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.
1,756 of 2,219
its rank by overall score
Closing balance

Named in

0of 60, its tower is dark

by 0 of 4 assistants

Statement note

What this means for Atoura

AI assistants do not name this fintech infrastructure company in any of their 60 answers, though they do name companies such as Q2 and nCino. It has an overall score of 25 out of 100, ranking 1,756th of 2,219. Its website passes 2 of 14 technical checks for AI tool readiness.

We asked 15 unbranded questions about its market, so no question mentioned Atoura by name. AI assistants named it in none of the 60 answers. Counting only how often it is named, it ranks 334th 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
25Overall score
  • Visibility0
  • Citations0
  • Sentiment50
  • Site quality80
  • AI readiness14
Summary of account
Named by AI in
0.0%
0 of 60 answers
Share of mentions
0.0%
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
486 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

Atoura came up on 0 of 15 questions. Open any window to read that answer.

  1. 01Should smaller banks consider integrated intelligence platforms or stick with their existing siloed systems?0/4
  2. 02What are common operational challenges that unified intelligence platforms solve for financial institutions?0/4
  3. 03Alternatives to all-in-one intelligence layers for banks seeking unified analytics0/4
  4. 04What other solutions exist besides integrated intelligence platforms for connecting banking data silos?0/4
  5. 05Which types of financial institutions benefit most from unified intelligence platforms?0/4
  6. 06How can banks overcome data silos when implementing unified intelligence software?0/4
  7. 07Are there affordable options for smaller financial institutions looking to adopt workflow optimization tools?0/4
  8. 08What is the typical cost range for integrated intelligence software geared toward banks?0/4
  9. 09Why do integrated analytics solutions sometimes fail to provide a single source of truth in financial institutions?0/4
  10. 10How does connecting revenue, risk, and operations systems help banks improve compliance?0/4
  11. 11Integrated intelligence platform vs traditional core banking system for operational efficiency0/4
  12. 12Is it worth investing in a unified intelligence layer to connect revenue, risk, and operations data in a bank?0/4
  13. 13What are the most important features to look for in an integrated intelligence platform for banks?0/4
  14. 14How do workflow automation tools for financial institutions compare to all-in-one intelligence platforms?0/4
  15. 15Best unified analytics solutions for optimizing financial institution workflows0/4
Total namedOpenAI: 0/15Claude: 0/15Perplexity: 0/15Gemini: 0/15All assistants: 0/60
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

The company is not named in this answer

Geminidid not name the company

“Alternatives to all-in-one intelligence layers for banks seeking unified analytics”

While "all-in-one" intelligence layers aim to centralize analytics for banks, several alternative approaches offer unified analytics without requiring a complete rip-and-replace of existing systems. These alternatives often leverage semantic layers, data virtualization, or orchestration layers to achieve a unified view of data and enable advanced analytics, including AI capabilities.

Here are some alternatives to all-in-one intelligence layers for banks seeking unified analytics:

Semantic Layers / AI-Ready Data Layers:

Description: This approach creates a virtual, unified …

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.

Named instead

CompanyTimes named
  1. 01
    Q2
    1
  2. 02
    nCino
    1
  3. 03
    Fiserv
    1

Share of mentions

CompanyShare
  1. Fiserv
    44.4%
  2. nCino
    33.3%
  3. Q2
    22.2%
  4. Atoura
    0.0%
  5. MANTL
    0.0%
  6. Tegra118 (now part of InvestCloud)
    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
    backbase.com
    12
  2. 02
    visbanking.com
    11
  3. 03
    moodys.com
    9
  4. 04
    linkedin.com
    7
  5. 05
    databricks.com
    7
  6. 06
    intersystems.com
    6
  7. 07
    sap.com
    4
  8. 08
    jinba.io
    4
  9. 09
    arxiv.org
    4
  10. 10
    larksuite.com
    4

Atoura 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

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

How Atoura 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
0

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
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
25
Ranks 486 of 574. Typical company: 29
Named in answers
0.0%
Ranks 334 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
25
Ranks 256 of 315. Typical company: 28
Named in answers
0.0%
Ranks 168 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.

This website turned most of our scan away, so it has no level. Only 44% of the checks could run. That reflects the website’s rules for automated visitors, and an AI agent arriving by itself meets the same rules.

Can an AI agent check out here?

We could not tell. The website turned away our requests for the files an agent would read. An agent arriving by itself is turned away the same way.

Agent payment protocols
  • x402Not present
  • MPPCould not check
  • AP2Not present
  • ACPCould not check
  • UCPCould not check
Interfaces for AI agents
  • MCP Server CardCould not check
  • A2A Agent CardCould not check
  • Agent SkillsCould not check
  • API CatalogCould not check
  • llms.txtCould not check
  • WebMCPCould not check
Show every check, for your technical team
Discoverability
64 of 100
  • robots.txtPassed

    A valid robots.txt with crawl rules at the site root.

    study: 86.1%
  • SitemapCould not check

    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
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.txtCould not check

    Publish an llms.txt with a short summary and the key links.

    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 AuthCould not check

    Only relevant if you run your own agents or crawlers: publish a signing key directory.

    study: 0.3%
APIs, auth and MCP
  • API CatalogCould not check

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

    study: 1.3%
  • OAuth discoveryCould not check

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

    study: 6.3%
  • OAuth Protected ResourceCould not check

    Serve protected resource metadata at /.well-known/oauth-protected-resource.

    study: 5.3%
  • auth.mdCould not check

    Publish an auth.md describing how agents sign in.

    study: 0.2%
  • MCP Server CardCould not check

    Offer an MCP server and publish its server card under /.well-known.

    study: 0.8%
  • A2A Agent CardCould not check

    Publish an agent card at /.well-known/agent-card.json.

    study: 0.2%
  • Agent SkillsCould not check

    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 manifestCould not check

    Publish an ai-catalog.json listing every agent interface.

    study: 0.2%

Score 35 of 100 · 44% of checks could run · checked on October 9, 2026 · atoura.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

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

  • Passedrobots.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%
  • Not metllms.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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