10
    fisglobal.com
    4

Hyperlayer 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

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

How Hyperlayer 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
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 financial technology and data companies

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

Overall score
27
Ranks 360 of 574. Typical company: 29
Named in answers
1.7%
Ranks 267 of 574. Typical company: 1.7%
AI readiness
29
Ranks 224 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
27
Ranks 184 of 315. Typical company: 28
Named in answers
1.7%
Ranks 123 of 315. Typical company: 1.7%
AI readiness
29
Ranks 117 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 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. Hyperlayer 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
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
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
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 32 of 100 · 97% of checks could run · checked on October 9, 2026 · www.hyperlayer.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%
  • Not metHTTP 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

  • 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 10 AI crawlers by name. All others follow its general rules.

Training AI

  • GPTBot*blocked
  • ClaudeBot*blocked
  • Anthropic-AIallowed
  • Google-Extended*blocked
  • Applebot-Extendedallowed
  • Meta-ExternalAgentallowed
  • FacebookBotallowed
  • Bytespider*blocked
  • CCBot*blocked
  • Diffbotallowed
  • Omgilibotallowed

AI search

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

Answering questions

  • ChatGPT-User*allowed
  • Claude-User*allowed
  • 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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